code/2018007夏/20180729_夏_数据分析.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "jupyter使用"
    ]
   },
   "source": [
    "## Jupyter Notebook常用快捷键\n",
    "- **M**切换markdown\n",
    "- **Y**切换code模式\n",
    "- **H**查询快捷键\n",
    "- **Shift Enter**运行并新生一个单元格\n",
    "- **Ctrl Enter**运行\n",
    "- **B**向下添加一个空单元格"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 以下示例所用的数据可在data文件夹中找到，数据来源于泰迪杯"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Python回顾\n",
    "- 恒等操作符（IS/IS NOT）\n",
    "    - type(2) is int\n",
    "- 向方法传递可变个数的参数 （*Args）\n",
    "- ord(\"A\")=65 将字母变为等价数字\n",
    "\n",
    "#### 代码风格\n",
    "- 方法名小写，驼峰写法OK\n",
    "- 私有方法首部以一个下划线开头"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 列表重点"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3, [4, 5, 6], 4, 5, 6]\n",
      "[1, 'insert', 2, 3, [4, 5, 6], 4, 5, 6]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, [4, 5, 6], 4, 5, 6]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#append,extend都是在末尾添加\n",
    "#append是添加元素，extend是连接列表（+号连接列表，会新建一个新的副本，效率更低）\n",
    "a = [1,2,3]\n",
    "b = [4,5,6]\n",
    "a.append(b)\n",
    "a.extend(b)\n",
    "print(a)\n",
    "#insert和分片赋值\n",
    "#强大的分片赋值\n",
    "a.insert(1,'insert')#等同于该分片赋值，a[1:1]=['insert1']\n",
    "print(a)\n",
    "#分片删值\n",
    "a[1:2]=[]#[1:1]是空分片，插入OK，但删除不能删个空片吧\n",
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 字典重要点\n",
    "- 键值不可变，也不能重复"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'username': 'josonle', 'age': 20, 'test': [1, 2, 3]}\n",
      "{'username': 'josonle', 'age': 20, 'test': [1, 2, 3]} {'username': 'lzw', 'age': 20, 'test': [1, 2, 3]}\n",
      "{'username': 'josonle', 'age': 20, 'test': [1, 2, 3]} {'username': 'lzw', 'test': [1, 2, 3]}\n",
      "{'username': 'josonle', 'age': 20, 'test': [1, 2]} {'username': 'lzw', 'test': [1, 2]}\n"
     ]
    }
   ],
   "source": [
    "#浅复制字典方法copy()\n",
    "#深复制copy模块的deepcopy()\n",
    "d={'username':'josonle','age':20,'test':[1,2,3]}\n",
    "dc = d.copy()\n",
    "print(dc)\n",
    "dc['username'] = 'lzw'\n",
    "print(d,dc)\n",
    "dc.pop('age')\n",
    "print(d,dc)\n",
    "dc['test'].pop()\n",
    "print(d,dc)\n",
    "#可见copy()方法，如果替换、删除某个值不会影响原字典，但是某个值仅仅是替换会影响到"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'username': 'josonle', 'age': 20, 'test': [1, 2, 3]} {'username': 'josonle', 'age': 20, 'test': [1, 2]}\n"
     ]
    }
   ],
   "source": [
    "from copy import deepcopy\n",
    "d={'username':'josonle','age':20,'test':[1,2,3]}\n",
    "dc = deepcopy(d)\n",
    "dc['test'].pop()\n",
    "print(d,dc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "value会自动赋None: {'username': None, 'age': None}\n",
      "可以添加默认值： {'username': 'null', 'age': 'null'}\n"
     ]
    }
   ],
   "source": [
    "#使用fromkeys新建字典\n",
    "d = {}.fromkeys(['username','age'])\n",
    "print('value会自动赋None:',d)\n",
    "d = {}.fromkeys(['username','age'],'null')\n",
    "print('可以添加默认值：',d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'age': 20, 'test': 1, 'username': 'josonle'}"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#使用update(dict)更新字典，直接赋值也OK\n",
    "d['test']=None\n",
    "d.update({'test':1})\n",
    "d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 迭代器\n",
    "- zip()\n",
    "- "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Numpy\n",
    "- ndarray数组对象\n",
    "- ufunc处理数组的函数\n",
    "\n",
    "[【Python数据分析】Numpy的详细教程 ](https://zhuanlan.zhihu.com/p/24988491)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ndarray属性\n",
    "- ndim：数组维度\n",
    "- shape：返回tuple，数组的行列\n",
    "- dtype：数组元素的类型\n",
    "- itemsize：返回数组每个元素的字节大小"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "维度： 2 shape: (2, 4)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[[1, 2, 3, 4]],\n",
       "\n",
       "       [[5, 6, 7, 8]]])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.array([[1,2,3,4],[5,6,7,8]])\n",
    "print('维度：',arr.ndim,'shape:',arr.shape)\n",
    "#改变shape，仅仅是改变每个轴数据多少\n",
    "arr.shape = 2,1,4\n",
    "arr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 生成一维数组的方法\n",
    "- np.arange(a,b,c): 从c开始到b为止（不含b），步长c的一维数组\n",
    "- np.linspqace(a,b,c): 从a到b（含b）的c个数据的一维等差数组\n",
    "    - linspace(a,b,c=50,endpoint=true,retstep=false)，默认输出50个\n",
    "    - endpoint为False，不包含b\n",
    "    - retstep为True，显示步长（公差）\n",
    "- np.logspace(a,b,c): 创建等比数列，从$10^a$到$10^b$（含$10^b$）的c个数据的一维等比数列\n",
    "    - logspace(a,b,c=50,base=10,endpoint=True)，多了一个base参数，默认10\n",
    "    - base参数是下标，比如base=2，则$2^a$到$2^b$间"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0. ,  0.2,  0.4,  0.6,  0.8])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(0,1,0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([ 0. ,  0.2,  0.4,  0.6,  0.8]), 0.20000000000000001)\n",
      "步长： 0.2\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 0.  ,  0.25,  0.5 ,  0.75,  1.  ])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(np.linspace(0,1,5,endpoint=False,retstep=True))\n",
    "print(\"步长：\",np.linspace(0,1,5,endpoint=False,retstep=True)[1])\n",
    "np.linspace(0,1,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.        ,  1.58740105,  2.5198421 ,  4.        ])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# np.logspace(0,1,5)\n",
    "np.logspace(0,2,4,base=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 生成多维数组的方法\n",
    "- np.zeros(shape): 生成几行几列的全0数组\n",
    "- np.diag([a,b,c,...]): 生成以 list 为对角线的矩阵，其余为0\n",
    "    - **挺重要，可取矩阵的对角线上数据**\n",
    "- np.eye(a): 生成以 1 为主对角线的 a 行矩阵\n",
    "    - eye(M,N=None,K=0)，M行N列的矩阵（默认N=M），K可以指明那一列对角线为1\n",
    "- np.identity(a)：生成主对角线为1的a行方阵\n",
    "- np.ones(shape): 生成几行几列的全 1 数组\n",
    "- np.empty(shape)：生成几行几列的空数组\n",
    "    - 注意：空不意味着值为0，而是任何的value,内存中没有被初始化的。\n",
    "- arr.fill(Value)：将数组重新用Value填满，常和empty一起用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0.,  0.],\n",
       "        [ 0.,  0.],\n",
       "        [ 0.,  0.]],\n",
       "\n",
       "       [[ 0.,  0.],\n",
       "        [ 0.,  0.],\n",
       "        [ 0.,  0.]]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros((2,3,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  0.,  0.,  0.],\n",
       "       [ 0.,  1.,  0.,  0.],\n",
       "       [ 0.,  0.,  1.,  0.],\n",
       "       [ 0.,  0.,  0.,  1.]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.eye(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.  0.]\n",
      " [ 0.  1.]\n",
      " [ 0.  0.]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  0.],\n",
       "       [ 1.,  0.],\n",
       "       [ 0.,  1.]])"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(np.eye(3,2))\n",
    "np.eye(3,2,-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  0.,  0.],\n",
       "       [ 0.,  1.,  0.],\n",
       "       [ 0.,  0.,  1.]])"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.identity(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function diag in module numpy.lib.twodim_base:\n",
      "\n",
      "diag(v, k=0)\n",
      "    Extract a diagonal or construct a diagonal array.\n",
      "    \n",
      "    See the more detailed documentation for ``numpy.diagonal`` if you use this\n",
      "    function to extract a diagonal and wish to write to the resulting array;\n",
      "    whether it returns a copy or a view depends on what version of numpy you\n",
      "    are using.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    v : array_like\n",
      "        If `v` is a 2-D array, return a copy of its `k`-th diagonal.\n",
      "        If `v` is a 1-D array, return a 2-D array with `v` on the `k`-th\n",
      "        diagonal.\n",
      "    k : int, optional\n",
      "        Diagonal in question. The default is 0. Use `k>0` for diagonals\n",
      "        above the main diagonal, and `k<0` for diagonals below the main\n",
      "        diagonal.\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    out : ndarray\n",
      "        The extracted diagonal or constructed diagonal array.\n",
      "    \n",
      "    See Also\n",
      "    --------\n",
      "    diagonal : Return specified diagonals.\n",
      "    diagflat : Create a 2-D array with the flattened input as a diagonal.\n",
      "    trace : Sum along diagonals.\n",
      "    triu : Upper triangle of an array.\n",
      "    tril : Lower triangle of an array.\n",
      "    \n",
      "    Examples\n",
      "    --------\n",
      "    >>> x = np.arange(9).reshape((3,3))\n",
      "    >>> x\n",
      "    array([[0, 1, 2],\n",
      "           [3, 4, 5],\n",
      "           [6, 7, 8]])\n",
      "    \n",
      "    >>> np.diag(x)\n",
      "    array([0, 4, 8])\n",
      "    >>> np.diag(x, k=1)\n",
      "    array([1, 5])\n",
      "    >>> np.diag(x, k=-1)\n",
      "    array([3, 7])\n",
      "    \n",
      "    >>> np.diag(np.diag(x))\n",
      "    array([[0, 0, 0],\n",
      "           [0, 4, 0],\n",
      "           [0, 0, 8]])\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(np.diag)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 0, 0],\n",
       "       [0, 2, 0],\n",
       "       [0, 0, 3]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.diag([1,2,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3, 4],\n",
       "       [5, 6, 7, 8],\n",
       "       [9, 8, 7, 6],\n",
       "       [5, 4, 3, 2]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.array([[1,2,3,4],[5,6,7,8],[9,8,7,6],[5,4,3,2]])\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 6 7 2]\n",
      "[2 7 6]\n",
      "[9 4]\n"
     ]
    }
   ],
   "source": [
    "print(np.diag(arr))\n",
    "print(np.diag(arr,1))\n",
    "print(np.diag(arr,-2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 1.,  1.],\n",
       "        [ 1.,  1.],\n",
       "        [ 1.,  1.]],\n",
       "\n",
       "       [[ 1.,  1.],\n",
       "        [ 1.,  1.],\n",
       "        [ 1.,  1.]]])"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones((2,3,2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 利用random模块生成（伪）随机数组\n",
    "- np.random.random((shape))\n",
    "- np.random.rand(shape)：均匀分布\n",
    "- np.random.randn(shape)：正态分布\n",
    "- np.random.randint(low,high=None,size=(shape))：指定上下限(不含上限)的int型随机数组\n",
    "    - 如果不提供high，则low就是上限"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.71989841,  0.12494185,  0.32974345],\n",
       "       [ 0.1948063 ,  0.07779789,  0.4709368 ]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.random((2,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.11396671,  0.45209123,  0.2277055 ],\n",
       "       [ 0.49934775,  0.84436453,  0.74712222],\n",
       "       [ 0.11287526,  0.60161053,  0.76510281]])"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.rand(3,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.10192405, -0.4812036 ,  0.89808382, -0.20317444,  0.14049996,\n",
       "        -0.46593152, -1.65632719, -0.65347466, -0.6796135 ,  0.27672644,\n",
       "         2.48461813,  0.73344194, -1.33366118, -0.56821763, -0.98658191,\n",
       "        -0.6101235 , -1.15057662,  0.27296846,  0.11949839,  1.19447121],\n",
       "       [ 0.24012023, -1.52267169, -1.29870975,  0.2313015 ,  0.86681907,\n",
       "        -0.43491738, -0.4723034 , -1.76864537, -0.46126694, -1.27851324,\n",
       "         0.61510308,  1.11900689, -1.31276449,  0.6208489 ,  0.76283272,\n",
       "        -1.50240191, -0.34983225,  0.15129925, -0.84636383, -0.7932725 ],\n",
       "       [ 0.51466851, -0.59004614, -0.2330279 , -0.03454349, -0.94383022,\n",
       "        -0.60802783, -1.84969004,  1.28882596, -0.52435041, -0.10032326,\n",
       "        -0.95095365,  1.01648065,  0.44810957,  0.96712467,  0.66527845,\n",
       "         0.69280862,  0.47812504,  1.18818404, -0.95723302,  0.2980651 ]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randn(3,20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3, 1, 0, 7, 4, 6, 5, 0, 9, 7],\n",
       "       [7, 8, 9, 6, 3, 1, 7, 7, 3, 2],\n",
       "       [6, 3, 8, 9, 8, 9, 5, 9, 2, 8],\n",
       "       [5, 0, 6, 2, 2, 5, 9, 5, 8, 1]])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randint(0,10,size=(4,10))\n",
    "# np.random.randint(10,size=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on built-in function randint:\n",
      "\n",
      "randint(...) method of mtrand.RandomState instance\n",
      "    randint(low, high=None, size=None, dtype='l')\n",
      "    \n",
      "    Return random integers from `low` (inclusive) to `high` (exclusive).\n",
      "    \n",
      "    Return random integers from the \"discrete uniform\" distribution of\n",
      "    the specified dtype in the \"half-open\" interval [`low`, `high`). If\n",
      "    `high` is None (the default), then results are from [0, `low`).\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    low : int\n",
      "        Lowest (signed) integer to be drawn from the distribution (unless\n",
      "        ``high=None``, in which case this parameter is one above the\n",
      "        *highest* such integer).\n",
      "    high : int, optional\n",
      "        If provided, one above the largest (signed) integer to be drawn\n",
      "        from the distribution (see above for behavior if ``high=None``).\n",
      "    size : int or tuple of ints, optional\n",
      "        Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n",
      "        ``m * n * k`` samples are drawn.  Default is None, in which case a\n",
      "        single value is returned.\n",
      "    dtype : dtype, optional\n",
      "        Desired dtype of the result. All dtypes are determined by their\n",
      "        name, i.e., 'int64', 'int', etc, so byteorder is not available\n",
      "        and a specific precision may have different C types depending\n",
      "        on the platform. The default value is 'np.int'.\n",
      "    \n",
      "        .. versionadded:: 1.11.0\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    out : int or ndarray of ints\n",
      "        `size`-shaped array of random integers from the appropriate\n",
      "        distribution, or a single such random int if `size` not provided.\n",
      "    \n",
      "    See Also\n",
      "    --------\n",
      "    random.random_integers : similar to `randint`, only for the closed\n",
      "        interval [`low`, `high`], and 1 is the lowest value if `high` is\n",
      "        omitted. In particular, this other one is the one to use to generate\n",
      "        uniformly distributed discrete non-integers.\n",
      "    \n",
      "    Examples\n",
      "    --------\n",
      "    >>> np.random.randint(2, size=10)\n",
      "    array([1, 0, 0, 0, 1, 1, 0, 0, 1, 0])\n",
      "    >>> np.random.randint(1, size=10)\n",
      "    array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n",
      "    \n",
      "    Generate a 2 x 4 array of ints between 0 and 4, inclusive:\n",
      "    \n",
      "    >>> np.random.randint(5, size=(2, 4))\n",
      "    array([[4, 0, 2, 1],\n",
      "           [3, 2, 2, 0]])\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(np.random.randint)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 索引使用\n",
    "- 每一维度上都可以和 list索引用法一样，并集\n",
    "- 每一维度可单独列出某些列(行)\n",
    "- 可使用布尔索引\n",
    "> 值得注意的是可以使用range(),如arr[range(0,100,5)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[6, 3, 1],\n",
       "        [7, 2, 2],\n",
       "        [9, 6, 0]],\n",
       "\n",
       "       [[2, 5, 1],\n",
       "        [4, 0, 8],\n",
       "        [9, 2, 0]],\n",
       "\n",
       "       [[3, 1, 6],\n",
       "        [8, 2, 1],\n",
       "        [4, 4, 4]]])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr1 = np.random.randint(0,10,size=(3,3,3))\n",
    "arr1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[2, 5, 1],\n",
       "        [4, 0, 8]],\n",
       "\n",
       "       [[3, 1, 6],\n",
       "        [8, 2, 1]]])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr1[1:,0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[2, 5, 1],\n",
       "        [9, 2, 0]],\n",
       "\n",
       "       [[3, 1, 6],\n",
       "        [4, 4, 4]]])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr1[1:,(0,2)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[6 3 1]\n",
      "  [7 2 2]\n",
      "  [9 6 0]]\n",
      "\n",
      " [[2 5 1]\n",
      "  [4 0 8]\n",
      "  [9 2 0]]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[[2, 2],\n",
       "        [6, 0]],\n",
       "\n",
       "       [[0, 8],\n",
       "        [2, 0]]])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_b = np.array([True,True,False])\n",
    "print(arr1[arr_b])\n",
    "arr1[arr_b,1:,1:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 四则运算\n",
    "- +、-、*、/ ：都是数据对应四则运算\n",
    "- *是按照元素对应相乘，和矩阵乘法不同\n",
    "- 矩阵乘法需使用dot()，**矩阵相乘要求前者列数等于后者行数**\n",
    "    - arr1.dot(arr2)\n",
    "    - np.dot(arr1,arr2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr1 = np.array([[1,2,3],[0,1,2]])\n",
    "arr2 = np.ones((2,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [0, 1, 2]])"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  1.,  1.],\n",
       "       [ 1.,  1.,  1.]])"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2.,  3.,  4.],\n",
       "       [ 1.,  2.,  3.]])"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr1 + arr2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  1.,  2.],\n",
       "       [-1.,  0.,  1.]])"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr1 - arr2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  2.,  3.],\n",
       "       [ 0.,  1.,  2.]])"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr1 * arr2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr3 = np.ones((3,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 6.  6.]\n",
      " [ 3.  3.]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 6.,  6.],\n",
       "       [ 3.,  3.]])"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(arr1.dot(arr3))\n",
    "np.dot(arr1,arr3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[-1  0  1  2]]\n",
      "\n",
      " [[ 3  4  5  6]]]\n",
      "[[[0 1 2 3]]\n",
      "\n",
      " [[4 5 6 7]]]\n",
      "[[[-2 -1  0  1]]\n",
      "\n",
      " [[ 2  3  4  5]]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[[-4, -2,  0,  2]],\n",
       "\n",
       "       [[ 4,  6,  8, 10]]])"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(arr)\n",
    "print(arr+1)\n",
    "#像 -=，*=都会对arr进行修改\n",
    "arr -=1\n",
    "print(arr)\n",
    "arr *2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 统计运算\n",
    "- np.sum()，可以指定axis对哪一轴进行统计。**axis=0，纵轴；axis=1，横轴。以下方法通用**\n",
    "- np.mean，平均数\n",
    "- np.std()，标准差\n",
    "- np.var()，方差\n",
    "- np.max/min()，最大/小值\n",
    "- np.argmax/argmin()，返回最大/小值索引\n",
    "- np.cumsum/cumprod()，累加、累乘"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4],\n",
       "       [ 5,  6,  7,  8,  9],\n",
       "       [10, 11, 12, 13, 14],\n",
       "       [15, 16, 17, 18, 19]])"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr4 = np.arange(20).reshape(4,5)\n",
    "arr4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "求和： 190\n",
      "对横轴求和： [10 35 60 85]\n",
      "对纵轴求和： [30 34 38 42 46]\n"
     ]
    }
   ],
   "source": [
    "print('求和：',np.sum(arr4))\n",
    "print('对横轴求和：',np.sum(arr4,axis=1))\n",
    "print('对纵轴求和：',np.sum(arr4,axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max索引: 19 Min索引: 0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([4, 4, 4, 4], dtype=int64)"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Max索引:',np.argmax(arr4),'Min索引:',np.argmin(arr4))\n",
    "np.argmax(arr4,axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  3,  6, 10, 15, 21, 28, 36, 45], dtype=int32)"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#累加\n",
    "np.cumsum(arr4[:2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([    5,    30,   210,  1680, 15120], dtype=int32)"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#累乘\n",
    "np.cumprod(arr4[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 多元组 np.ix_()方法\n",
    "> 比如你想计算 a+b*c，首先要考虑是否可以广播。但可以将a，b，c组成三元组来计算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.array([2,3,4,5])\n",
    "b = np.array([8,5,4])\n",
    "c = np.array([5,4,6,8,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ax shape: (4, 1, 1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[[2]],\n",
       "\n",
       "       [[3]],\n",
       "\n",
       "       [[4]],\n",
       "\n",
       "       [[5]]])"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax,bx,cx = np.ix_(a,b,c)\n",
    "print('ax shape:',ax.shape)\n",
    "ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[42, 34, 50, 66, 26],\n",
       "        [27, 22, 32, 42, 17],\n",
       "        [22, 18, 26, 34, 14]],\n",
       "\n",
       "       [[43, 35, 51, 67, 27],\n",
       "        [28, 23, 33, 43, 18],\n",
       "        [23, 19, 27, 35, 15]],\n",
       "\n",
       "       [[44, 36, 52, 68, 28],\n",
       "        [29, 24, 34, 44, 19],\n",
       "        [24, 20, 28, 36, 16]],\n",
       "\n",
       "       [[45, 37, 53, 69, 29],\n",
       "        [30, 25, 35, 45, 20],\n",
       "        [25, 21, 29, 37, 17]]])"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = ax+bx*cx\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a[1]+b[2]*c[4]结果为： 15\n"
     ]
    }
   ],
   "source": [
    "print('a[1]+b[2]*c[4]结果为：',result[1,2,4])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 读写文件\n",
    "- **.npy**存储单个arr 和 **.npz**存储多个arr\n",
    "- 二进制格式读写\n",
    "    - np.save(file,arr)，保存单个数组\n",
    "    - np.savez(file,arr1,arr2,...)，保存多个数组\n",
    "    - np.load(file)，**文件名必须要有后缀名，区分npy，npz**\n",
    "        - npy，返回arr\n",
    "        - npz，返回arr的类似字典，键值从load_arr.files取\n",
    "- 文本格式读写"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4],\n",
       "       [ 5,  6,  7,  8,  9],\n",
       "       [10, 11, 12, 13, 14],\n",
       "       [15, 16, 17, 18, 19]])"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr1 = np.arange(20).reshape(4,5)\n",
    "arr2 = np.arange(100).reshape(10,10)\n",
    "file = '../save_file_arr1'\n",
    "np.save(file,arr1)\n",
    "load_arr = np.load(file+'.npy')\n",
    "load_arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "keys: ['arr_0', 'arr_1']\n",
      "arr1：\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4],\n",
       "       [ 5,  6,  7,  8,  9],\n",
       "       [10, 11, 12, 13, 14],\n",
       "       [15, 16, 17, 18, 19]])"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file1 = '../savez_file_arr'\n",
    "np.savez(file1,arr1,arr2)\n",
    "load_arrs = np.load(file1+'.npz')\n",
    "# type(load_arrs)\n",
    "print('keys:',load_arrs.files)\n",
    "print('arr1：')\n",
    "load_arrs['arr_0']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Matplotlib\n",
    "#### pyplot基础语法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 绘图流程\n",
    "1. 创建画布，是否创建子图\n",
    "    - 子图：一张画布画多个图，可指定子图行列、编号\n",
    "2. 对画布添加描述\n",
    "    - plt.title ,标题\n",
    "    - plt.xlabel/ylabel ,添加x/y轴名称\n",
    "    - plt.xlim/ylim ,指定x/y轴数值范围\n",
    "    - plt.xticks/yticks ,指定x/y轴刻度\n",
    "3. 绘制图像、添加图例\n",
    "    - plt.plot() , 绘图\n",
    "    - plt.legend ,指定当前图形的图例（比如说图像右下方指明用什么线画的、y=$x^2$这类的）\n",
    "4. 保存并显示图像\n",
    "    - plt.show()\n",
    "    - plt.savefig()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 基本画例"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12f68626c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = np.arange(0,1.1,0.01)\n",
    "plt.title('test')\n",
    "plt.xlabel('X')\n",
    "plt.ylabel('Y')\n",
    "#定下xy轴范围\n",
    "plt.xlim((0,1))\n",
    "plt.ylim((0,1))\n",
    "#确定xy轴刻度\n",
    "plt.xticks([0,0.2,0.4,0.6,0.8,1])\n",
    "plt.yticks([0,0.2,0.4,0.6,0.8,1])\n",
    "#画图\n",
    "plt.plot(data,data**2)\n",
    "plt.plot(data,data**4)\n",
    "#添加图例\n",
    "plt.legend(['y=x^2','y=x^4'],loc=2)\n",
    "#legend可以通过loc参数指定图例的位置（1是右上角，2是左上角，3是左下角等等）\n",
    "plt.savefig('../test.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 绘制子图示例\n",
    "[子图add_subplot参考](https://www.sogou.com/link?url=DSOYnZeCC_pKZzihDKzFgzQoUkRGi7SFpmCC6ra1tyflRQBB3amNXw..)\n",
    "\n",
    "比如说figure中要画两张图，就是add_subplot(2,1,1)/(2,1,2)，横着并列；(1,2,1)/(1,2,2)，竖着并列。第三个数代表第几张子图。**注：如果数值小于10可以简写成211,212**\n",
    "\n",
    "[如何让子图纵坐标一致](https://zhuanlan.zhihu.com/p/31856211)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12f632b9390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rad = np.arange(0,np.pi*2,0.01)#np.pi是π\n",
    "#定画布大小\n",
    "p1 = plt.figure(figsize=(8,6),dpi=80)#8行6列\n",
    "#子图一\n",
    "ax1 = p1.add_subplot(2,1,1)\n",
    "plt.title('lines')\n",
    "plt.xlabel('X')\n",
    "plt.ylabel('Y')\n",
    "#定下xy轴范围\n",
    "plt.xlim((0,1))\n",
    "plt.ylim((0,1))\n",
    "#确定xy轴刻度\n",
    "plt.xticks([0,0.2,0.4,0.6,0.8,1])\n",
    "plt.yticks([0,0.2,0.4,0.6,0.8,1])\n",
    "#画图\n",
    "plt.plot(rad,rad**2)\n",
    "plt.plot(rad,rad**4)\n",
    "#添加图例\n",
    "plt.legend(['y=x^2','y=x^4'])\n",
    "\n",
    "#子图二\n",
    "ax2 = p1.add_subplot(2,1,2)\n",
    "# plt.title('sin/cos')\n",
    "#配置rc参数显示中文\n",
    "plt.rcParams['font.sans-serif'] = 'SimHei'\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "plt.title('正余弦')\n",
    "plt.xlabel('Rad')\n",
    "plt.ylabel('Sin/Cos')\n",
    "plt.xlim((0,np.pi*2))\n",
    "plt.ylim((-1,1))\n",
    "plt.xticks([0,np.pi/4,np.pi/2,np.pi*3/4,np.pi,np.pi*5/4,np.pi*3/2,np.pi*7/4,np.pi*2])\n",
    "plt.yticks([-1,-0.5,0,0.5,1])\n",
    "plt.plot(rad,np.sin(rad))\n",
    "plt.plot(rad,np.cos(rad))\n",
    "plt.legend(['y=sinx','y=cosx'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 散点图\n",
    "- 判断相关关系（线性、非线性）\n",
    "- 判断离群点\n",
    "> 语法.plt.scatter(x,y,s=20,c='b',marker='o')\n",
    "主要参数是x，y两个要探究相关性的arr。\n",
    "> - s接收数值和x等长的arr，表示点的大小\n",
    "> - c表示点颜色，'b':blue,'y':yellow,'r':red,'g':green等\n",
    "> - marker表示绘制点的类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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nTKBj5PCN0jpGDmf2jAmJ8qn0PehdtRpnQ9+DBUt6h3xtKykYEBGR0ps5qYu5sybS1dmBAV2dHcydNTHxFX1R+x6omUBERIQQEDRanV/UvgeqGRAREUnJQH0M6ul70EwKBkRERFKSVt+DZlMzgYiISEoqzQxFG02gYEBERCRFafQ9aDY1E4iIiJScggEREZGSUzAgIiJScgoGRERESk7BgIiISMkpGBARESk5BQMiIiIlp2BARESk5DTpkIiIFMKCJb25mtkvb+VphIIBERHJvQVLejntyqXrlwfuXbWa065cCtCSE3Ca5clDUKFmAhERyb3zFi5bf+KtWL1mHectXFbo8lSCit5Vq3E2BBULlvSmWNqhKRgQEZHcW75qdaL0rKVVnrwEOQoGREQk98Z1diRKz1pa5clLkKNgQEREcm/2jAl0jBy+UVrHyOHMnjGh0OXJS5CjYEBERHJv5qQu5s6aSFdnBwZ0dXYwd9bElvXeT6s8eQlyzN2b+oat0t3d7T09Pa0uhoiItFgeeu83ozxmttjdu2vZVkMLRUSkNPI2RLHyvq2en0DNBCIiUhp56b2fNwoGRESkNPLSez9vMg0GzGysmd0U759rZjfE271mdpqZdZnZI1XpY+K2F5vZrWZ2ZlVeNaWJiIgMJC+99/Mms2DAzLYGvguMBnD3s919urtPB+4BLgH2Bz5TSXf3FWY2Cxju7lOAncxs11rTsvosIiLSHvLSez9vsqwZWAccBzxdnWhm+wKPuHsvMBn4DzP7vZl9Nm4yHbgi3r8WmJYgbSNmdrKZ9ZhZz4oVK9L5VCIiUlh5G6KYF5mNJnD3pwHMrO9T/wWcHe9fA3wKeBa4zsz2INQkVCZlXgnsnSCtbxnmA/MhDC1s9DOJiEjx5aH3ft40dWihmXUCL3P3+2LSLe7+fHxuCbAr8AxQabzZnFB7UWuaiIiIJNTsE+ibgF9WPV5oZjuY2SjgcEJfgsVsqPLfE3gwQZqIiIgk1OxJh2YAn6t6fC5wPfAC8DV3X2ZmjwI3mdk44EhCvwKvMU1EREQSyuV0xHEkwmHAje7+WJK0gWg6YhERKZPCT0fs7k+yYaRAojQRERFJRp3uRERESi6XNQMiIlJMeVoRME9lyTsFAyIikoo8rQiYp7IUgZoJREQkFXlaETBPZSkCBQMiIpKKPK0ImKeyFIGCARERSUWeVgTMU1mKQMGAiIikIq0VARcs6WXqvEXsOOdqps5bxIIlvUO/KKOylIU6EIqISCoqHfMa6cGfVse/NMpSJrmcgTALmoFQRCT/ps5bRG8/7fpdnR3cPOeQFpSouJLMQKhmAhERyQ11/GsNBQMiIpIb6vjXGgoGREQkN9TxrzXUgVBERHJDHf9aQ8GAiIjkysxJXbk6+ZdhjQMFAyIiIgMoyxoH6jMgIiIygLKscaBgQEREZABlGeqoYEBERGQAZRnqqGBARERkAGUZ6qgOhCIiIgMoy1BHBQMiIiKDyNtQxywoGBARkZYow/j9olAwICIiTVeW8ftFoQ6EIiLSdGUZv18UCgZERKTpyjJ+vygUDIiISNOVZfx+USgYEBGRpivL+P2iUAdCERFJrNGRAGUZv18UCgZEREokjeF8aY0EKMP4/aJQM4GISElUTuK9q1bjbDiJL1jSmygfjQRoPwoGRERKIq2TuEYCtB81E4iIlERaJ/FxnR309vOaVo0E0EyGjVPNgIhISaQ1nC9PIwHSavoou0yDATMba2Y3xftdZvaImd0Qb2Ni+sVmdquZnVn1urrTRESkf2mdxGdO6mLurIl0dXZgQFdnB3NnTWzJ1bj6L6Qjs2YCM9sa+C4wOibtD3zG3S+q2mYWMNzdp5jZt8xsV2BivWnu/uesPo+ISNGlOZwvLyMB1H8hHVn2GVgHHAf8LD6eDLzezN4D/MrdTwemA1fE568FpgGTGkjbKBgws5OBkwHGjx+f3icTESmovJzE05K3/gtFlVkzgbs/7e5PVSVdQzj57wtMMbM9CLUGlYadlcDYBtP6lmG+u3e7e/eYMWNS+mQiIpIXeeq/UGTNHE1wi7s/D2BmS4BdgWeASvi2OSE4aSRNRERKRDMZpqOZwcBCM/s34CngcODrhKv7acBtwJ7AMuCRBtJERKRk2q3poxWaGQycC1wPvAB8zd2XmdmjwE1mNg44ktCvwBtIExERkYTM3VtbgDDq4DDgRnd/rNG0gXR3d3tPT092H0RERCRHzGyxu3fXsm3LZyB09yfZMCqg4TQRERFJRp3uRERESq7lNQMiIpJfmve/HBQMiIhIvyrz/lem+63M+w8oIGgzaiYQEZF+ad7/8lAwICIi/dK8/+WhYEBERPqV1pLHkn8KBkREpF9pzfu/YEkvU+ctYsc5VzN13iIWLOkd+kXSVOpAKCIi/Upj3n91QiwGBQMiIjKgRuf9H6wTooKB/FAzgYiIZEadEItBNQMiIjmS1iQ/eZksaFxnB739nPjVCTFfVDMgIpITlfb13lWrcTa0ryftcJdWPmlIqxOiZEvBgIhITqQ1yU+eJguaOamLubMm0tXZgQFdnR3MnTVR/QVyRs0EIiI5kVb7et7a6RvthCjZU82AiEhOpDXJjyYLkqQUDIiI5ERa7etqp5ek1EwgIpITaUzyk2Y+Uh7m7q0uQ1N0d3d7T09Pq4shIiLSFGa22N27a9lWzQQiIiIlp2BARESk5BQMiIiIlJw6EIqIFFheph2WYlMwICJSUFoeWNKiZgIRkYLK07TDUmwKBkRECipv0w5LcamZQESkoNJcHlh9D8pNNQMiIgWV1rTDeVryWFpjyJoBMxsNHANMAjYD/gr8wt3vybhsIiIyiLSmHR6s74FqB8ph0GDAzP4NOAr4HvApYDWwI3CsmZ0KfMzdn8m8lCIi0q80lgdW3wMZMBgwsx2BMe5+Yp+n7gU+aWa7Av8OzM+wfCIibSlPbfRp9j2QYhqwz4C7P+DuXxrktfsBV6RfJBGR9pa3NnoteSyDdiA0s02r7v+o6v4I4LXAt7IrmohIe8rb/AAzJ3Uxd9ZEujo7MKCrs4O5syaqv0CJDNWBcCEwPd5ff1S4+1rgdDO7JaNyiYi0rTy20afR90CKa6hgYE3V/e3M7O3Vj4Fn0y+SiEh7Uxu95M1Q8wx4n203Azri7R/A+wZ7sZmNNbOb4v3xZnaDmS0ys/kWdJnZIzH9BjMbE7e92MxuNbMzq/KqKU1EJO/URi95M1TNgFXdfwb4NfC4u/9zqIzNbGvgu8DomHQK8D53/6OZXQNMBHYBPuPuF1W9bhYw3N2nmNm34qiFibWkufufa/vYIiKtk9b8ACJpGSoYqK4Z2B44H3i5mT0JnObuiwd57TrgOOBnAO5+RtVz2wJ/B94GvN7M3gP8yt1PJ/RRqIxSuBaYRpjwqJa0jYIBMzsZOBlg/PjxQ3xUEZHmURu95MlQzQSbVt2/392Pcfd9gY8C3zKzNw70Qnd/2t2f6ptuZscBf3D35cA1hJP/vsAUM9uDUJNQGV+zEhibIK1vGea7e7e7d48ZM2aIjyoiIlJOQ9UMHFd1f5PKHXdfamZHAjeY2S/dfd1LX/pSZrYT8DHg0Jh0i7s/H59bAuxKaI6o9KLZnBCw1JomIiIiCQ16AnX3x6rud/d5bjnwugSBwNbAZcBJVTUGC81sBzMbBRwO3AMsJlT5A+wJPJggTURERBKqawnjePLeF7g5wcvmAOOBC80M4GzgXOB64AXga+6+zMweBW4ys3HAkcBkQt+FWtJEREQkIXP3wTcwe7W7/6lP2r8QTuSbuPuA/QbqLlSoRTgMuLFSO1Fr2kC6u7u9p6cn7aKKiIjkkpkt7lurP+C2NQQDa4CPuPuF/Tx3i7sfUF8xm0vBgIikIU8LDIkMJkkwUEszwZ+AV5jZT4H3x74CFZqBUERKo7LAUGVdgcoCQ8CAAYGCBymCWnrgP+/uHwfmAT80s9PMrDKRkA3yOhGRtpJ0gaG8rU4oMpChVi0cRqw9cPfbgdcBjwOLYk3BE5mXUEQkJ5IuMJTm6oQLlvQydd4idpxzNVPnLVJAIakaqpngP4H1Mwd66GBwMXCxmb3M3R/PsnAiInmSdIGhtFYnrKd5QiSJAWsGLIz/e5GwVPHdZtZjZrdUbsBVZnZn00oqItJiSRcYGihISLo6YZo1DCL9GTAY8OCr7j6FUDswEvgeMNXdD3D3ye6+V7MKKiLSajMndTF31kS6OjswoKuzg7mzJg54dZ7W6oRp1TCIDKSmSYfc/edm9hvgeEIAUdOsgyIi7SbJAkNprU6YtHlCJKkBgwEzeyUwyd0XALj7s8C3qp7fDjjU3S/PvJQiIgWVxuqEs2dM2KjPANRXwyAykMGaCR4CXm1mXzKz9UecmY0ys7cDXwZubEIZRURKLWnzhEhStcxA+ErgRGBnwnoAq4FfuvvV2RcvPZqBUEREyiTVGQhjDcGnGy6ViIiI5FItMxCKiIhIGxtyBsKqqYf7e+7YbIolIiIizTJUM8GrgLeY2R3A1n2eM0JfgisyKJeIiIg0yVDBwFrCnAKfAG4CxhLWJ/g98GdCh0IREREpsMHmGRhB6Di4BbADcDWwKzAB+B1wM7BPE8ooIiIiGRqqA+FNwAt9tvM+f0VERKTABqwZcPe1ZnYtsBUwBrgQ6CDUEuwA/DthOWMREREpsKH6DIwH7nT3z/V9wsyGEZoOREREpMAG6zOwKXA68JyZHdLPJsOA3qwKJiIiIs0xWDPB88CRZrYT8FlgD+BDwBNxEwM2zbyEIiIikqlapiO+HzjezN4CPOzu92ZfLBGR1lqwpLfhpYdFimLIYKDC3X9c/djM9nL3O9MvkohIay1Y0rvRksG9q1Zz2pVLARQQSFuqaW2C2FmwMgWxmdk3gGMyLZmISIuct3DZ+kCgYvWadZy3cFmLSiSSrUFrBsxspLuvAf5uZncC2wMzCJMOrTOzE9z9+00op4hI0yxftTpROqhZQYptqGaC75jZSsLwwkPM7Hp3/yvwDTN7JbAQUDAgIm1lXGcHvf2c+Md1dvS7vZoVpOgGbSZw9xOAy6uTzGxLM/sWsBJ4NMvCiYi0wuwZE+gYOXyjtI6Rw5k9Y0K/26tZQYqultEEN5tZ5aEBxwKXuvs/zExTEotI26lczdda7V9Ps4JIngzVZ2Ay8PGqJAeuBD5iZgcBO2dYNhGRlpk5qavmKv6kzQoieTPUaILtgPcAr4lNA9sBzwDXA1cBB2ZbPBGR/EvarCCSN4PWDLj7LwDMrJsNqxeOAmYDzwHzgIczLqOISM1a0as/abOCSN7UNOmQu/ddg+AIM9uB0IdARCQXWtmrP0mzgkjeDDnpUFywiDjZ0P6VdHd/1N2XZ1k4EZEk1KtfpD6DBgNmNhz4FYC7O/CSpYyHeP1YM7sp3h9pZj83s5vN7KQs0kSk3OqdLGjqvEXsOOdqps5bxIIlWoxVymeoeQbWEeYW+HasFah5KKGZbQ18Fxgdkz4ALHb3qcBbzGyLDNJEpMQG6r0/1GRBvatW42xoVlBAIGVTy9oEBvwQODVh3uuA44Cn4+PpwBXx/o1AdwZpGxfc7GQz6zGznhUrViQsvogUjSYLEqnPgMFArOK/FFjj7r9y9xOTZOzuT7v7U1VJo4FKuL0SGJtBWt8yzHf3bnfvHjNmTJLii0gBzZzUxdxZE+nq7MCArs4O5s6aqMmCRIYw2GiCp4FLgY+Z2XbA1Abf6xmgA3gK2Dw+TjtNREpOkwWJJDdgzYC7r3b3awkBw88IKxY2YjEwLd7fE3gwgzQRkZppsiCRoJZ5Bjx20sPMPmJmxwB/JaxkuDbBe30X+KWZHQjsDtxOqOZPM01EpGaaLEgksDBicIAnwwpFM9z9V/HxHGAzQi3BAcDl7v7Zmt/MbBzhan5hpT9B2mkD6e7u9p6enlqLKiIiUmhmttjdX9K5vt9tBwsGhniTDuC/gK+6+9NDbd9qCgZERKRMkgQDtQwtxMxO7if5BeDIIgQCIiIiMrCaggHgRAgzEprZL2H9hEQvZFUwERERaY5ag4G1sD4AqB5zU18bg4iIiOTGoKMJ4pz/a4GxZvZ2wmyE1fdFRESk4IYaWrgNsAYYCXQSAoDq+yIiIlJwgwZ/+JyxAAAgAElEQVQD7v45ADOb6e5fivffVHX/6OyLKCIiIlkaqplgJrAfYbpfzGwYYZ4BERERaRNDNRPcTDj5/4uZXQ2cTZwCOAYGm2RbPBGR/i1Y0quZA0VSMlQzwQrgcuByM9sb2NzdX4zPvWhmH21CGUVENrJgSS+nXbl0/fLDvatWc9qVSwEUEIjUoZa1CQBw99/3k7Y43eKISLtL44r+vIXL1gcCFavXrOO8hcsUDIjUoeZgQESkUWld0S/vZ9nhwdJFZHC1TjokItKwwa7okxjX2ZEoHUIgMnXeInacczVT5y1iwZLeRO8p0s4UDIhI06R1RT97xgQ6Rg7fKK1j5HBmz5jQ7/aVGoneVatxNtRIKCAQCRQMiEjT1HNF35+Zk7qYO2siXZ0dGNDV2cHcWRMHbGpIq0ZCpF2pz4CINM3sGRM26jMAg1/RD2bmpK6a+xmoj4HI4FQzICJNk/SKPi1p1UiItCvVDIhIUyW5ok9LmjUSIu1IwYCI5Foa8xJUtteMhSL9UzAgIrmV5kyDraiRECkKBQMikgnNNChSHAoGRCR1mmlQpFg0mkBEUtfKmQZFJDkFAyKSulbNNCgi9VEwICKpa9VMgyJSH/UZEJHUtWqmQRGpj4IBEUmdxvWLFIuCARHZSNIhgQNtryt6keJQMCAi6yUdEpjmpEAi0jrqQCgi6yUdEqilgUXag4IBEVkv6ZBATQok0h4UDIjIekmHBGpSIJH2oGBARNZLOsmPJgUSaQ/qQCgi6yUdEqghhCLtwdy9OW9k9j7guPiwE1gMHAbcH9M+4O5Lzexc4Cjgd+7+/vjamtIG093d7T09PWl+JBERkdwys8Xu3l3Ltk1rJnD3i9x9urtPB24Cvg5cVkmLgcA+wDRgP+BxMzu01rRmfQ4RycaCJb1MnbeIHedczdR5i1iwpLfVRRIpjab3GTCzLmAs0A28wcx+Z2YXm9kI4CDgJx6qKxYCByZI6++9TjazHjPrWbFiReafTUTqU5mvoHfVapwN8xUoIBBpjlZ0IHw/cBFwB3Cou+8HjCRU+Y8GKt/+lYSgoda0l3D3+e7e7e7dY8aMyeCjiEgaNF+BSGs1NRgws2HAwcANwN3u/mh8qgfYFXgGqIxJ2jyWr9Y0ESkozVcg0lrNPokeCNweq/cvNbM9zWw4MBO4i9CpcFrcdk/gwQRpIlJQmq9ApLWaHQzMAG6M9z8JXArcCdzq7tcBvwUmmdkFwBzgsgRpIlJQmq9ApLWaNrSwVmbWARwN/N7d70+SNhgNLRTJt6SrJYrI4JIMLcxdMJAVBQMiG9PJV6S9JQkGNAOhSAnVs/SwggeR9qVe+CIllHQon+YBEGlvCgZESijpUD7NAyDS3hQMiJRQ0qF8mgdApL0pGBApoaRD+TQPgEh7UzAgUkIzJ3Uxd9ZEujo7MKCrs4O5syYO2CFQ8wCItDeNJhApqZmTumoeDVDZTqMJRNqTggGRNpfWkMAkwYOIFIuCAZE2Vs98AiJSPgoGRAomyZX+YEMCFQyISIWCAZECSXqlryGBIlILjSYQKZCkk/9oSKCI1ELBgEiBJL3S15BAEamFggGRAkl6pZ90PgERKSf1GRApkNkzJmzUZwCGvtLXkEARGYqCAZEC0eQ/IpIFBQMiBaMrfRFJm/oMiIiIlJyCARERkZJTMCAiIlJy6jMg0ibSWpBIRMpHwYBIG9CCRCLSCAUDIjmlBYlEpFkUDIjkkBYkEpFmUgdCkRzSgkQi0kwKBkRySAsSiUgzqZlAJAON9uwf19lBbz8n/sEWJAJNUywi9VEwIJKyNHr2a0EiEWkmNROIpCxpez+EAGLqvEXsOOdqps5bBKClh0WkaVQzIJKypO39A9UkzJ01kZvnHJJZOUVEKlQzIJKypD3766lJEBFJk4IBkZQl7dmvOQJEpNUUDIikbOakrkTt/ZojQERaTX0GRDKQpGd/PSMHRETS1LSaATMbYWYPm9kN8TbRzM41szvM7CtV29WdJlJESWsSRETS1syagT2Ay9z9vwHMbB9gGrAfcJaZHQo8WW+au1/XxM8ikirNESAirdTMYGAy8AYzOxhYCiwDfuLubmYLgSOBpxpIe0kwYGYnAycDjB8/PvtPKKXT6EyDIiJ50Mxg4A7gUHd/1MwuAToIAQHASmAssBa4r860l3D3+cB8gO7ubk/zw4jUM9OgggcRyaNmBgN3u/vz8X4PMJIQEABsTui/8EwDaSI1S+OkPNj8AP3llcY0xSIiWWjmSfRSM9vTzIYDM4HRhHZ/gD2BB4HFDaSJ1KRyUu5dtRpnw0l5wZLeRPkknR9AkwuJSF41Mxj4JHApcCdwK/BpYJKZXQDMAS4DfttAmkhN0jopJ50fQJMLiUheNS0YcPd73H0Pd5/o7me4+4vAocBNwJHu/kAjac36HFJ89ZyU+y4ktGBJb+KZBjW5kIjkVUvb2t19tbv/2N3vTyNNpBZJT8oDNStAspUFkwYPIiLNohkIpXSSzvg3WLPCzXMOqbnzX2U7jSYQkbxRMCClk/SknGZbvyYXEpE8UjAgpZTkpDyus4Pefk78ausXkXah8fnS1vrr+JeU2vpFpN2pZkDaVlqT/KitX0TanYIBaVtJZwgcjNr6RaSdKRiQtlXvfAKqARCRslEwILmUxkk5acc/rR0gImWlDoSSO2mtHZC045/WDhCRslLNgOROPW39g9UktGI+ARGRIlEwILmT9KQ8VPW+5hMQERmcmgkkd5KuHZBW9b7mExCRslIwILmT9KScVvX+zEldiRYeEhFpF2omkNxJ2tafZvW+5hMQkTJSMCAtNVDHvyQn5aSrEIqIyMYUDEhd0pgHQNMFi4jkg4IBSSytk3iaQwhVvS8iUj91IJTE0uq9X+8QwkYnIxIRkY0pGJDE0uq936ohhCIisjE1E0hi9fTe7696P2nHP80QKCKSDdUMtKkFS3qZOm8RO865mqnzFqValZ50HoCBqveBROP6k9YkiIhIbVQz0IbSXH0vjTn/B6vev3nOIRpCKCLSYgoG2lA9vfT7k9ac/2nOEAgaQigikjYFA20orZNvWkGFZggUEck3BQNtKK2Tbz1BRRodBUVEpLnUgbBF8tTBb6DyJO2wl1ZHQRERaS7VDLRA3jr4DVSeN+/TxU8W99Z8RZ9WR0EREWkuBQMtkLcOfgOV5/p7VzB31sSagwrNAyAiUkwKBlogbx38BitPkqAizY6CIiLSPOoz0AL1TJ7TX5t+q6YFHkg9fRVERKT1FAy0QFoz+HWOGtnv9kmDirRO4jMndamjoIhIAZm7t7oMTdHd3e09PT1Nf9+BOvgN1vGvr6nzFvVb/d7ZMZLn1774kg5+A52A+/YxqN4eNJmPiEg7MbPF7t5dy7bqM5ChrGfwe2r1Gr5w3F5NnxZYRETaS9OCATPbCrgcGA78EzgO+Atwf9zkA+6+1MzOBY4Cfufu74+vrSmtlfq70q+ng19/+QzWMa8V0wKLiEh7aWafgROA8939cOAxYA5wmbtPj7elZrYPMA3YD3jczA6tNa2Jn+MlBmrT7+8EDgOffAfK5+DdxqTSpq9V/0REpD9NCwbc/avu/uv4cAywFniDmf3OzC42sxHAQcBPPHRkWAgcmCDtJczsZDPrMbOeFStWZPbZBqoBGG7W7/YDnXyHGu/faMc89fYXEZH+NL3PgJlNAbYGfg18290fNbNLCFX+o4H74qYrgbGEoKGWtJdw9/nAfAgdCFP/MNFAV/rr3OkYObzmGfzSGu8/EK36JyIi/WlqMGBm2wAXAm8GHnP35+NTPcCuwDNA5bJ5c0LNRa1pLTNQm35XVd+BWk6+zZi0R6v+iYhIX83sQLgJ8CPgNHd/yMyuMLPPAPcAM4HPAi8AxxI6Gu4JPAgsrjGtKZKuypfk5KvV/UREpBWaNs+Amb2PcMK/KyZdT6ghMOAqdz/DzIYBNxFqCo6It4dqSXP3BwZ7/zTmGWjGOP0k8w+IiIgMJMk8A7mbdMjMOoCjgd+7+/1J0gaTRjAw0OQ/XZ0d3DznkIbyFhERSVOhJx1y99XAj+tJy5rG6YuISDvS2gQJaJy+iIi0IwUDCWicvoiItKPcNRPkmcbpi4hIO1IwkJDG6YuISLtRM4GIiEjJKRgQEREpOQUDIiIiJadgQEREpOQUDIiIiJScggEREZGSUzAgIiJScgoGRERESk7BgIiISMkpGBARESk5BQMiIiIlp2BARESk5BQMiIiIlJyCARERkZJTMCAiIlJyCgZERERKTsGAiIhIySkYEBERKTkFAyIiIiWnYEBERKTkFAyIiIiUnIIBERGRklMwICIiUnIKBkREREpOwYCIiEjJKRgQEREpOQUDIiIiJadgQEREpOQUDIiIiJRcoYMBM7vYzG41szNbXRYREZGiKmwwYGazgOHuPgXYycx2bXWZREREimhEqwvQgOnAFfH+tcA04M/VG5jZycDJ8eEzZrasjvfZDvh7nWVU/so/y/yLXHblr/zzmnc75F/xylo3LHIwMBrojfdXAnv33cDd5wPzG3kTM+tx9+5G8lD+yr9oeSt/5Z/n/Itc9mbkX4/CNhMAzwAd8f7mFPuziIiItEyRT6CLCU0DAHsCD7auKCIiIsVV5GaCBcBNZjYOOBKYnNH7NNTMoPyVf0HzVv7KP8/5F7nszcg/MXP3Vpehbma2NXAYcKO7P9bq8oiIiBRRoYMBERERaVyR+wyIiIhIChQMiIiIlJyCgRYxs0z3vZlZxvlnXf6i51/Y/d8G+77Q/9usNeHYLGz+RT92GslfwUCTmdnuAO7+Ykb57xzzz6QzSBPKX/T89zQzK+L+b4N9X/Tv1suzyLcq/6zLX9j82+DYaTh/BQNNZGb7AV83s44hN64v/8nA581sVEb5Z13+oue/D/ANYDMzG5lB/pmVvw32fdG/W9OAczLMP+vyFzb/Njh2UslfowmawMw+BHQBuwH3AQ8Az7v711LK/8SY/4HAjcByYK27X5ZS/lmXv+j5nxXz3wF4DlgCrAYuSONKIMvyt8G+L/p366iY/5uB7xOmVsfdr04p/6zLX9j82+DYSTV/1QxkqKr95jlgF+CjhJPEuwgnjLTy35kw6dJ5hIUp3gfclWL+WZe/kPlXuRd4GfDvwO+Bk4C73N0bacPLsvxF3/ft8N0ys02BNwJHA5cARxA+x91p5B/vZv3bULj82+HYySJ/BQMZiieDgwgzJK4gHHiTCCeM481s/UIVSU8alXbpWEW0D9BD+H/uDvwFeKOZvSav5W+H/OPrDiMEAZsRJsCaDNwAvMvMDqnUDJjZ8DyVv+j7vsjfrUr+wGuBVwBXAg8RftwfBV5nZjWvNteq8hc1/3Y4drLIX8FAhiy0RZ0FnA6sBb4CLCXs91cBu8btDgcSrWAVD4jXAWcAc2N+FwK3AiOBAwhXq5jZwVZH56Qsy98m+e8JfAr4ILAM+CphjQwnBAfdcbujgaPr+GHJrPxtsO8L+92K+R8GnA1cDBwMfJGwFPso4K3ERdjMbFqsQchb+QubfxscO5nkr2AgW68hRPwvA4YTDroJwOPAl4FXmNkbCCeUx2vNtOqkMhV4AXiK8L/8KeHq4n7gO8BuZvZG4HygnivfTMpf1Pz7OZmPBW4j1AaMBM4kXO09S6i628JCm/AcYGkd/Qey3D+F2vfNyj/r71ZV/ofHvw8DWwG3ADsC98T3mmhmb4qfa7sclr+Q+UdFP3ayyV8dCNNhYXzqW4E73P3+qvRTCCeLcwlR5geBVYSIdFvgKuCn1a8ZJP83AbdWr8NgZp8iVHG9BTiO0O71JOHqdCRwB/B9d78vB+Uvev5vAe5197urquveQOjA80ugk9D++DfCl3BHwoIk17r7A60qf5vs+6J/tw4Fetx9ZVX6p2P5/xX4j1iGR4EtgH8AvcA33P0vOSh/IfNvk2Mns/w34u66pXAjRJunx79bA/9Z9dwHgQuAveLjbYAPABOBYTXmvwdwGiGCHQO8o+q5eYSTzqHx8c6EarDdgJE5KX/R89817v9/AbYEZgObxudmxv29T3w8DHgn0J2H46cN9n3Rv1vdwH8T2qa3A95c9dx5wLeBfyP8iHfHz/NqYLOclL+w+bfBsZNp/tU3NROkwMxGE3qQP+TufyS053Sa2WZmNsHdv0SI2j4YX9JNiOoe8xomuTCzLYBjgT+4+7KY/9iY/5buPgd4hBAlQvjR2R74m7uvyUH5i55/B3Aq8KS730P4cRkFDDOz6e6+IOZ/SnzJwYTOhI8MlXfW5W+DfV/079ZWhI5qt3u4wj8bGGNmmwG4+2xCx6/XxPy2BdYBj7r7czkof2Hzb4NjJ9P8+1IwkI55hMhtrZn9jDBU6K/ADwhDPnD3c4CnzewjhGrBz7j7ihrz/x9CldYKM/sBocrxj8ACQo9S3P2TMf/jgROAL7j7kzkpf9Hzn0toX9zCzK4j1ASsAr5J7Ojl7hcCz5nZXEKzwZle+7LaWZa/6Pu+6N+t84CjgD+b2TeBaTH/a8xsatzmfAAzOwR4N/A1d/9HTspf5PyLfuxknf9G1GegQWZ2MWGikEuAHxI6cdwC3EkY9rEpoT3ws8CehDadP7j7szXmP58w/OV/CZ1b7gEWE8a2305omz6WUN31VuBPwP+5++qclL/o+X+L0H77aeA3hA5FvyIMQ7qHEAx8kHDFtyWwE3Cnuz/T6vK3wb4v+nfrIkL/kfsJx8fiePsr4fhZTahB+jbwccLxtTRB/lmXv7D5t8Gxk2n+/RlR7wsFLIwFvtHdv2tm5xOqjr9LGDt8AKHt5hDgInd/3sz+DyDBAbcXcI+7fyn+sHQQOqo9TzjpdALHAF9397Vmdk3IvuYDLuvyFz3/rYCfufvP4hV/B+Eq7nWEyWIOJlQrfsXdnzGzNcDKWqp3sy5/G+z7on+3pgPL3f1TZvZtwlDT3xMmFBpHGHXyLkIHwXVm9g3gxQT5Z13+wubfBsdOpvkP+L6qGWiMhclkLiZEls+zYbzqKYQryne5+71mNtzd19WZ/1cJnVseAp4gRIXvAh4jdIhZ2mD+WZe/6PlfSrjK6CBE6sMIs30NA4539/vMbJjXschJluVvk31f5O/WMML8AbsAvyWMEgA4njD17Wx3X5zj8hc2/zY4djLNvz/qM9C4VwBXE8aYP0aYI3ol4YB7ijAelAb+YXsDiwidR54gDHl5JOb/F8Lwo0byz7r8Rc9/M+BHwCcIVb5LCT8uzxNq1raK+de72lmW5S/6vi/kd8ts/XjwGYQq3bMITQLfA/4MPEOYn+LOPJa/TfIv5LHTxPxfQsFAA8zM3P1B4MdsmNe6kzDu8z3Ar4GjrI6paCvc/Q7CyegkQhVjZVGTjxCuLqZX/fjkqvxtkP8Id/8nocPOOwhjd4cTph8+HrgIeKuZ1dXclmX522DfF/a75RuqWxcBlwMnAn8gDP36V8LY9hHAfvWWPcvyFz3/Ih87zcp/oDfVrY4bMCL+3Z4wI9RYQnvObcBB8blzgN0azH8MYczxTsB4QnXjgfG5C4Bdc17+ouY/PP59OWH0wBaEH/NbgYPjc6cShoTlqvxtsO/b5bu1DWGo4B7xOLoBmBqfuwTYKeflL1z+bXTsZJL/oO+ddoZluLHhRLEjYYjZsfFxJ7Bv1XY1TVwxRP7PAEfFx68BJsf7Rpz0JuflL3L+ryJU0b276rlJeT5+2mjft8N3azVhmOkIQmfTAyrbAJsXoPyFyr/Njp3U8x/y/bPItJ1vVf+wVxIWpTkn4/zP7WcbK1D5i57/2f3t83r/B1mWvw33fdb5Z/ndegD4ZNVzw6r/FqD8hcq/zY6d1POvqQxZZt5utz7/sL8Cn6h6ru4vufJX/o3mX+SyK3/ln9e82yH/msvRrDcq+o0NbTlZHRDKX/nnLm/lX1P+WZ8sir5/dOy3KP9EZWnmmxXlVvknALsThqhU5mPYiVCF09A/rCq/nYHN++T/UAr5Z13+oudfyW8SsEdV+i55L38b7Puif7cq+XX1+Tw7ppx/1uUvXP5tdOxkkn+jt6a+WVFu8Z91EHAN8MqY9jLgPuBTjf7DCMNEDo75b1uV9leq2ooayD/r8hc9/9fE/X8tMDGmbU2Y6vOzeS5/G+z7on+3OoHXE6ak7ohp4wlLD59dgPIXNv82OHYyzb/RW9PfMM83Qk/NzQlzhC8njBOeQhimMg44IoUDYizwU0Ike0M8GEYRpic9vpH8sy5/m+Q/EriJMIHQNYT5Al5NmF1wSl7L3yb7vrDfrfi6XQjrCNwby19pHjgQeG8Byl/I/Nvk2Mk0/zRumo44ihNVuJlNBD4G3EWI2A4kLB7yjaptR7j72jrzP4CwsM2thFmw9gZ+7O4LU8o/6/IXMv+q1+4MzCH09r4NOBx4wd3PqtqmnulPMyt/0fd9G323jgQ+CvyCcHJ6DfBpD8vjVrZt5NjJuvyFy7+Njp1M8k+TZiDc4C1mVllB7GnCVcBawsIWfzCzifF56vyHHWFmBxIWunmCMBnJX2P+T5rZa81sWgP5Z13+QudvZv9uZjMJs3qtJixW8rKY/3Vm9hoz+7eYfz1TfGZZ/kLv+ybkn/V3a18Lyw1/gLA63HOEaYYPAEaZ2QQzqywpW8+xk3X5i5x/0Y+drPNPT6uqJPJ2I8xjvZTQ/nQkYZGLOwmr021DiEhXARPqyHs4oX36LmAZYTrb7xKixCMJJ6U7CMts7py38rdJ/j8mzAv/R8Kqg18n/LAfQ2gH/j3hx2b3vJW/DfZ9Yb9bhOrdX8Xy/x74EGGNgd8Qlh/eAbgZuB4Yl8PyFz3/Ih87mf/up3kr/RLGVVUzDxK+7L8kLD6zB2Fxix0J1TsfIfT6fCFh/sOAFwknmpsJ82JvBewT89+MsCrep4DtCAtR5Kn8Rc+/Um17O+EH5UpCZ8HdCD80BwDvB/4L+BfCFzgX5W+DfV/o71b0D8Ix8VNgISFw/ABwHeEk9G3gW4R27aTf3ax/Gwqbf9GPnSYdm6kqbTOB2fpFHjaLf39LGN4xmrDS1dnALcBnCCeSRYRVo55OmH+HhzDxRkJ02Al8PN4eJES6VxOuPp4C1uSs/EXPvzP+vZOw0tdmhC/gPEJtwfuB+939JsJiMo+1uvxttO8L+d2qep9RHta4vwXoIcwX/4F4e57Qd+DbwGXAs4SAoOXlL3L+RT92mnVsZqLVVROtvBGWGL2KMHf4FjFtFvD/CJ2DFhH+mR8h9DofnjD/1wPfjPdfHv+eSjgRbUE4qH9HqHqcBmySs/IXPf/DgZ9TNQ88MBn4fHzuV4RqvI8Ds0neyzmz8rfBvi/6d2s6oXMgxEVtgLnA3Hh/CWE1uQ8R1rDYLGflL2z+bXDsZJp/VrfSjiYws+mEf9DZwJPAs+6+ysy2J/QY3gn4J2EYyD3Afe7+RML8PwScThgOs8bd/2lmOxIO6tGEjmw/i3+vd/e/56z8Rc7/IEI14mkx6Ul3X2FmncAJhKFJ/yBc8V1NGFGwMg/lb4N934z8s/5ufYwQIN4H4O4vmNkE4JuEq7jVhGrelwPfdfcVOSt/IfNvk2Mns/wz1epopJk3YLv4dyIhMtuV0Jnse8DWVdt1EMaAvhHYMUH+W8a/e1XlfxAwnxjhxue3JvQqfROwTY7KX/T8dwA2ifnfBuwLHEJoDhgTtxlJqM414ChgrzyUvw32fdG/W5vFv/sQrtx2icfOBcCoqu22J9QEzAJG5qj8hc2/DY6dTPNv1q1sHQiPNrP/JLQd9xA6/jwJvN1D9LkfcARhGMiWwLHA4loyNrORwLEWxiJD6CVanf8/zGxfwo/NNwmzlh1D6Fna8vK3Sf7Tgf8hdDi6AfgsYRjSOz3UCuwHvA04N25/LPCJnJS/6Pu+sN8tM+sA3mlmu8XXLiYEAWuBk9z92Zj/K939x2b2WkJv8BuAIWuTmlD+QudPsY+dZvzuN0ero5Fm3oD3AI8Tosv9gf8DronPTSD0Fp4eHxsJ1xwHPg38ndAutD9hGFt1/tdV5b8VVVFvTspf2Pzj9ocROhN1xPzvAn4Sn9+T0CP54KrXdOSo/IXd923y3bqEMKXwtoT+JP8H/CA+txuhnfqg+PgVxJqmHJW/sPm3wbGTaf7NurW8AE35kBsWhNieUGX8FsIQs80InVCuJ/QM3qfB99kd+Abwhvg+m6aRfxPLn0n+bFhgZHvCpD9ZlX8UoUPgO2N+nYSxvb+MPygHVO/POvdPJuWPef8kg32f6bHTxPyz/m69nlCrdBhwBaHJ6b8IQwp/AXQ3eOxnVf5C51/kY6fqfTLNv1m3Ugwt9PgfI1QxbQNMBR529+eABYT5rUe5+2LYaHhIzeK40soUpTOBx9z9ecKPSV35V7arKv+oLMpvZpvGux0x/wPSzJ8NY/e3jbdU8q9sZ2bbxc+wBaFTznjCVKWrCE0G4wlVvktgo/2Z1JZplb+q7N1mtj+hr8PWaeRdvV3VZ92cDP63/eSf6rFZlf9WhO/WMaTw3eon/xGETqX/Aaxw90cJPdZfA6x19546838x3h1Nir8NVSrbp/rbU6VyjuhMK/+qY390TKr8Lmd5bKb+u2xh9sBdCb+bWez7pmr70QTxh3YiYXGahwiTVSwnDC9zwtjhTxN+ZHYHZiU5WZjZTu5+f7w/nNBp7R5Cm6MTJpRoJP/XEYbPXAP8L6Fj033AeYQfgrrLb2YvJ1SJvj2WdwGwH2Ehls/FzRrJ3wi9rS8F3uqh3X7/mH9D5TdbP+f3DMIP+CnuvtLMJhHG8Z5CqM59ecx/KqGDz3vcveYxvbEdudPdb4uPuwkzGdZ9/FSVfTrwBWC+u19UtW8a3vfxfQ4kdIL7tbvfEttel6WY/2TCHPG3E+ZxeBXhO5ZW/nsR/oe/c/e/mdnehFkjv0SY0KXR79Z4d3843u8kNCX9DriQ8L99GeF7MZ1wsvpAwmNzCqFvwS8IbclTCN2pqn0AAA4dSURBVMMRG/5tMLNXxHK9jbD07e8sTGt7J+ntn1cQ2ulPdvcnq/Kvu/xV++YI4B2E7+MzGRybuwOr3H15fDyFMJPhhcC6esrep/yHEL67n3D3qyxMV31XI/um5dKoXsjrjQ29U99HGNbxn8B+8bkphFmhqleqG5sg70ogdTXwsar0UfHvZMI49rryj9tPJbRVnkrofHJQ1XNTCFXijeR/DOGAPR64qPr1je6fPu9zF6F6vbNP/tc1WP4jCB2JxgKvJFQ3bj1I/tvVUfazKv9fwtXpzmmUn3CSvh74MPBlNp4LIY3/7VTCD3dlYZ0d++Tf0P+WMD76NuC/CRPvvCXl8u9XVf5LCD3Id43PTSY0+zSSvwE/BN5XlbZ5/Ls/oWlpcnw8CnhZwvwPJsw89wnC1MUTqp5L47fhOEIH2Q8CP2DjXusN75/4mm0JE3F9q893d3Ij/1/gaEJw9ApCbdiWbFgBsqFjhw2/y98A/qPv/6/RssftDyF89z8JfIXQrDEirf9tq25tXTMQryROd/e3mNl4wj9qd0L134HAje5+vTW2Ct6PCFfrV7j73Jj2csJJ5LJG8jezowgnnwvN7F2ENrU3EarsPkq44lvUQP7HEYbFfMPMLiGMn/4eYUasNwE3NFj+Ye7+opldQBhus5ZQlbYNodPQ7Q2Wfxahne52Qu3GI4Tg4HrCyfDumH/ileSq3mNXwpXWVfEzbEOoZdoOuKOe8pvZ1oQRDd9399vNbDbQE/f1WMI45Wsb3PdHEtZZ+LyZfYjQVPILwv/4YOCmBvM/lnDy/JaZvZXQRnoE4Yr0DGBhg/lPA05091NiDcS+hCagu4B3EVZ8q/vYie/xY0IA+Ut3/2xM25Fwdfpld7++nnxjPscCuPsVZvYxQkeyUwh9Ec4h/DY0cuy/G3jC3ReY2fcJNTKXE8bgfxy4vNHftvg+XyFUg7+McPGwDWHVz5/XW34zO4lQi/QA4Xv7PGHO/iWE2oK6j/2qK/dpwMmEoOhgQufQpYRgo5GydxEmn/qyh9qYyr5+OK3f/ZZpdTSS5o0NUeEYQhtgJ6FT0JSYvgPhAHkXVZFuHflvx4ZIcHdCZPhDYE7Vti9Pofz7Aa+LaVsAl1ZtW8/CHH3z7yJeMRKCizMINSjHUHWlUef+GV6VfgIhEKsMx/oB9V2pVJd/ZLw/hxAMbEtYG3wuIWhKPKtXf+UnNDM8TBg3vC3hh+UCYHydeW8T/1auQocROjyeVbVtPTUYGx2bwCTCSefzhCvId8a/HwK2bXTfAG8FvkO46tqP0EZ6YOX/kzT/ft5vD8LV3fbx8d7x87y+nmOzn/w3YUOH0suB/656bqcU8j8OOCren0CckS4+bnhRGjZc2GxKqN37NOE3aB/q+G3rb//Ev3Pi+xxFqKX8PLBDg3nvHPP5X0KtwKGE2qXJJBzhM8h7dAMr4v7Yg9CR+GNAVwp57xD/Do/H5Bl9nyvira06ELq7m9mhhCrv8wk/ICOAGWY2wUOnoNuBkwjtOvXm/zXgi2Z2OCEoeC7mOcnMTo/bPtJg/ucRAoD74tPPAB0WOst9kfCFaiT/zxM6vXh87vPu/hnC1eNB7p544Yz+9k+80n2AMInII4QAZG8PbcCJOwRV5X9+vL8c+LD7/2/v/GO9Kus4/npL/HApzsoVSxTDwiU4Z7YIG2oZzDsSnK0WrbKyWpo0W0uxsmJtpI1Z4AQ2UdaiRm1WiFrqarpmuDEIEprGZV6RyICKW14Mr3764/N84cvtcjnnfL/nyzn3fl7bd4Pv+Z43b57znHM+z/N8nuex/Wa2DR93nGlmuTYuOYb/2fia8/fgXdT78XyQSWRcC30Q7VWSlgIzJE02TzBbg9edRmsy94pkTfor8bpzOj5n+9fABjNbjXfrX0GBTVEG6N+B7+FwJr5G/CXpZx9Kv828Gl8zki6U9KWksRWfq71I0hgz24QP+X2Tguu4D9A/hOcfAHwWL/+vp2M726C/1sweSod24Vsdv07SEryVXVT/xqS/wcy2myerLTKzb+Cr2s0zT5xt1X/j/tmBP0efxfOtusxsT957d4B2N/4cXmJmvWb2GH7fdpnZwVa9p39jIx6gjk91aTsebGRerXAQ/QVJe09q9b+K32tvl+d2kd4xtWTYLDokz+Yfh3fFrcTnfZ6Hv4DeCcyT1Iu/YHfgL9dW9PfiCWkXpZ6pP0m6FnhA0lYzW9+C/ork/0J8L/XfmNlmSafi0f+WpgdNUf29eOvxYkk/w18Q78Nb1TvyaA+ivxKfN/yu9NmKD6X8EG8xjYJ8Wf3HKJ+peL7AJkkT8FbSVeRbLOd4/i/Au0nPTNf3MmBPngfuAO27k/ZFeN152My2SFqGBwinm9k/W/C+An/gTcWzmXuBt0jqxlt53XkDpUH0/4EvC/swsNm8y/VHwHeK+G+iH7hV0kEzW2VmX5N0J/B9SQ/i9/H2FHy3ov+Smd1nZvvSQ/2ldG3XS9qS994dRL/PzO6FwzN1DB8fvx/oMbOiC870Awsl/cfM7mv6/ix5Zn4XacZMC/q3Jv3V6btdeNf3p4BrSLML8ty7A7T7zOxe8+GT0fIE3R48T6PwsMwxvD8PfExSPx7w/bXFunOLpF4zW21m/ZJGp7rzO2CKpN/bkdkjtWPYBAPpIvRJ2oiP7T6GvzBm4C+fHry19DZ8XFyN8aUW9P+Oz0ueI+lfZrZL0pWWI1v9OPovJv0rJW3DV+jqN7OvwJHxsRb9z8JX9DqAJ1XtNLNvtUl/L96tex7+EFlsZpsarYo2+b8CT0g6Gw9o9qUejnbo78cDpNfw8nkZfxndkUd/iLL5INAlaTfe0n6uyIt0iLpzKR4sTQA+AWwzs8/l8X4M/eV42Y8Dpkt6Br/HNrYQCIAPw/wcuErSqWb2AzO7SdLH8Uz/acCP5bN2XivwQmroz5P0ejO7q+mh/nLRe3cQ/bnyHQ/vSi13UkPkOTNbkP6eufyH8p++78IbOS+a2ZI2+L9a0inJ/1OSXgDWFb13B2jPTdpLzewV+cqDJwO9Zra0Td7HJ/3vJv3z8SmLtxX0PlC/UTaNutKNryVQ20AAGB45A3gL7qN499t8fJOLRtb3JHw8bVpJ+mfhu1FNG3BO5oVtjqM/Ee+WnYovh9o4J/MOexn8LwKmluT/nPT38/Pq5vB/WL+E8jknlU+h65vB++3ABSWW/W0lls3EVPdb9f8RPGhp7FB3Gr7b5IK8dSWn/vVNvxtVgv9m/Zkl6N9wjHPyXt9M5Z/33s3qvdlvWd7LvLbD5VP7nAH5jIG78TH0G/DFPc4GrknHnufIxiJl6L+Aj51e3XyepZrTBv3d+Dj7XDPrSefIMkahGf1PxrsAy/DfA7wDH37IrJvT/5RB/LerfHrw3qSj6k+W/0dG72/FZ1jk0s7h/aiyaXPd2Y3X/Xb4vwnv0p1iZgfwJN8PSLoli1ZB/dmSbk6eD882aaP/2ZIWJs0n0jmZW6UZ9Gc19JspeH2PW/557t083ht+W6ibmetOm6/tzVm06kItg4GmrqpReNd2t/nUoLV40tEOvFvnMjxpaj8+NaxM/QdK1N+Hz3sGjl+hC+qXWT5l6++l3PLPXH8qWncO65dQd9rhfzGeTf5f4MOSzjVPorwufZeZAvp58yfy6h81Rl2w/DPrl+A/c/lXsGwqVXeqTi1zBszM5PvVX49nYu+TNNXMnpZ0CH9Y9QA/xRe32IwngIR+6JeqX2fvFdGfi2d97zCflXAn5MrPCP0TpF9n753QrzxWgbGKvB98AZIn8PnrK/Ds8RtJc7/xVstD5Bh/Cv3Qb4d+nb1XRH9m6NdTv87eO6Ff9U8tewbwLpt1ZrZG0l/wKXjn4nuS78STPPZb8ezO0A/9ovp19h76oR91vzz9SlPXYOBv+NKS4OuXbzOzL0u6HM98FrCmMQZkKawL/dDvgH6dvYd+6EfdL0+/2pzorol2fPAEjzfjXTuzQj/0q6JfZ++hH/pV1R4O+lX71HI2QQP5ymjgiwk9AvSZ2SPpWK7lMkM/9NupX2fvoR/6VdUeDvpVpa7DBMBR82n/iC81mXtlvtAP/TL06+w99EO/qtrDQb+qDIstjCWdZr4YBErb5oZ+6FdBv87eQz/0q6o9HPSrxrAIBhqUHbmFfuhXUTv0Q7/K+nX23gn9qjCsgoEgCIIgCPJT6wTCIAiCIAhaJ4KBIAiCIBjhRDAQBEHLSBorafKJ9hEEQTEiGAiCoB3MB5YN9QNJiyVdLOkkSZdKmizpug75C4JgCCKBMAiClpB0Bj4nuxvoBSYAe/DGxslmdrmkccCj+EZH04Fr8d3hHjSzWSfAdhAETdR60aEgCE4skkbj270uM7Pvpe82mNmcAT/9AvC4mb0q6YvA7Wb2iqRnJM0wsyc7bD0IgiaiZyAIgsJImgR8BhiP7/AGcAnwB7yxsQ74FfAksAp4HPi0mX0ynf9G4H5gjpn9u5PegyA4QgQDQRC0jKTf4pu59KeegelNx+YDbwImA+8BdgPvB7YAE4Ft+HDBys47D4IAYpggCIIWkDQq/XHQVoV805e1wHuBN+BBwCF83/guSQuBjWb2aCf8BkEwOBEMBEHQCp8H5gIHgV/KN3WbIml9Oj4K+AWwHcDM+iS9G3g6HR8L9HXUcRAE/0cEA0EQFMbMlgPLm7+T9NTABEJJM4GTJI0Bvg18NR06AzjQAatBEAxBBANBELSbUwb5biwwBlgC/MTM/ixpNb5n/LMd9BYEwSBEAmEQBB1jpOwAFwR1I4KBIAiCIBjhxHLEQRAEQTDCiWAgCIIgCEY4EQwEQRAEwQgngoEgCIIgGOH8D7d7OGV9DMSfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12f68565b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif'] = 'SimHei'\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "data = np.load('../国民经济核算季度数据.npz')\n",
    "# print(data.files)\n",
    "# print(data['columns'],'\\n',data['columns'].shape)\n",
    "# print(data['values'].shape)\n",
    "# data['values']\n",
    "\n",
    "name = data['columns']\n",
    "value = data['values']\n",
    "plt.figure(figsize=(8,7))\n",
    "plt.scatter(value[:,0],value[:,2],marker='o')\n",
    "plt.xlabel('年份')\n",
    "plt.ylabel('生产总值(亿元)')\n",
    "plt.ylim((0,225000))\n",
    "plt.xticks(range(0,70,4),value[range(0,70,4),1],rotation=45)\n",
    "plt.title('00~17年国民生产总值散点图')\n",
    "\n",
    "plt.savefig('../00~17年国民生产总值散点图.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12f633f0b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function xticks in module matplotlib.pyplot:\n",
      "\n",
      "xticks(*args, **kwargs)\n",
      "    Get or set the *x*-limits of the current tick locations and labels.\n",
      "    \n",
      "    ::\n",
      "    \n",
      "      # return locs, labels where locs is an array of tick locations and\n",
      "      # labels is an array of tick labels.\n",
      "      locs, labels = xticks()\n",
      "    \n",
      "      # set the locations of the xticks\n",
      "      xticks( arange(6) )\n",
      "    \n",
      "      # set the locations and labels of the xticks\n",
      "      xticks( arange(5), ('Tom', 'Dick', 'Harry', 'Sally', 'Sue') )\n",
      "    \n",
      "    The keyword args, if any, are :class:`~matplotlib.text.Text`\n",
      "    properties. For example, to rotate long labels::\n",
      "    \n",
      "      xticks( arange(12), calendar.month_name[1:13], rotation=17 )\n",
      "\n"
     ]
    }
   ],
   "source": [
    "plt.figure(figsize=(8,7))\n",
    "#picture 1\n",
    "plt.scatter(value[:,0],value[:,3],marker='o',c='red')\n",
    "#pivture 2\n",
    "plt.scatter(value[:,0],value[:,4],marker='D',c='blue')\n",
    "#picture 3\n",
    "plt.scatter(value[:,0],value[:,5],marker='v',c='yellow')\n",
    "plt.xlabel('年份')\n",
    "plt.ylabel('生产总值(亿元)')\n",
    "plt.ylim((0,225000))\n",
    "plt.xticks(range(0,70,4),value[range(0,70,4),1],rotation=45)#rotation刻度值角度\n",
    "plt.title('00~17年各产业季度国民生产总值散点图')\n",
    "plt.legend(['第一产业','第二产业','第三产业'])\n",
    "plt.savefig('../00~17年各产业季度国民生产总值散点图.png')\n",
    "plt.show()\n",
    "help(plt.xticks)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 折线图\n",
    "- 趋势关系\n",
    "- 数量差异\n",
    "> plt.plot(x,y,color=None,linestyle='-',marker=None,alpha=None)\n",
    "> - color同上面的c，补充一下'k':黑，'c':青\n",
    "> - linestyle线条类型，marker同上\n",
    "> - alpha接收0~1小数，表示点的透明度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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I9fcQERHpqx2H6yge13nWQGZqfE3m69cxA2Z2PjAS2A+U+YqPA/lAZozLgt3/FjNbb2brKyoqYvStREREwvexhZP50IJJncp2Hqnljt9vYu/RE4NUq876LRgws1HAD4GPA3VAum9Xlu++sS7rxjl3v3NunnNuXl5eXmy+mIiISASuPWc8S2eN61RW19TG79bvZ+eRukGqVWf9NYAwBXgUWOGcexvYgNeUDzAH2NsPZSIiInHlYHUDWw7W0NbuOpUX5qR17I8H/dVp8QngHOBLZvYl4JfAh82sELgSWAA4YG0My0REROLKqo1lrFyzjc1fuZzstOSO8jFZqSQnGmVxknioX1oGnHP3OedGOueW+H4eBJYALwMXO+eqnXM1sSzrj+8hIiLSF9sO1VKUm94pEABISDDG5aQN+ZaBbpxzlZycAdAvZSIiIvFk++HaTvkFAk0enUlrmwu6b6DF19wGERGRIaK1rZ3dFSe4qDj4APZff/xczGyAaxWc0hGLiIj0g73H6mlua6c4xMqE8RIIgIIBERGRflGQk8avPjafRdPGBN3/8u5jfPiBVzhSO/iDCNVNICIiAqwu9Ub+l1c1UJibzvKlxSwrKYr6epmpSSwpHhtyf0NzG2t3HGX/8QbGZqdFfZ9YUMuAiIgMe6tLy1ixajNlVQ04oKyqgRWrNrO6tKzXc0N58q3DrNtzPOT+wlwvd148zChQMCAiIsPeyjXbaGhp61TW0NLGyjXbor7mt/6+lf9Zuzvk/oJcrzWgvErBgIiIyKAL9UCO9kHd1NrGnqMnmBFi8CDAiLRkslOTKI+DxEMKBkREZNjzN9mHW96T1aVlLP7Ws7S1O/73lbd77GqYO3kkWXGwguHg10BERGSQLV9azJ2rNtHY0t5Rlp6cyPKlxRFdxz/2wN/lUFnfwopVmwGCDkb81cfO7UOtY0ctAyIiMuwtKynioxdM7tjOSU/i7mtnRzyboD/GHgwEBQMiIiJARkoS/jxA7507IapphZGOPfjTa2Vc9r1/0NDcFnT/QFEwICIiApTuq2T62Cw+vvA0ZhWNiOoakY49aG1z7DhSN+jTCxUMiIjIsOeco3R/FSUTRvKf7zqDa0rGR3Wd5UuLSUronGa4p7EHJ3MNDO6MAgUDIiIy7DW1tvOxC07jqrMKAKJutn/nWQWkJiWQnpyAAUW56T2OPSiMk1wDmk0gIiLDXlpyIv9y6XQAfvTMDr735Ha2f/1KkhIje2d+fsdRTjS38fOPzOOyM/J7PX5cjj8YUMuAiIjIoNp5pI6axhYARmel0u7gcG1TxNdZVVrGyIxkLpoRfNnirlKTErnsjHzyR6RGfK9YUsuAiIgMe1/4XSk56cn8780LKPC9rR+saqAogqRDtY0tPPHmIW6YN4GUpPDftX/+kXkR1zfW1DIgIiLDWkNzG1sO1lIyYSQQ/aC++uY23jWnkOvmRjf4cDApGBARkWFtc1k1be2Okom5ACdbBiKc7pc/Io3vvHcOZ0/Ijei8Hz+7k/nfeArnXETnxZKCARERGdZK91UCdDzEs9OS+fSSqZw1PvyH+rG6Jt4oq47qgZ6alEBFbRPVDS0RnxsrCgZERGRYK91XxaTRGYzOOjmI744rZrJgyuiwr/GHjQe4+ofPs/945FME/d0SgzmjQAMIRURkWPvXy2ZwpLbzg7i+uZVjdc1MGJUR1jX+WFrO2RNymTg6vOMDBXZLnFEYXebDvlLLgIiIDGvF47JZPL3zVMC7/vIW1/zkxbDO33qohi0Ha7gmirUMgI4ZC4OZeEgtAyIickpYXVrGyjXbKK9qoDA3neVLi6NaTCjQG2XV7DxSxxVnjiMtObGjvCAnnaN1TTS1tpGalBj0XH99ynwP8QjzE3UYk5XKNSVFTBydGd0FYkAtAyIiEvdWl5axYtVmyqoacEBZVQMrVm1mdWlZn677p9fKuP0Pm0iwzusJFPjSBB+uDp54KLA+ft94fGtU9fnz6+Ws23Ocj/5iHQvveabP3ykaCgZERCTurVyzjYaWzusFNLS0sXLNtj5dt3RfFbOLcrolCSrM8TXdh5heGKv69FeQEykFAyIiEvdC9af3pZ+9ubWdzWXVlATJCzCul1wDsapPfwU5kVIwICIica8wRFrgUOXh2HKwhqbWdkomjuy2b/zIdP7rXWeEzDUQq/r0R5ATDQUDIiIS95YvLSY9ufNAvvTkRJYvLY76mm+W1wB0ZB4MlJacyMcWnsbUvKwe6tP5ERpNffojyImGggEREYl7y0qKuPva2RTlpmN40/HuvnZ2n2YTfOC8iby04pKOef5d7T9ez5vl1SHrc/sVMzu2o61PfwQ50dDUQhEROSXUNrVyRuEInlu+hORo5/ER/hTF//rzmxyqbuSv/7I46HXGj/QSDP3h0+czd9KoqOriv2+sp0xGSsGAiIicEv6x7Qi7j57g/n/uZuPblTzw0fkRX8M/et8/aM8/eh/o9gAuyEnrWLcgmC0HvW6G4nF9yxq4rKRowB/+XambQERE4p5zjg1vVzJ34kiq6pt5YdfRqBYFimT0fmFuOpX1LTQ0t3XbB2DA3EkjyUo99d+r+zUYMLN8M1vr+/xVM3vO97PVzFaYWZGZHQgoz/Md+4CZvWRmXw64VlhlIiIy9Ow+eoLK+hbmTR5JYW46jS3tVNZHvspfJKP3e1vK+HPvmM4fPn1BxHWIR/0WDJjZSOBBIBPAOfdfzrklzrklwBvAr4HzgG/4y51zFWZ2LZDonDsfmGJm08Mt66/vIiIig2vD215z/dxJIwNW+Yt+hcBwyk/mGhi81QQHSn+2DLQB7wNqAgvNbD5wwDlXBiwAbjazjWb2Td8hS4BHfJ+fABZFUCYiIkNQZkoSi6ePYcqYrI6FfcqiCAaWLy0mKaFz6uFQo/dnFeTw0w/NZea47G77Nu6r5JLvPsemA1UR1yEe9Vsw4Jyrcc4Fm5PxL8APfZ//hvdQnw+cb2Zn4bUk+PMwHgfyIyjrxMxuMbP1Zra+oqKiz99JREQGxzvPKuChT5xHQoJRlJvO/MkjSU2K/BG2rKSI6+YWkZGS2OsUxZyMZK44cxyjs1K77XuzvIbdFScYlZkSzdeJOwM66sHMcoGxzrldvqIXnXNNvn2lwHSgDvC312ThBSzhlnXinLsfuB9g3rx5kY80ERGRQdfc2o7DdaweODIzhUdvjb6v/lvXzeFb180J69h1e46TYDBvcuepg1sO1jAiLamjleJUN9CzCd4D/DVge42ZFZhZBnA53liCDZxs8p8D7I2gTEREhpjnd1Yw+ytP8EZZ8ARAkXjyrcM0tgSfHRDMXY+9yQ+f2dmtfMvBGmYWjMC6rHZ4qhro+RBLge8EbH8VeBZoBn7qnNtmZgeBtWZWCFyJN67AhVkmIiJDzIa3K2lrd0zJy+woW7FqEwcqG3joE+eFfZ3X91fxyV+v5673zOIj508O65yCnHT2HavvVNbe7th2qJYb5k0I+97xrt9bBnyzB/yfP+Cc2xiw/axzbqZz7izn3I98ZTV44wheBi52zlWHW9bf30VERAbe+r2VzCocQUbKyffXNt8DORIPvriXzJRErokgwU9hTlq3ZYzrW9q4anYBC6eNiej+8SwuMyU45yo5OVMgojIRERk6Wtraef1AFTfOn9ipvDA3nSO1TTS1tnWMJejJkdpG/rKpnA+eN4nstOSw7z8uJ53axlbqmlo7kgtlpSbxnfeGN+bgVKEMhCIiErfeKq+hsaWduZM6LzPszwtwuLoprOs8/Mp+WtocN10wOaL7F+b6cg0ETGOsa2qNKvthPFMwICIicSsvO5XlS4s5b0rn0fyR5hrYuK+Si4vzOG1MZu8HB1g8PY8/3baQCaMyOsq+8NtSrvnJixFdJ97FZTeBiIicmsJdETBchbnp3HbxtG7lk8dk8q45hT2uC9C5Lml89pLu1+nNqMyUbrkEthys7dZScapTy4CIiMSEf0XAsqoGHCdXBFxdWtbruaH8Y3sF1UHWICjKTeeH7y9h9vicMOvSyF1/2RJVXX6/4QAv7ToGQHV9C2VVDZxe0LeVCuONggEREYmJSFYEDEd5VQM3/WIdfyw9EPKYlrb2fq/Lt/++lVUbvTpsOeRl2D+9oHuK4lOZggEREYmJSFYEDMd63+JEXbP/+b3vZy9x84Pr+70uBbnpHKrxFivactALBs4oVMuAiIhIN5GsCBiOjW9XkpGSGHShIIDcjOSQD/dY1qUwJ63jPudMHMkXL5vB2Oy0iK8TzxQMiIhITCxfWkxyYngrAvZkdWkZC+95hl+9uJe2dsdjmw4GPa4wN53yqoag0/yWLy0mPbnzIy6auoC3lPHB6kacc8yZkMvn3zE94mvEOwUDIiISE8tKilh5/RyKctN7XREwlMCBfwBNre0hByEW5aZzormNmobWoHW548qZnY6NtC5+hTnp1De3cfxEMxvePk59c/f7neo0tVBERGKirqmVq2YXsKykiNrGlogy/fn1NPCv64O8MCDXQE5G93u9c3YhVfUtvHN2AdPzox/wd8O8CbynpJCjdc1cd99LfP99c7imZHzU14tHahkQEZGYeGDtHuZ89Qm+9fetnH3Xk7SGGOnfk0gG/p1eMIJPXTiF7LTg77V52al84dIZfQoEAHIykhmbncbWjpkEQ2vwICgYEBGRGNm4r5JJozOYPDqDtnbHwerGiK8RycC/08ZksuKq0ztlBwz0Znk11Q3dcxREqrGljR88tYNfvrCXlMQEpuZl9fma8UbBgIiI9Fl7u6N0XyUlE0cyfqT3cN5fWd/LWd1FOgjxRFMrx+q6r0/gnOOmX6zj64+9FXEdukpOTODeZ3bw2v4qpo3NIjlx6D06h943EhGRAberoo6axlbOmZjLBF8wcKAy8jn9y0qKGJ+bTlKChTUI8ap71/KVv3R/4B+sbuRoXXPIDIWRSEww8rNTgaHZRQAaQCgiIjGwcZ+XIGjupJGMy0kjwaILBnZV1LHnWD0rrpzJpy6a2uvxhTnpQccTbDpQDcCZRX0PBlaXllHha31Yu6OC1aVlfVpvIR6pZUBERPps7qSR3HnlTE4bk0lKUgJfvGwGC04LnjmwJ4+uP0BignHNOeE9bP25Brp6o6yaxATjjD6+yfunOra0ebkMjtQ29Xm9hXiklgEREemzaWOzmTb25Kj9z14SXWKeMVkp3DBvQtgZ/opy0zhc00hrWztJAX35m8qqmT42i7TkxKjq4RfJVMdTmYIBERHpk9rGFl7de5z5k0d15Baob26lvKqRaWMjG3l/8+IpER1fmJtOu4PDtU0UBcw4uPOKmTGZSRDr9RbilboJRESkT9bvreTjv1rP5rLqjrKf/mM3l3//HzS3hp9rYNOBqohzE8w/bRRfffcsMlM6twCcUTiC86eOjuhawcR6vYV4pWBARET6ZOO+ShITjDnjczvKxo/03tgPhZlr4FhdE9fd9yL//dSOiO49NS+Lmy6YTG5GSkfZW+U1/Om1Mhq7NO9Hw1vjoHOgEe0aB/FMwYCIiPTJxn2VnF6QTWbqyZ7n8SO9N+dwcw2sfq2cljbHu+YURnz/nUdq2X/85H0e21TO/3vk9YivE8yykiLuvnZ2n9ZbOBVozICIiEStrd3x2r4qrpvbOVf/yVwDvQcDzjkeXb+fOeNzKA6xXHFPrv/pS1x9VgFfXzYbgM1l1RSPy+7z4EG/ZSVFQ+7h35WCARERidq2Q7WcaG5j7qSRncoLctJITLAecw2sLi1j5Zpt3jLEwPVzo1v8x8s14HVHOOfYXFbNFbPGRXWt4UrBgIiIRK14XDZrvnAh40Z0ngqYlJjA3dfODjnP3z9/P3Da3mObylk0bUzEb+GFuekdLRAHKhuoqm+JSebB4URjBkREJGqJCUbxuOygSwjfMG9CyAyAwebvN7a0s3LNtojrUJSb1jHV781yb2XB2THIPDicKBgQEZGofWfNNl7adSzovoPVDbyw82jQfbGcv1+Ym05NYyu1jS0snZXPSysuGbJrCPQXBQMiIhKVo3VN/OjZnWw6UBV0/2/X7edDD7xCU2v3KX6xnL9/2Rn5/OzDc0lOTMDMKMhJH5IrC/Yn/bZERCQqG9/2Fic6p8vgQb8JozJ9lMQ7AAAgAElEQVRwDg5Wdc81EMv5+1Pyslg6axypSQksf/R1nt8RvDVCQlMwICIiEVtdWsYXfXP5/+Xh0qAL9/hzDQSbUbCspIivL5vVsd2X+futbe38c3sFz247wqMbDoSd20BO0mwCEZFhJHA6X2FuOsuXFkf8AO46E6C8upEVqzYDdLpWb4mHZuR7/fr3vr+Ed0eRbMjPzPj4r16lyHc/DR6MnFoGRESGCf9DvMw3r7+sqiGq5Xh7Wskv0LgRaSQlWMjEQxv3+boZJuYG3R+uxAQjf0Qabx+rJyUxgRn5kScuGu4UDIiIDBPhPsR7E+5MgKTEBB746HxunD8x6PEJBiUTczutNhgt/zVmFmSTkqRHW6T0GxMRGSZiNZ0vkpkAF83IY8KojKDHf/j8yfzxMwsxs4ju39Xq0rKOFRN3HamLuKVDFAyIiAwbsZrOd9vFU7uVhZoJsO1QLb/fcKBbeXu7wzkX0X2D6Tp+4URzW1RdH8NdvwYDZpZvZmt9n4vM7ICZPef7yfOVP2BmL5nZlwPOi7pMRESCW760mIQuL+HRTOdrbGkHYGx2aq8r+a158xD/9ujr3ZYTfmrLYc775tPsPFIX0b27ilXXx3DXb7MJzGwk8CCQ6Ss6D/iGc+6+gGOuBRKdc+eb2S/MbDowO9oy51xkC2GLiAwj75pTyB1/eJ2mVu+NvCg3jeVLZ0Y0m8A5x29f3cec8Tn86bOLej3eP6OgvKqBKXlZHeUb91VRWd/csT9ascxkOJz1Z8tAG/A+oMa3vQC42cw2mtk3fWVLgEd8n58AFvWxrBMzu8XM1pvZ+oqKilh8JxGRU9ab5dU0tTpKfKP3V9+2KOJphRv3VbH9cB03nht8UGBX4zuWMu78cN64r5IzCnP6vMxwLDMZDmf9Fgw452qcc9UBRX/De4DPB843s7PwWg38HTvHgfw+lnWtw/3OuXnOuXl5eXkx+mYiIqcm/xoCHzxvEgC7KiJvov/tun1kpCTyrjDzAgRLPNTS1s6mA1V9nlIIsc1kOJwNZNKhF51zTQBmVgpMB+oAf/iWhRec9KVMRERC2HP0BFPzMlk0bQwXF+dFlb//pgsms3DaGLJSw3t85I9IIznROiUe2nqwlsaWds6ZGDyNcST8LRt9TaQ03A1kMLDGzN4PVAOXAz/De7tfBLwMzAG2AQf6UCYiIiHcc91ZnGhqJTM1iV9+7NyornFmUU7IZYmDSUww/vK5RZ2a7TNTE/noBZOZP3lUVHXoallJkR7+fTSQwcBXgWeBZuCnzrltZnYQWGtmhcCVeOMKXB/KRESkB5kBb/Rt7Y7ErtMLevC9J7dz+Rn5EQUDADPHdV5OeEpeFl9596wQR8tg6PemdefcEt+/zzrnZjrnznLO/chXVoM3juBl4GLnXHVfyvr7u4iInKoefHEvtz60gdY2b1rgV/78Jpd897mwz3+jrJp7n97BBt9KhZFYt+c4P352Z8f2W+U1tPjqIfFh0PvZnXOVzrlHnHOHYlEmIiLdPfnWYfYeO0GSb5xATnoy+47Xd5v/H8rD6/aRmpTAsrMjb45ft+cYK9dso7GljSO1jVx171oefHFvxNeR/jPowYCIiPSvptY2Xt17nPOnju4omzo2C+dg77ETvZ5f39zKn14r552zC8jJSI74/oHTCze+XQXQMb1R4oOCARGRIe61fVU0tbZz/pSAYCDPywe360jPwcDq0jIuuOcZ6ppa+eeOiqjS/AYuZVy6v5LkRGNWoZYZjicDOYBQREQGwUu7j2EG5512MhiYMsbLBthTroGuef+P1jWzYtVmgIhG7/sXKjpQ2UDp21XMikGyIYkttQyIiAxx+SPSuKakqFMTf3pKIp+6aAqzx4d+Q49V3v+8rFRSEhPYf7yeTWVVMckvILGllgERkSHu/edO5P1B0gevuPL0Hs+LVd7/hARj3ZfeQWZqEhdOz2PsiNSIzpf+p5YBEZEhrKaxhabW4DMG2tod+4/Xh1xKOJZ5/3MzUkhOTGDR9DHMyM+O+HzpXwoGRESGsJ//czdzv/ZU0CmED6/bx+JvP8uhmsag5/7b5TO6lUWb9//rj7/FlBWPc9qdj7PwnmeiGogo/UfBgIjIEPbirmNMG5sVdMDeFN+Mgt0VwWcUlPj69nPSkzGgKDedu6+dHXHq39WlZfzqhb20Oy91bFlVAytWbVZAEEc0ZkBEZIg60dTK6/ur+OSFU4Lun5Z3ckbBwmljuu1/ebe3yuEfPn0+08ZG37S/cs02Wts7d0X4ByJqTYH4oJYBEZEhav3blbS2u075BQLlZaeSlZrEriPBpxe+suc4Y7JSmOoLGqIVq4GI0n8UDIiIxJHVpWUsvOeZPvetry4t47b/3QjAHX/YFPQ6ZsbUvEx2hegmuO6c8dxxxUzMwl/MKJhYDkSU/qFuAhGRONE1yY+/bx0iS/LT9ToHqxtDXuczF08jJTH4e+Gi6d27DqKxfGlxp/pA9AMRpX+oZUBEJE7EKslPJNdZOmscF88c2618y8EaNu6rpL09+LTDSCwrKeLua2dTlJvep4GI0n/UMiAiEidi1bceyXUamtvYXFbNlLxMxmSdTAb087W7eW5bBRu+fGlE9w5lWUmRHv5xTC0DIiJxIlZ966Ey/AW7zv7Kem742Uu8sPNop/JXdh9nwZRRfR4vIKcGBQMiInFi+dJiuj56o+lbnzIms1tZqOtMGp1BgtFpRsH+4/WUVTV0WthIhjYFAyIicWL+aaNwQGqS96d5bHZqxH3r+47V8+reShZPGx1WH31qUiITRmV0mlHgzy+wIMSURBl6NGZARCRO5Gen8vAnF9DY2sbHfvkqX776DN49pzCia9z7zA4SEozv3HA2+SPSwjpnal5Wp6WM1+05zqjMFKaP7Vt+ATl1KBgQEYkTSYkJnD91NI0tbSQY7DhcG/E1Zo7LZsLIaWEHAgBT8zJ5YedR2todiQnG15adyc3H6klI0HiB4ULBgIhIHKhpbOGHT+/gg+dNYvKYTCaPyWR7FMHAzYuDpx7uyfvPncgVZxZ0bKclJ1I8TisLDicaMyAiEgf+sa2Cn6/dw9G6JgBmjM1mx+HgaYKD2V1Rxx9LD9AWRV6AKXlZzJ00ksQE4x/bK/jW37fS0Bx82WMZmtQyICISB57acpjRmSkdKwVeekY+hbnpOOd6nN63urSMlWu2UVbVgOEtTvShBZMjund7u+Mvm8oZPzKDxzeV88Rbh1l+ubIDDicKBkREBllLWzvPbj3C0lnjSPT1018/dzzM7fm8rmmHHfCNx7eSlZoc0QwEM/ivP7/JVbMLeHn3cc47bZTGCwwz6iYQERlkr+45Tk1jK5eekd+pvKWtnbqm1pDnxSp9sZkxZUwmz+84yr7j9covMAwpGBARGWQHqxsZm53K4oCFgVrb2pnz1Sf4ybM7Q54Xy6WBp+Zlse94PaD8AsORggERkUF23dzxvLziHWSknOy5TUpMoCg3ne09DCKM5dLAjQEtDDf/+tWol06WU1OvwYCZZZrZh8zsu2b2YzO708zOHIjKiYgMda1t7QBB++hn5Gez40jo6YXLlxaTnpzYqSya9MWrS8tY8+bhju3yKm/JYwUEw0ePwYCZvR/4KVABfA34IrAauNbMfmpmSk8lItIH9z23i3d897lOb+Z+0/O9pvtQ0/yWlRTxtffM6vPSwCvXbKPZF5T4RTP2QE5dIWcTmNlpQJ5z7sNddm0F7jKz6cAHgPv7sX4iIkPaU1sOk52WTFqXN3yA4vxsnIOdR+qYPT6n2/6jdU18afUb3HPdbK4pGR91HWI59kBOTSFbBpxze5xz9/Zw7rnAI7GvkojI0Le6tIwF33ya1w9Us7uiLmiT/NkTc7n9imJGZ6UEvca6Pcdpam1n0ujuqxRGIpZjD+TU1Fs3QWrA50cDPicBs4Bf9F/VRESGJn9+gEM1jQDUNLYG7aMvyEnnM0umhXwov7z7GBkpicwu6t5qEIlYjT2QU1dvAwjXBHzu6IRyzrU65/4dGNcvtRIRGcIiyQ9QUdvEG2XVQa/z8u5jzJs8iuTEvk0MW1ZSxN3Xzu7z2AM5dfWWgbAl4PMYM/tI4DZQH/sqiYgMbZH00d/12FtsfLuSF+68pFP50bomth+u69NYgUDLSor08B/GegsnA1e8SADSgHTfTy3w6Z5ONrN8M1vr+zzRzJ4zs2fM7H7zFJnZAV/5c2aW5zv2ATN7ycy+HHCtsMpEROJdJH30xflZlFU1cKJLJsIEM5YvLeYdp4/tlzrK8NJbMBA48bUOeBL4jXPuZ865nzvndoQ80Wwk8CDgH9nyKeDTzrlLgAnAbOA84BvOuSW+nwozuxZIdM6dD0wxs+nhlkX+9UVEBt7nLplGUpe8AqH66Kfne0sJ7zjSOfnQqMwUbrt4GjPytdSw9F0kLQPjgO8Bz5nZE2bWyxIatAHvA2oAnHNfcs5t8e0bDRwFFgA3m9lGM/umb98STs5SeAJYFEGZiEjcq29uo7XdkZeV2msfvf9hv/1w5+RDz+84SnVDS7fjRaLR25iB1IDPu51z1wCY2WzgN2b2ZefcX4Kd6Jyr8R3bqdzM3ge86ZwrN7O/4SUzqgeeMrOz8FoS/ENqjwPnRFDW9V63ALcATJw4sZevKiLS/1rb2nng+T3MnzySR2+9oNfjJ47KIDUpgR0BwcDRuiY+9MAr3HHFTD69ZGp/VleGid5aBt4X8LljoqtzbjNwJfBdM+ueKSMEM5sC/BvwBV/Ri865WudcG1AKTMfrjvB3nGX56hhuWSfOufudc/Occ/Py8vLCraaISL/52xuHKKtq4JOLp4R1fGKC8ZMPnsMHzpvUUfbK7uMALJgyql/qKMNPj8GAc+5QwOd5XfaVAxf6HuS98o0heBj4uHPOP09mjZkVmFkGcDnwBrCBk03+c4C9EZSJiMQt5xz/s3Y3p43J5NLT83s/wecdp+dz2piTiYVe3n2MzJREzuxjfgERv966CYLyPbznAy9EcNqdwETgh76ug/8Cvgo8CzQDP3XObTOzg8BaMyvEa31YgDd2IZwyEZG41dDSRmFuOjfOyAu6MFEoh6ob+eeOCq6aXUBWahIvxSi/gIhfr8GAmc1wzm3vUjwF+Dxek/+7ejrfObfE9+8dwB1BDpnZ5fgaM1sCXAZ829+KEG6ZiEi8ykhJ4r4P9Tb2urs3yqq5/febmJqXycRRmew8Usd158Qmv4AIhLGEMfCmmX0usMA594Zz7jq8WQEx55yrdM490qWbIqwyEZF4VFbVwK6Kut4PDKJ4nH9GQR1jslJ44l8v5NpzlCBIYiecYGA7MMHM/uhrkg+kDIQiMqysLi1j4T3PcNqdj7PwnmeCLjAUzI+e2ck7711LbWPk0wGLctNJT05k++FazIwZ+dnkj0iL+DoioYQTDDQ5524H7gF+Z2YrzMw/kiX8Ti8RkVOcf4GhsqoGHN7bfrAFhrqes+Dup3l43T4SzHh6y5GI75uQYEzPz2LH4TpWrtnKi7uO9uFbiHTX26qFCfjGFTjnXgEuBI4Az5jZH4Fj/V5DEZE4EckCQxCwOmG1tzphfXNbr8FDKDPys3lx11F+/OwuNh3QECmJrd5aBj4DfMm/4TwPOOfOAz7lnLuhX2snIhJHIllgCCIPHnpyZtEIstOSAXhg7Z6oAgqRUELOJjBv/l878O9m9g286X/NAYckmFmac+7sfq6jiEhcKMxNpyzIgz/UwkORBg+hrC4t41t/OxlYVNQ1sWLVZgCtNCgxEbJlwNcK8BPfQkBfApKB3wALnXMXOOcWKBAQkeFk+dJiEsNcYAgiW52wJ7FsYRAJJqyMFb71B84HGsM9R0RkqFlWUsSojGRSk7w/g5kpiSEXGAIveEhP7pyxvafgIZRYtTCIhBLywW5mk8xsmX/bOVfvnPuFP/2wmY0xsxsHopIiIvGgvKqBirpmli8tZtG0MUwcndljM/2ykiKm52cyMiO519UJexKrFgaRUHrqJngbmGFm95pZRxhrZhlm9hHgR8A/B6COIiJxISstiW9fdxZLZ41j/uRRbD1UQ00PeQMOVNaz6UANX7h0BnvueScv3HlJVH38sWphEAmlx3TEzrlvm9kk4MNmNhVvPYAG4K/OObUKiMiwMiItmRvmTwBg/uSROAcb3q7k4uKxQY9ft8dbXfDc0/q2uqA/gFi5ZhvlVQ0U5qazfGmxBg9KzPS6NoGvheDrA1AXEZG45Zzj0Q0HWDx9DAU56ZRMHMnvblnAnAm5Ic9Zt+c4OenJFOdn9/n+y0qK9PCXfqPBgCIiYdhVUcftv9/EP7ZVAJCeksh5U0aT1qX5PtAre44zf/KoiFYoFBkMvWYgDEg9HGyfkg6JyLDw/A4vBfDCaWM6yrYfruXbf99KU2tbt+ObWtuYPDqDJcV5A1ZHkWj11k0wGbjezF4FRnbZZ8CHgUf6oV4iInHlhV3HmDgqgwmjMjrKdlec4CfP7eIdp49l7qTO4wJSkxL55cfOHehqikSlt26CVqAN+A9gDnA53viBa4Ez8QYUiogMaa1t7by86xgLp3VetX3eZO8d6dW9ld3OCdZaIBKvesozkIT34F8EFACPA2uBw8A64LGBqKCIyGDbfriO2qbWTl0EAGOyUpkyJpP1e493O+c9P3qBFas2DVQVRfqkt5aBtXjrEQQe57r8KyIypJ1ROIL1X76US2Z2n0I4b/JI1r9dSXv7yT+JVfXNbD1US5GSAskpIuSYAedcq5k9AeQAecAPgXS8VoIC4AN4yxmLiAx5Y7JSg5bPnzyKv79xiEM1jR0ZAf3dBueeNjroOSLxprcBhBOB15xz3+m6w8wS8LoORESGrMaWNj77fxu5efEUFkzp/nB/z9lFXHfO+E7TB1/ZfYyUpATmTMgZyKqKRK2nJYxTgX8HGs3skiCHJABaUFtEhrRX9x7nqS1H+OCCSUH3pyR1721dt/c4JRNySU0KnYNAJJ701E3QBFxpZlOAbwJnAV8AjvkOMSB4u5mIyBDxws5jJCca504OnVL44XX7+Ovmgzz0ifMA+MC5ExmRnjxQVRTps3DSEe8GbjSz64F9zrmt/V8tEZH48OKuo5RMGElmaug/lw3NbazdcZSD1Q0U5KRz47kTB7CGIn0Xdjpi59zvAwMBMzu7f6okIjL4VpeWcf7dT7PpQDVbDtWwujR0r+h8X6vBq3sreaOsmv3H6weqmiIx0WvLAHiDBZ1z7b5Bgw64HygHXuvPyomIDIbVpWWsWLWZhhYvcVBtYysrVm0GCLpY0OkF2WSmJLJ+73EeemkvLW2O1bctHMgqi/RJb2sT+Du9jprZM8AbwHi8pENvm9kH+7l+IiIDbuWabR2BgF9DSxsr12wLenxSYgLnTBrJ8zuO8vr+as7r45LFIgOtt26CX5nZD/GmF14CHHbO7XfO/Rx4Gi9NsYjIkFJe1RBROUB+dipvH6unua2d32840GO3gki86TEYcM59EPhtYJGZjTCzXwDHgYP9WTkRkcFQGCJzYKjy1aVlPL75EG3Oy0J47EQzK1ZtVkAgp4xeBxA6514I2DTgBuAh51wtSkksIkPQbRdP7VaWnpzI8qXFQY+PtFtBJN70NmZggZmtCihywCrgHWb2FaD7fzEiIqe4A5Ved8DY7FQMKMpN5+5rZwcdPAjRdSuIxJPeZhOMAT4JvOHrGhgD1AHPApXAL/q3eiIiA+tIbSO/fGEv7zm7kB/cWBLWOYW56ZQFefCH6lYQiTe9jRl4zDl3DJgH3AFcBmQAy4H/BAr7vYYiIgPo+R1HaWt3/OulM8I+Z/nSYtKTO6ce7qlbQSTehJVnwDnXdRTMFWZWgDeGQEQkbqwuLWPlmm2UVzVQmJvO8qXFIZv3g7n2nPEsnp5HXnb42db91+/LfUUGU7hJh8Y657ouV/weYDde8iERkUHXNVlQWVVDj8mCuvI/yCMJBPyWlRTp4S+nrN4GEI4ws88DvzWzZWZ2k3kSgFrgf81sWg/n55vZWt/nZDP7i5m9YGYf748yERne+jKqf/vhWhZ/+1lWbTzQX9UTiVu9TS28F9gENAGvA9Pxkg1dApwHrHHO7Qx2opmNBB4EMn1FnwM2OOcWAtebWXY/lInIMBbNqP7VpWUsvOcZLv/+P2lvdzQ0t4U8VmSo6i0Y+AFQDeQCZwJHgRbgN8A459yHeji3DXgfUOPbXgI84vv8T7xBibEuE5FhLJpkQStWbe6YCeCArz++RcmCZNjpLRi4CfgUMApvTYLdwC3OuXFAjZndGOpE51yNc646oCgT8P8XdhzI74eyTszsFjNbb2brKyoqevmqInKqW760mKSEzuOalSxIpHe9TS38gnPuVmCXc+4+4F3Ap8zs34DHgOVm1u0hHEId4A/Ps3z3jnVZ1/rf75yb55ybl5eXF2Y1ReRUtaykiIKcNJITvYAgOy1JyYJEwtBrOuIuzsHLQPg68FXgNufc4TDP3QAs8n2eA+zthzIRGcba2x1XzynkG9fM5v3nTgQHV59VEPL4SLsVRIaqsKYWAplmNgOvS+0Q8BZwO7A5gns9CPzVzBYDZwCv4DXzx7JMRIaxhATjjitmApCRksjD6/axqayacyaODHr88qXFnaYigpIFyfAUbsvAbrwMhK8B3wLW+P79lJkl9nSic26J79+38TIYvgBc6pxri3VZBN9bRIag1/ZX0dLWDsAFU8cA8OLOoyGPf/ecQqbkZTAqMyWsNQhEhqpwMxB+rGuZmY3AG1wYNudcOSdnAPRLmYgMT0frmrj2Jy/whUtn8Pl3TGdUZgpnFIzg+Z1H+ewl04Oe80Z5NW+W1/L9983hmpLxA1xjkfgRVsuAmd0SpPgEcLXeyEUkHjz51mHaHVx2xskxzRcV52EYzgVfbf3pLUdIMLhoxtiBqqZIXAp3zMCHgft9XQJ/cc5d5ZxrM7PmfqybiEjY/v7GISaNzmDmuJP5x25fWoxZ6CVUntl6hJKJIxmVmTIQVRSJW+GOGWgF8LUCBA6zDR5ui4gMoOqGFl7cdZQrZo3r9PD3f25v7/6n6nBNI5vLqrlkploFRHpsGfDl/G8F8s3sI3irFAZ+FhEZdM9tO0JLm+PyWeO67bvrL2+x6UAVv//0BZ3KqxtaWDx9DJeeHm6qFJGhq7duglF46YeT8VISW5fPIiKD7uqzCinMTadkQm63fTnpyWzYV0lVfTO5GSe7A2bkZ/PQJ84byGqKxK3eMhB+xzn3A+CAc+5e3+f9AZ9FRAZdYoIxf/IoEhK6v6MsnDYa5+ClXcc6yppb2zlW1zSQVRSJa70tYbzMzL6Jl+4X39LFaQNRMRGRcKzdUcFX/vwm1Q0tQffPmZBLZkoiL+w6mW/gpd3HmPeNp1i/9/hAVVMkrvU2gPAFvCWMD5rZ43jpiBdBR2CgIbgiMqhWbSxj9WtlZKQEz3+WnJjAeVNG8+LOky0Dz2w5TGpSAmcW5QxUNUXiWo9jBpxzFcBvgd+a2TlAlnOu3bev3cz+3wDUUUSkm9WlZXz771spr24kIyWRxzcdDJk58IZ5E9h9tI62dkeCwdNbj7Bo2hjSkntMoCoybISbZwDn3MYgZRtiWx0Rkd6tLi3rtKZAfXMbK1Z5S6UECwiuOPPkLIPth2s5UNnAZ5ZMG5jKipwCwg4GRERiYXVpGSvXbKO8qoHC3HSWLy2OeC2AlWu2dVpcCKChpY2Va7aFvFZ1fQtvHz/BC77ugotnallzET8FAyIyYLq+0ZdVNfT4Rh9KeVVDROUAX/7TG7y8+xh//MwFjMtJpSBHyxSL+IWbgVBEpM96eqOPRGFu8Ad5qHKAjOQEKmqbWPytZ/nOmu2sLi2L6J4iQ5mCAREZMNG80Qdz9ZwCUpI6//lKT05k+dLioMevLi1j9WvlgJdD3d8ioYBAxKNgQEQGTDRv9F3VN7fyp9JyxmalUJSbhgFFuencfe3skF0NK9dso6m1vVNZNC0SIkOVxgyIyIC57eKpfO2xt2hoOflg7umNPpj7ntvFoZpGfn/r+cybPCqsc2LVIiEyVKllQEQGzJo3DzNxVAZFuV4i0+RE6/GNvqt9x+r52T93s+zswrADAYhNi4TIUKaWAREZEM9tO8I/tlfwpatO55MXTuFoXRO56ckkJYb/TvL1x98iKcG488rTI7r38qXFnWYxQOQtEiJDmYIBEel3rW3tfOPxLUwancFHLpgEwJis1LDODcxLMCI9iStm5TMuJ7IlUvwtD33NbyAyVCkYEJF+99tX97PjSB0//dA5pCadTAH89cfeYsKoDG66YHLQ87rmJahuaOVvbxzmwhllET/Il5UU6eEvEoKCARHpF4Fv9EmJxpQxmSydNa7TMRv3VVK6vypkMBBNpkERiZwGEIpIzPnf6MuqGnBAS5ujvKqBP/nm+vvNP20Umw5U0djlge+nWQAiA0PBgIjEXLA3+sbW9m7z+s+dPIqWNsdr+6uCXkezAEQGhoIBEYm5cN/o500ahRms23M86PHLlxaTmGCdyjQLQCT2FAyISMwVhBjt3/WNPicjmQun53V74Pu9a04hGckJpCUlhJVpUESiowGEIhJTLW3tjMxMoby6sVN5qDf6Bz9+bshrVdQ2MS0/m1sWT+HK2QUxr6uIeBQMiEhMfe/J7bxZXsP15xTx0u7jYc/rb2t33VoIxuWk8cfPLMQ519/VFhnWFAyISCeBUwLDeYh3Pf7WJVP49nVnccP8CWHdr66plcu/9w8+vug0bl48paO8qbWNxpZ2ctKTMQvejSAisaExAyLSoeuUwN6W+g12/Dcf39pteeGeZKUmkZyU0G0Q4VNvHWH+N55iy8GaPnwjEQmHggER6dBTkp9YHB/K/MmjeHXv8U7dAatfKyM3PZkZ+dkRXUtEIqdgQEQ6RJrkJ1ZJgc6dPIrK+hZ2HqkDoKq+mee2HeHdcwpDzjQQkdhRMCAiHSJN8lreJvYAACAASURBVBOrpEDzT/OWI1631+sq+OvmQ7S0OU0hFBkgCgZEpMPypcWkJyd2KktLTgiZ5CdWSYEmj87gk4tPY+Y4r0tg9WtlTM3LZFbhiIiuIyLRGbDZBGb2aeB9vs1cYANwGbDbV/Y559xmM/sqcBWwzjl3m+/csMpEpG+6LvVbkJPG7VfMDPmGvqykiP9Zu5udFXU0tbRHvTSwmfGld57Rsf3Na2ZztK5JswhEBsiABQPOufuA+wDM7IfAQ8BR59wd/mPMbC6wCDgX+E8zuxSoDKfMOffUQH0XkaHqld3H+NNrZTxy6/kU+Zr629sdzrmQD+bHPr+Y5tb2iGYQBNPW7th6qIbxuRlMG5vFtLFZfbqeiIRvwLsJzKwIyAfmAVeb2Toze8DMkoCLgD84b0jxGmBxBGUi0ke/e3U/6/dWMiojBYA3y6u56DvPUhpiIaH65laAPgcCAPf9YyfvvPd55tz1BPO+/mTI6YwiEnuDMWbgNrwWgleBS51z5wLJeE3+mYD/L8BxvKAh3LJuzOwWM1tvZusrKir64auIDB01jS389Y2DvOvsQtJTvHEDk0ZncrS2mUfXH+h2/NG6JuZ9/Sn+sKH7vkitLi3jx8/sDLh2c4/5DUQktgY0GDCzBOBi4Dlgk3PuoG/XemA6UAf4hyFn+eoXblk3zrn7nXPznHPz8vLyYvtlRIaYx14/SGNLOzfMO5k5MCs1iSvPHMdjr5fT0Nw5n8Dq0jLqm9s4a3xOn+/t5Sto71QWTb4CEYnOQLcMLAZe8TXvP2Rmc8wsEVgGvI43qHCR79g5wN4IykSkD363fj8z8rOY0+Xhfv288dQ2tbLmzUMdZc45fr/hAHMm5DI9BkmBYpWvQESiM9DBwFLgn77Pd+ENInwNeMk3APB5oMTMfgDcCTwcQZmIRKm93XH5GfncetHUbgMFF5w2mvEj03l0w/6OsjfLa9h6qJbr546Pyf1jla9ARKIzoAsVOef+PeDzG8BZXfa3+2YLvBP4gXNuD0C4ZSISnYQE47aLp4Xcd+eVM0lNOpl/4NH1+0lJSuDdZxXG5P7LlxazYtXmTqmNo8lXICLRseGyNOi8efPc+vXrB7saInGnubWdJ946xKWn55PWJeFQKPuO1bOprIqrYxQMwP9v78zDpKiuPvyeWYBhVxFQBAVE0AgujIiCsriAqHGJWzQx0bh8xmg2jRjjnghqNNEk7iZGk5hEY9S4Y3CLa0BF0YiKiCyiqCwi2yzn++Pchp6hB3urnq7u8z5PPTNTXf2rM7duVZ1777nnZr5aouM4G0dEpqtqbVrHujPgOOVJ4uW7IIzLn7xX3yaJf5qzYOkqHpixkJP36keFrxfgOEVPJs6ApyN2nDIkeenhBHe8MHejU/muf/JdJj38Fv1++hBDL/U8AI5TSrgz4DhlSKqlh1fXNbY4le/eVxZwd1I+gU+/8DwAjlNKuDPgOGVIplP5rnx0Fqs9D4DjlCzuDDhOGZLpVD7PA+A4pY07A45ThqRaqnhjU/k8D4DjlDbuDDhOmbG6roGZC5Zx/kHb06trDQL06lrDpMMHtziVL1PnwXGceFHQpEOO47Q+F//rTe586QNuP3EYz04cm9Z3Ek6C5wFwnNLEnQHHKXGSk/l0bV/NkpV1/N+o/uy9XWaLdx26Sy9/+TtOieLOgOOUMIl8AolphEtW1lEhMKB7h1a2zHGcYsJjBhwnZtz7ygJGTJ5K34kPMmLy1I3O9U+VT6BR4eop70RtpuM4McJ7BhwnRjRv6S9Yuopz73kdIGUXvk8JdBwnHbxnwHFiRKqW/saS//iUQMdx0sGdAceJEZm29M8eN5DqyqaLCvmUQMdxmuPOgOPEiExb+gfvtCVdaqqprpS08gk4jlOeeMyA48SIs8cN5Oy7Z1DXsH7p8ZrqihZb+lPeXMQnK9byu2N35cAhWxTKTMdxYob3DDhOjDhk5y3p3qktVRXW0t+0Qxs2ad+G/XboscGxqsr1T73H1pu1Z/yOPQtvrOM4scGdAceJEctW1dGra3t+cdiOzJl8IDcfX8vCZau55Zk5Gxz70pzPmDFvKSfv1Y/KCkmh5jiOY4iqfvlRJUBtba1Omzattc1wnLygqojYC/60P03nqbcX89TZY9i8U9t1x6yua+C+VxdwyM69aNdsXQHHcUofEZmuqrXpHOs9A44TE+Z9tpKPP18NsM4RAPjJ+EGsrW/kmn+/3eT4dtWVHL1bH3cEHMf5UtwZcJyYcNlD/+PAa/9DXUNjk/19u3XguN37cOdL85i/ZCUAVzzyFn996YPWMNNxnBjiswkcJwbMXryCR95YxOmjt6W6ckMf/ox9BtC2uoKjb3yehUtXo8DeA7pxzLA+hTfWcZzY4T0DjhMDbn76PdpUVvDtEduk/Pw/73zCHc9/wILgCAC8OOezja5b4DiOk8B7BhynSEleeliBEf03o1vHtimPTZWmeE19I1c+OssTDDmO86V4z4DjFCGJBYkWBEcAYNrcJS229H1BIsdxcsGdAccpQjbW0k+FL0jkOE4uuDPgOEVINgsS1TSbQugLEjmOky4eM+A4EZA83r9l1xrOHjcwo7H7Hp3bsWj56g32t9TST2jnck7HccoXdwYcJ88kxvsT3fwLlq7i3HteB0jr5fzhslU0NDZusP/LWvqH7tLLX/6O42SFOwOOk2dSjfevqmvYaGR/ck9CRYVQJXDmPtvyj+kLvKXvOE7kuDPgOHkm0/H+5j0JDY1KVVUF/bp15NmJYyOz03EcJ4EHEDpOnunavjrl/pbG+zOdOeA4jpNv3BlwnDyyfHUddQ2NSLMVg2uqK1oc7/ccAY7jtDbuDDhOHrn28Xf4Ym0DP9x3AL2SegKOqu3d4nh/pj0JjuM4+aZgzoCIVInIByLyZNgGi8jFIvJfEfld0nFZ73Oc1qZnl3acsGdfztxnO56dOJbZl01gQPeOPDf7UxoadYPjl61sqSfBcwQ4jlM4CtkzMAS4U1VHq+pooA0wEhgGfCwi+4rI0Gz3FfD/cJwWOWmvflxw8A7r/q6sEH6w73a88/EKHnht4QbHT37kf6yqa+RH+21Hr641CNCraw2TDh/sMwccxykYhZxNMBw4SETGAK8Ds4B/qKqKyKPAAcCyHPY9XsD/xXGa8MSsj/l8dT0HD9kCadbMP2DHntRuvQnLV9c32T9zwTLufGkep+zdjzPGDuCMsQMKabLjOM46CukM/BfYV1U/FJHbgRrMIQD4DOgB1AOzs9y3ASJyCnAKQJ8+vq67k3/ufWUBVzzyFguXraaqQqivb+Dwob2bHFNRIdz1f3ts4CTssEVnrjpyJw4Y3LOQJjuO42xAIZ2B11R1Tfh9GlCNOQQAHbEhixU57NsAVb0JuAmgtrZ2wwFbx8mB5vkB6huV8+59g4qKig26+EUEVeWyh/7Hg69/yIdLV69LJNS+jaf7cByndSlkzMAdIrKTiFQChwIdsHF/gJ2A94HpOexznLS595UFjJg8lb4TH2TE5KktLg28MTaWaTAVVzwyi5ufmcPCpatR1qcpzubcjuM4+aSQTZJLgL8AAtwP/Bx4RkSuAcaHbS4wKct9jpMWua4dkCDT/AD3z9jwpf9laYodx3EKQcF6BlR1pqoOUdXBqnqeqjYC+wLPAAeo6pxc9hXq/3DiT6Yt+pZoKQ9AS/sXLt1wFULb78mFHMdpXVp1sFJVVwF352uf46RDNhn/khcS6lxTxVG1vTl73MAmPQyw8fwAW3atYUGKc3hyIcdxWhvPQOiUHZt0aJNyf0sv5cSwwoKlq1Bg2ap6fv+f9wGYdPjgtPMDnD1uIDXVlU32eXIhx3GKAQ9jdsqKlWvrWVPXgAho0vySjb2UUw0rNKhy5aOzeHbi2LTH+xPHJXoYfFlix3GKBXcGnLKifZsqbjq+lpkLlnH783NZuHQVXdtXM3ZQ9xZfyvlcSOjQXXr5y99xnKLDhwmcsuGj5RbAN2Lbbpw6qj/PThzLnMkHsue23Xjw9Q9ZtCx1gN+WXdu1sN/H+h3HKQ3cGXBKmuR8Artf9m8uvG/mBsdMHD+IxkZanE1w9rhBPtbvOE5J486AU7I0D/wD+Nu0eRsk+em9aXtOGLkN97wyn5kLlq3bv3TlWr5xy4sM7Nkpo0BBx3GcuOExA07Jkirwb3VdY8okP6eP2Za7ps3n5w++yZ0nD6ehUfneX17hpTmfsXJtg4/1O45T0rgz4JQsmQT+dW5XzYUH78D0uUsYeflUFoQEQcfs1puhW28SqZ2O4zitjTsDTlGSnOQn2yl4mSb5UYW7ps1v0ptw36sLGd5vM+8VcBynpPGYAafoaD7Wn+mCPp+sWMML732acZKffKUpdhzHiRveM+AUHRt7KbfUQk/uSaioENpVVfDfn+27Ti+dHoZ85hNwHMeJE+4MOEVHpi/l5qsQNjQq9Y3KY298lFHgn68d4DhOueLDBE7R0aldah+1pZdyqp6ENfWNGXfv+9oBjuOUK94z4BQV/3nnE5avrqdSoCFp7YCqCmnxpZyv7n1fO8BxnHLFnQGnaFixpp7v//UVtuvRke+M6Mu1U99l4dJV1LSpZOXaBnpvmrpnIJ/d+55PwHGccsSdAadVaT6F8LBdenHc8K3p260DRw/rA5iTMP7XT3PWXa/x0Jl7UdPGuvIbG5XLH3mLE0dswy8fe7vJUIF37zuO46SPxww4WZGc83/E5KlpT/trrtF8CuGfX/yAGfOWNjmuY9sqrjhiCHM++YLLH3lr3f6rpszixqffo7JCPF2w4zhODnjPgJMxzaP3E3kAgIxewJlMIdyzfzd+Mn4gq9Y2MGLy1HXDAnv025Rv7bkNIuIvf8dxnCzxngEnY/KVnCfTwL8tu9RwyzNzmsQHvDJvKfe9ujCj8zqO4zhNcWfAyZh8RO8v+WItbapSV79MphAmFh5yHMdxsseHCZyMySZ6v3mg4MgBm1Hf0Eh1pVCXNIdwY4F/niHQcRwnGrxnoETJR4BfKhYuXZUyOU+bqooWX+KpAgXvf3UhEw/YniuP2CntwL+WnA3PEOg4jpMb3jNQguQrwC+hlWjRd2pXxaq6Bh7+/l5MOnzwuv09u7TjnPGDWtROHWPQyG3Pvc+zE8embdPZ4wY2+b/ApxA6juPkA3cGSpBsFvpJRXOnwjIDCq/PW8ZhQ7dqoqWq/H3aPMYO6k63jm2b6HiGQMdxnOLGnYESJF8v31RORYMqv5zyNocN3arJ/vlLVvGzf85kzKDNueEbQxERAB57Y1GL+p4h0HEcpzhwZ6AE6dapLYs/X7PB/kxfvpk4Fb03bc9Z47bjsofeYtdLp7B0ZR1bdq2hz6Y19Opaw+IVa1hT37jueO/edxzHKR7cGWglmkfX56u7e/rcz1i+ci0CJK3zQ5vKlhf6aW5Pj87tOLJ2q4xnDXTr0JYKgSUr6wCLVfj0izVcesiOVFdWePe+4zhOkeLOQCsQVYDfZh3bsGxVHVtt0p7j99iaW56Zw8Klq6isENpWVTBmYPe07Fm0fDW/nfouxw7rzT2vLEw7YO+qKW/TqE33ra5r5NePv5NRoKDjOI5TWHxqYSuQrwx+zafsfbJiLfUNygkjtuaEEX15duJY5kw+kHtPH8HKukYuffDNtO1R4Mm3P8ko57/nAXAcx4kn3jPQCkQZ4KfAjU/N4fg9+q7bt2OvLpw3YXsG9eyUsT2ZBOzlcylhx3Ecp3C4M1Bg6hoaaVtdweq6xg0+yySD33dH90/54oXUL/cTR653DlR1XbS/nbcdC5auzsieVHgeAMdxnHjizkABWVvfyBl3vszqug3T8AKctFfflN9LFWNw3r0zWzxPSy9xVeWk26fxwnufsnJNAx3aVnHG2G05e9ygvLzEPQ+A4zhOPHFnIGKSW/SJHoGLDt6Bru3brNvfvXNbPvtiLY+98RHf2mMbKiqkiUaq4QCATm0rqW8k7Zf4fa8u5MlZi2kIUX4r1tRz5aOz+OWROzXJKJjLS9zzADiO48SPgjkDItIF+CtQCXwBHA28C7wXDjlDVV8XkYuBCcBLqnp6+G5a+4qN5i36RI9A1/ZtNnhp3vnSB5x7z+vc9tz7Tbr0oeUx/RVrGvjV0Tun/RK/8tFZ6xyBBPWNypWPzvJof8dxnDKmkD0DxwFXq+oUEbkemAjcqarnJA4QkaHASGAYcIGI7AssSWefqj5ewP9lA1LlDbjikbc2aNHXNWjKtMDH7NabDz5bSUOjMmLy1HU6+2zfnc41VSxbVb/BObfsWpNRS9yj/R3HcZxUFGxqoapep6pTwp+bA/XAQSLykojcKiJVwCjgH6qqwKPAXhnsazVSrcp31l0zWLhsw6A8SP3yFREG9ujE1VPebqJz+/NzaVNZQU1100uVzZi+r/rnOI7jpKLgeQZEZA9gE2AKsK+qDgOqsS7/DkBird3PgB4Z7Et1rlNEZJqITFu8eHEE/42Raky/vnn2nSRaevm2FBtQXVXBpMOHpD3fvyVSLT3s0f6O4zhOQQMIRWRT4DfA14BFqppIoD8NGACsABJvyo6Ys5Luvg1Q1ZuAmwBqa2tbfjvnyMa62WuqK9MO8GtJ58Olq/MSmOfR/o7jOE4qCtYzICJtgLuAc1V1LnCHiOwkIpXAocAMYDoWCwCwE/B+BvtaBVWlc011ys8SLfh0W/SF6MY/dJde6zITetCg4ziOA4XtGfgOsCtwnoicBzwB3AEIcL+qPi4iFcAkEbkGGB+2uWnuKwjJgYI9u7SjV9d2LFtVR4XQJC9/ogcgkxa9J+1xHMdxWgOxGLziQURqgAOBl1X1vUz2bYza2lqdNm1aTrY1nyqYYMKOPdlv+x78csrbOXe/R7WaoeM4jlNeiMh0Va1N69hicwaiIh/OwIjJU1OmAO7VtYZnJ47NSdtxHMdx8kkmzoCvWpgBPk/fcRzHKUXcGcgAn6fvOI7jlCLuDGSAz9N3HMdxShFfqCgDfJ6+4ziOU4q4M5Ahviqf4ziOU2r4MIHjOI7jlDnuDDiO4zhOmePOgOM4juOUOe4MOI7jOE6Z486A4ziO45Q57gw4juM4TpnjzoDjOI7jlDnuDDiO4zhOmePOgOM4juOUOe4MOI7jOE6Z486A4ziO45Q57gw4juM4TpnjzoDjOI7jlDnuDDiO4zhOmePOgOM4juOUOe4MOI7jOE6Z486A4ziO45Q57gw4juM4TpnjzoDjOI7jlDnuDDiO4zhOmePOgOM4juOUOe4MOI7jOE6Z486A4ziO45Q57gw4juM4TpnjzoDjOI7jlDnuDDiO4zhOmePOgOM4juOUOe4MOI7jOE6ZE2tnQERuFZHnReRnrW2L4ziO48SV2DoDInI4UKmqewD9RGRAa9vkOI7jOHEkts4AMBr4e/j9MWBk65niOI7jOPGlqrUNyIEOwILw+2fArs0PEJFTgFPCnytEZFYW5+kGfJKVha7v+tHqx9l213f9YtUuBf0EW6d7YJydgRVATfi9Iyl6OVT1JuCmXE4iItNUtTYXDdd3/bhpu77rF7N+nG0vhH42xHmYYDrrhwZ2At5vPVMcx3EcJ77EuWfgXuAZEdkSOAAY3sr2OI7jOE4siW3PgKoux4IIXwDGqOqyiE6V0zCD67t+TLVd3/WLWT/OthdCP2NEVVvbBsdxHMdxWpHY9gw4juM4jpMf3BlwHMdxnDLHnYFWQkQiLXsRkYj1o7Y/1vpRE6X9cS/7uN9bUVOAZ0Ns9eNed3LRj/UDMY6IyA4AqtoYkX7/oB9JMEgB7I+1ftJ5Irnpo7Q/7mVfAvfWVlHoJulHbX9s9Uug7uSs785AARGRYcCNIlLzpQdnpz8cuEpE2kekH7X9sdYP59hJRCSiB1Zk9se97Evg3hoJXBShftT2x1a/BOpOXvR9NkEBEJEfAL2AQcBsYA6wRlVvyJP+N4P+XsDTwEKgXlXvzJN+1PbHWj/pPEOB64FRWPnX5Uk3MvvjXvYlcG9NCPpfA/6MpVZHVR/Mk37U9sdWvwTqTl71vWcgQpK6ilcD2wI/BlYBJwCv5FG/P5Z06UosF/VpwIw86kdtfyz1k85zgYjcCFyIZcL8AXB6rkMFUdof97IvhXtLRNoCBwMHArcD47H/47V86Idfo342xE6/FOpOFPruDESIqqqIjMIyJC7GKt4uwMvAMSKyLjd1pi+ORFd06CIaCkzDrucOwLvAwSKyfbHaXwr6SbwFdAeODdonAjPC+bPWjdL+uJd9nO+tpGGkrwC9gXuAudjD/UNgbxFJe4GZ1rI/rvqlUHei0HdnIELExqIuAH4K1AO/A17Hyn0bYEA4bn8go0UrQoXYGzgPmBT0fgM8D1QDe2IvKERkjGQRnBSl/aWgH767H+YEtAP2wzz1J4ETRGRsInZARCqLyf64l32c762gvx/Wk3QrMAb4NbYUe3vgSMIibCIyMvQgFJv9sdUvgboTib47A9GyPebxdwcqsUo3EPgY+C3QW0QOAi4N+9IiyVsdAawFlmHX8p9Y6+I94DZgkIgcDFwNZNNCjcT+UtEXkZ3Cd88EZgHXYcMEijkHteG4A4EDs2gBR2l/rMs+Kv2o760k/f3Dzw+ALsBzQF9gZjjXYBE5JPxf3YrQ/ljqB+Jed6LR9wDC/CA2P/VI4L+q+l7S/lOx1uLF2MvhTGAp5pFuBtwP/DP5OxvRPwR4XlUXJe2/FOviOgI4Ghv3WoK9kKqB/wJ/VtXZRWB/bPWDVpNZAqHlMAF7kI8idDMCb2DBYF/FPPZzgeNVdU5r2B/3si+Re2tfYJqqfpa0/+fB/q8CJwUbPgQ6AZ8DC4CbVfXdIrA/lvolUnci02+CqvqWhw3zNn8afm4CfDfpszOBa4Cdw9+bAmcAg4GKNPWHYC+VgcDmwLeSPpuMLXyxb/i7P9YNNgioLhL7465fARwFDAl/Jxzpg4DLMWfgECyq9y7gbmyZ7VOBvq1pfwmUfdzvrVrgHGxsuhvwtaTPrgT+AHwde4jXhv9nO6BdkdgfW/0SqDuR6idvPkyQB0SkAxY0NldV/4eN53QVkXYiMlBVr8W8tjPDV2oxr26RppHkQkQ6YS+iN1R1VtDvEfQ7q+pEYD7mJYI9dHoCH2ka09sKYH+s9QP9w9YoIp2Bs0Skrao+gLX+RwHzVXVvzFN/ADhVVW/UjfQIRG1/3Mu+BO6tLlig2otqLfwLgc1FpB2Aqp6NBX5tH/Q2AxqAD1V1dRHYH1v9Eqg7keo3x52B/DAZ89zqReQ+bKrQPOAv2JQPVPUiYLmI/AjrFvyFqi5OU/9yrEtrsYj8Bety/B9wLxZRiqpeEvSPAY4DfqWqS4rE/ljriyUj+R6wRFVnYi2N9kCFiIxW1Xuxh8qp4StjsGDC+UVgf6zLvgD6Ud9bV2JDSe+IyC3AyKD/sIiMCMdcDSAiY4HvADeo6udFYn+c9eNed6LWb4LHDOSIiNyKJQq5HfgbFsTxHPAqNu2jLTYeeBmwEzam84aqrkxT/yZs+stTWHDLTKz7+S3gRSxA5Cisu+tI4G3gTVVdVST2x1o/nOPXQA8sJmAcsBWWXGgY8CdVfTgcdy3wBbAG+OOX9QhEbX/cy74E7q3rgY+C3RcG7enYC+llbG77ftgwwU+AfwOvZ6Aftf2x1S+BuhOpfiqqsv2iA2JzgZ9W1T+KyNVYa/GP2NzhPbGxm7HA9aq6RkTeBMigwu0MzFTVa8ODpQZ4CHvZ9AO6AocBN6pqvYg8bPJpV7io7Y+1fjjH77FgrjOxh/XHwC3YVKQ7gRoRuQp72E/GrsurqrqiNe2Pe9mXwL01GlioqpeKyB+w2SUvYwmFtsRyDJyABQg2iMjNQGMG+lHbH1v9Eqg7keq3eF7vGcgNsfnjt2Ke5RrWz1c9FXuJnKCqb4lIpao2ZKl/HRbcMhf4FPMKTwAWYQExr+eoH7X9sdQXG+8drar3icgkbGzu+8DeWMtiBTbG+DtVfVhsPrhoGmO9BbI/tmVfQP0o760KLH/AtsB/sFkCYDNO5gBnq+r0IrY/tvolUHci1U+FxwzkTm/gQeBn2EV6Guue+hybB7oWIIcLtiswFQse+RSb8jI/6L+LTT/KRT9q+2Orr6rLgAfCeF2noN0N66rbE0sf+/3gCFSo6ppMHIGo7Y9YuxT0I7m3RNbNBx+HdelegA0J/Al4B3MiX8C6rIvO/hLRj2XdKaD+BrgzkAMiIqr6PjaNLJHXuisWTX4yMAWYIFlkn0ugqv/FpqqdiHUxJhY1+RHWuhid9PApKvvjrh9oh5X/+dj47+tYS2MNNszWBbJb+jRK++Ne9nG+t3R9d+tU4K/AN7HcE/2xILWLsbozLFvbo7Q/7vpxrjuF0m/ppL5lsQFV4WdPLCNUD2w85wVgVPjsImBQjvqbY3OO+wF9sO7GvcJn1wADitz+WOo3O4dgiw8dhk0/+hc27nsKlhK0qpjsj3vZl9C9tSk2VXAIFnT6JDAifHY70K/I7Y+dfgnVnUj0N3rufAuWwwZUhp99saxVR4W/uwK7JR2XVuKKL9FfAUwIf28PDA+/C9A2BvbHTr/ZObYCDsWGCfpjOQXGhM++h80PLxr74172JXZvrcKWl63CppvumTgG6BgD+2OlX2J1J+/6X3r+KERLeUu6YFtjeegvilj/4hTHSIzsj5V+s3Nsg43XfSfps12K1f64l32J3VtzgEuSPqtI/hkD+2OlX2J1J+/6adkQpXipbc0u2Dzg/KTPsr7JXb849FOc433gwvC3NDsu4xszSvvjXvau7/rFqF0K+mnbUagTxX1j/VhOVBXC9VtRP+hE+cCKzP64l30J6Ef9soh7+XjdbyX9jGwp5MnisiUuArADNkUlkY+hH9ZazOmCJen1Bzo205+bB/2o7Y+1fgvnzNtNGaX9cS/7Eri3Enq9mv0/ffOsH7X9sdMvoboTiX6uW0FPFpctXKxRwMPA1mFfd2A2cGmuFmoO9wAAFspJREFUFwybJjIm6G+WtG8eSWNFOehHbX/c9RM34S6EVQjD39vm6aESmf0lUPZxv7e6AvsAjwA1YV8fbOnhC2Ngf2z1S6DuRKqf61bwExbzhkVqdsTSzi7E5gnvgU1T2RIYn4cK0QP4J/bSeTJUhvZYetJjctGP2v646yd9d/twUz4GDA77NsGSCV2W7TmitD/uZR/3eyt8b1tsHYG3gv2J4YG9gP+Lgf2x1C+RuhOpfj42T0ccCIkqVEQGA2cBMzCPbS9s8ZCbk46tUtX6LPX3xPLcP49lwdoVuFtVH82TftT2x1I/cQ5smtdUbA7vy1jO8pfDuXZW1efDsRWaQSKhKO2Pe9mX0L11APBjbHnqjphT+XO15XETx2aTOrdQ9sdOv4TqTiT6+cQzEK7nCBFJrCC2HGsF1GNpZ98QkcHhc7K8YONFZC9sudJPsWQk84L+EhH5ioiMzEE/avvjro+a59sHa9ldgy0vuzPwDVVdleQIVGbiCBTA/riXfdzvrd3Elhs+A1sdbjWWZnhPoL2IDBSRxJKy2aSHjdr+OOvHve5ErZ8/WqtLotg2LI/169j40wHYIhevAgdjmcRmYIksBmahXYl1Sc8AZgHHYi3S58O5ugP/xZbZ7F9s9peI/rFY8qCXgWuxDHDHYDnL98ZaeV8v0voT97KP7b2Fde8+Eux/GctE+Sesy3o/YAvgWeAJYMsitD/u+nGuO5E/9/O5lf0SxkldM+9jN/tDWL75IdiLoi/WvfMjLOpzbYb6FUAj5tU+i+XF7gIMDfrtgDuAS7FFcD4vMvtjrZ/E4di65fVYzvLjgEuAc7Dc31OBbUVkhqq+WQz2x73s435vBT7HHur/BB7FAgjPAB7HXkJ/AH6PjWtneu9G/WyIrX7c606B6mZeKdthgjB+DHZRwHI/zwU6YCtdXQg8B/wC6xKciq0atTxD/Ro1N/FpzDvsCvwkbO9jnu6DWOtjGVBXZPbHUj/pPInFSF4E7gG+jQXuDMLKfU/MOfg+5hiktXhJlPbHvezjfm8lnae92hr3zwHTsHzxZ4RtDRY78AfgTmAl5hC0uv1x1o973SlU3YyE1u6aaM0NW2L0fiyorFPYdzhwBdZtPBW7mD/CupQrM9TfB7gl/L5V+Pk9YDKW6/454CWs63Ek0KbI7I+tPuunDyam8OyHtSRGYV1zB2ALmawEbgrH7A1sXiT2x7bsS+TeGo0FB0JY1AZblGpS+P0VrEfpB1ja6nZFZn9s9Uug7kSqH9VWtrMJRGQ0doEuBJYAK1V1qYj0xCKG+wFfYNNAZgKzVfXTDPV/APwUmw5Tp6pfiEhfrFJ3wBYyuS/8fEJVPyky+2OrH86xP9aK+7qqrgj7hgNHYl2+P8J6xx4HFLhK0wwcjNL+uJd9idxbZwFnY2PVqOpaERkI3IK14lZh3bxbAX9U1cVFZn8s9Uuk7kSmHymt7Y0UcgO6hZ+DMc9sANYa/BOwSdJxNVhX8sFA3wz0O4efOyfpjwJuIni44fNNsKjSQ4BNi8j+WOs3O9co4B/YFMLtCC1+rLvu9HDO64HvYPOVv/Q6RGl/3Mu+BO6tduHnUKzlti0wFpt10j7puJ5YT8DhQHUR2R9b/RKoO5HqF2ortwDCA0Xku8ACbAzw95j3ebya9zkMGI9NA+kMHAVMT0dYRKqBo8TmIoN1RSfrfy4iu2EPm1uwKW6HYZGlrW5/KeiLyBbY9J2BwOVYr8BWwHeB08JhXwDXhe0AYKGqLioC+2Nd9lHqR31viUgN8G0RGRS+Ox1zAuqBE1V1ZdDfWlXvFpGvYHXnSeCzIrA/1vrEu+4U4rlfGFrbGynkBpwMfIx5l7sDbwIPh88GYl3Ho8PfQoZrjgM/Bz7BxoV2B/7XTP/xJP0uJHm9RWJ/3PW/DnwA3IuNz03BUn/2CJ8Pw6YVbha224DexWB/CZR93O+t27GUwpsB+wf7/xI+G4SNU48Kf/cmg9iSAtkfW/0SqDuR6hdqa3UDCvJPrg8m64lFjh+BRZa3w8aNn8Aig4fmeJ4dgJuBg8J52uZDv4D2R6LP+gVGegJ3RWF/eEjsh0UW14Sbcgbwj/D5Ttj0pDFJ36nJonyisr8nNqyR77KPtO4UUD/qe2sfrDdpP+DvWP6A72NTCh8AanOs+1HZH2v9ONedpPNEql+orSymFmq4YlgX06ZYFPkHqroaa0VuiY0LTocm00PSJswrTaQoPRRYpKprsIdJVvqJ45Lsbx+F/SLSNvxaE/T3zKc+66frJVrk+dZPlNGzWKviaOB8QtyAiDyEBe/8XFWfSCrXVRmepnO+7E8cIyK1IrI70AYbU8xL2aSoOx2J4Nqm0M9r3UzS74LdW4eRh3srhX4Vlu/+JGCxqn6IRaxvD9Sr6rQs9RMBqR3I47MhicTxeX32JJF4R3TNl35S3e8QdiWey1HWzbw/l8WyBw7AnptRlH1BKfnZBOFBOxh4Bpuv2heL8rwKiyDfHOvmOQzz8A7XDApFRPqp6nvh90pgNyzK9Zqg3y1H/b2x6TMPA09hgU2zsVS6kov9IrIV1iV6fLD3Xqwr/S3gl+GwXPQFG7O/AzhSVReH6/FWnuxP5P1OJO3oigXtTMda/eeGceC7sWt/RKYOQPh+V1V9IfxdC7xDDvUnye7RwK+wqY3XJ5VNzmUfzrMXFgQ3RVWfC2Ovs/KoPxzLEf8ilhVuG6yc86W/M9Yl/5KqfiQiu2LpgK/FErrkem/1UdUPwu9dsd6jl4DfYNe2O3ZfjMZeVmeko590fffAYgsewMaS98CmI+b8bBCR3sGub2CrbL4kltb2VfJXPr2xcfpTVHVJkn7W9ieVzXjgW8DJqroigrq5A7BUVReGv/fAMhn+BmjIxvZm9o/F7t3zVfV+sXTVM3Ipm1Yn166FYt5YH516GtYy/C4wLHy2BzamvEfS8T0y0E44Ug8CZyXtbx9+DscSSmSlH44fgY1Vfg8LPhmV9NkeWKrLXPQPwyrsMVhkfY9m+lmXT7PzzMC617s20388D+U/LmhvGv7eBWthTwR+h03h2Q2b7nMbGUSAB70LEtcXa532z5P9Y7FuxB8CvyVpHDRP13YE9uBOLKzTt5l+TtcWmx/9Apao6Q+Yo5VP+4cl2X87FkE+IHw2HBtHzkVfgL8BpyXt6xh+7o4NKQ0Pf7cHumeoPwbrqTofS108MOmzfDwbjgYuw3Jn/IWmUes5l0/4zmbAG1hAXNdm+llfX+BAzDnqHe7VzqxfATKnusP658LNwEnNr1+utofjx2L3/iXYM6YdUJWva9taW6sbEOk/t35lKLAozqOAi8LFvJAwfpy4kFme4y4sAcm5Sfu2wlqoOekDE7DWCMAJmONRhXmdk4CxOeofjXnmYA/cW8NDbFfg4jzYnxhvvAZ7uN6PdTt2A87Ng/3jsV6AHsDW2NjjJuGzVC/rblmcYwDWK3Ma1kq6DZuOeE629ocH4LXA7uHvs5PKuke4trmW/QHAj8PvPwi2j8Va8hfkQf8oLNIeLG/D85iz1AkL3sxVfyRwY/h9ODYz5CTMsbsu17oTvns3luHup0n7+mKxG2Oy1U0qn6PC72dhL6cKbOnam/NQ978DHBp+/zPmGAwB+gM35lr+Sef5HeYMPABUh/r5q1zsB07EXqQnYA7xI1jw76Bc6z7rnYGR2DPtOKwhdWm4Jrna3ivoJhqVPwH6hN/z8txvra2khgmSunA2x6Z2dMReOveq6vNiU88OxpKG/FNVl2ap3w3rgqoP3VHvYQtQvKKqk8OxW6nq/Bzt3xWb//y0iHQCrlPVb4ZjB6rqrBz1e2DZr+aIyI8xD7cL9mB/XFUzzcedXD5LNKzgJiLHYUMbm2Av0YXAD1X1o0z0U5zvcCxo50VsqGM+5hw8gbWMX1PVqZLmsrKp7A9DKc8B38SGf4ZgY4NXaehiTtPWhPamqvqZiHRU6x6tCLb3UdVLwrHdNMNEJM3rJtYrdgj2cl6Dda9vh0Vt36GZJ3BqUjbYPPsDsd62HbH77GpVfUZENtcMkvC0cL4hmANwvqouCkMEX8WG+17KtG6m0G+DBQU+JyJ/xe7dy8Nn64b+ctA/GvhcVR8SS1Z0tqqeFD7rr6qzc9QfjqXgnY31PoA5rldgiXgyeral0G+jlmhpIubEb4M5Na8Av1SLqchWuz9Wb2qx5/Ew7KV9IzBDM4/lSXWOWsyJH48973fExvDvVNUFOWpvoaofhmHh87HEQr9I/iw361uHkgogDA+rfbEu76uxl2kVMC68PD/EXhwnYuM62erfAPxaLMNdlVrAy4nALiLy03BsRo5ACv0rsQd54qGxAqgRkW4i8musBZCL/lVY0IuGz64KFfoBbDgi44dtqvIRkR7AHCyJyHzMs95VbQw400DHRODO5iJSrar3YC+5E7EXxSWYw7E15jhNDXaltaxsCvvHYemKb8G6qD/FHIJtyHCNhCTtW0XkWmDP8FJoxFp2u4jIUeHYjDOSJenfiNWdTbA5248AL6jqbVi3/r5ksShKM/0rgEVYS+gXmOMF9mAnW0dARHYWke8Fjdcwp+OS8GJ6GRvyO58s87g301+L1R2wVvYuInJe+CwrR6CZ/t9U9aHw0TxsqeMqEbkKi0XIVv+MoP+Cqr6pFqx2iar+DBsSOzRbRyBF+QC8iz1H38Z6TSaEF2Gm926y9mzsOXyVqi5X1cexeIEJ2ToCyfrhHNOwIL7OoS69ifUwZeQEN9M/M2h/KLaQUgN2rw0Qi+0iro4AUDpJh0ILqx1wKvbA+gTrdpqPBXAcKiLLsRfsu9jLNRf9xVjGqV1Do+l1Efk28C8ReU1VH8hB/4Zg/87YWuqPquoroXfgb5j3/FDLamnpL8bG12tF5O/YC2IkNr3n3Uy0U+jfiLVAh4btNSzw8RpsaKISmkT7pkXSC+k0YKGI3Mf6XoZPgU9FZBawt6renSf7h2DRwluF6zsa+DCTB24z7euC9q5Y3XlYVWeIyG8wB2ETVV2Sg+03YA+8REtoOdBTRGZjcRSzkx702ep/hqWFfRhrUU8VkduBi7OxP4l64KciskpVb1XVn4jIr4ArReRB7D5+Mzjfueh/oap/UNVPwkP9i3BtHxBbtTKjezeF/kpV/T2sm6mj2Pj4PcBcVc024Uw9cK6IrFDVPyTt7yMWmT8Ba7lnS8L+FcF5BHNkLsCC/b5GaEBmeu/SrGxU9e8iUi0WoDsXi9N4Is+2fwB8XUTqMYdvYY51Z6KILFfV29R6hatD3XkCGCgi/9E005kXIyXjDISLsFJEpmHdZY9jL9Q9sZfPXKy11A9LcymJrs8c9D/G5iUfJCJLVXWeiBygqhm3XFrQ/yjoHyAib2AZuupV9Uewvus2R/v3x8bSlmFBVe+p6oV50l+Mzd8ehD1EJqnqy0kt/LT1W3CWdsSGOl4WGwJqi/UQZJI5b2P2f4o5SI1Y+azGXkZXZGL/RspmP2CCiCzAWtrvZ/Mi3UjdGYWVzxbYMMcbqnpyJra3oH89VnfaAcODA1YJTMvBEQALWLsL+KqIdFLVX6vqD8WGmXbChj7+JNY925jFCymhf6iIdFDV3yY91Fdne++m0D9EbMXD34aWO6Eh8r6qnhn+Trv8N2Z/2D8Ba+R8pKpX5cH+w8SGsX6rqi+KyHzg/mzv3WbahwTta1W1TizzYA2wXFWvzZPtnYP+z4P+V7ApixdkaXtz/UTZJOrKbCyXQGwdAaA0AgixFtzRWPfbsVhwYCLqexssYn5wRPp9sICpwc2+I3nS7411y+6IpUNNfKcij/ZfAuwYkf19w99fyVS3hXOdg403Cjbk8K2w7zosacxNmf4Padh/SbbXN42yvxwYEmHZX5Ao+wjqTu9Q93O1/yjMaUmsUNcF+BdwZi51JQ397yYdVxmB/cn6e0egf3oL38n0+qZV/pneu+nanmxvVLZHeW1LZYt9zIBYYNF12Bj66Vhyj62Br4XPPmD9wiJR6M/Hxk4PS/6ehpqTB/0F2EvvEFWdG74jmv7qeunY3x/rAozC/rlY4NoRmeg2O8dQETlaRLpj3ZabA/3UAoGewFqov1PVc1T1lPCdinTOlab9/WhWf/KknYihODRT7QxsH0jStc1z3VmA1f182P9DrEt3oKouwyLN9xELYMuKNPTHicg5weZ1cSV5tH+ciJwbNJ8O38mkN+zL9PdP6CeT5fX90vLP5N7NxPaEvTnUzbTrTp6v7TnpaMWFWDoDSV1VlVgLcbaqXoaNpy/Bxrw3w8Z3X8C6e++PWP9fEep/gk3NA768QmepH2X5ZKTf7FzpvFCbvPBg4w/EKOtPkdaddfoR1J182D8Jy1WwBjhCRLZVC6I8KexLmyz0M42fyFS/yRh1luWftn4E9qdd/kVYNkVVd4qdWMYMqKqKyCisu/jPwCcisqOqzhSRtdjDai5wJ5bc4hUsAMT1Y6CfaD01fyGJTePcgaYvpBuwqZBpv5CitD/uZV8E+odgUd/vqs1K+BVkFJ/h+q2kH2fbC6Ff9GgRjFVkumGJR57G5qbegAWMncH65A+jsZZ02uNPrl88+kFjFNYa/SqWoGfHsH9bLIjwOKxnay8sn0S7YrA/7mVfBPp7u3489eNseyH0i32LZc8A1mVzv6r+WUTewabgbYutSf4eFuTxqWYf3en6ragvtv73pdgUvwnYTfqO2LSed8USAf0ESyDyTJHZH+uyd33Xz0E/zrYXQr+oiaszsAhbdAIsf/kbqvp9ERmDRT4L8OfEGJAGt871Y6Mf9U0Zpf1xL3vXd32v+9HoFzet3TWRjw0L8OiBde3s7/rx1semsY0Lv7cBbg2/jwnnuBFLMyrkOFUx6vKJW9m7vuvHQbsU9Itti+VsggRiiWjAkgk9BqxU1cfCZxmly3T94tFX1Y9V9dHw+1qgQiyt8dHAPap6qqo+ooFisz9qbdd3/WLWj7PthdAvVuI6TAA0mT72KpZqMuPMfK5fvPpiuQIaWX9T/jv5psz1HFHaH/eyd33XL0btUtAvVkpi1UIR6aKWDCL5BeL6paN/EbYe+XfD33m9KaO0vwTK3vVdv+i0S0G/2CgJZyBB1J6b67eOfqFuyijLJ65l7/quX8zapaBfLJSUM+CUNuVyUzqO4xQadwYcx3Ecp8yJ9WwCx3Ecx3Fyx50Bx3FyRkTaikj/1rbDcZzscGfAcZx8cCzwm40dICKTRKRWRCpEZJSI9BeRkwpkn+M4G8FjBhzHyQkR2Rybkz0bWA5sAXyINTZqVHWMiLQDpmALHQ0Hvo2tDvegqu7fCmY7jpNErJMOOY7TuohINbbc629UdXLY94KqHtTs0FOBp1S1QUROAy5X1ToRmSUie6rqcwU23XGcJLxnwHGcrBGRbYATgc7YYlIAI4DnscbG/cB9wHPArcBTwAmqenz4/mbAPcBBqvp5IW13HGc97gw4jpMzIjIVW8ylPvQMDE/67FigG9Af2B1YAIwFZgC9gTew4YIbC2+54zjgwwSO4+SAiFSGX1O2KsQWffkbsAewKeYErMWWqJ4gIucC01R1SiHsdRwnNe4MOI6TC6cAhwCrgHvFFnUbKCIPhM8rgX8CbwKo6koR2Q2YGT5vC6wsqMWO42yAOwOO42SNql4PXJ+8T0RebB5AKCJ7Y0tRtwEuAs4KH20OLCuAqY7jbAR3BhzHyTcdU+xrC7QBrgL+oqr/E5HbsOWp3y6gbY7jpMADCB3HKRi+2JTjFCfuDDiO4zhOmePpiB3HcRynzHFnwHEcx3HKHHcGHMdxHKfMcWfAcRzHccocdwYcx3Ecp8z5f8Aq4tI6ALLRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12f63a1e940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,7))\n",
    "plt.plot(value[:,0],value[:,2],linestyle='--',marker='o')\n",
    "plt.xlabel('年份')\n",
    "plt.ylabel('生成总值(亿元)')\n",
    "plt.ylim((0,225000))\n",
    "#特意标记某个点\n",
    "# plt.annotate(xy=[value[:,0][36],value[:,2][36]],s='Look')\n",
    "plt.xticks(range(0,70,4),value[range(0,70,4),1],rotation=45)\n",
    "plt.title('00~17国民生成总值折线图')\n",
    "plt.savefig('../00~17国民生成总值折线图.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **plot一次可以接收多组arr，绘制多条折线图**\n",
    "- **定义点线的形状颜色的三个参数可连接起来用一个字符串表示**，如'ro-.','bD--','ys-'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12f633124e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,7))\n",
    "plt.plot(value[:,0],value[:,3],'ro-.',\n",
    "        value[:,0],value[:,4],'bD--',\n",
    "        value[:,0],value[:,5],'ys-')\n",
    "plt.xlabel('年份')\n",
    "plt.ylabel('生产总值（亿元）')\n",
    "plt.ylim((0,125000))\n",
    "plt.xticks(range(0,70,4),value[range(0,70,4),1],rotation=45)\n",
    "plt.title('00~17年各季度国民各产业生产总值')\n",
    "plt.legend(['第一产业','第二产业','第三产业'])\n",
    "plt.savefig('../00~17年各季度国民各产业生产总值折线图.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(15,) (69, 15)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['序号', '时间', '国内生产总值_当季值(亿元)', '第一产业增加值_当季值(亿元)', '第二产业增加值_当季值(亿元)',\n",
       "       '第三产业增加值_当季值(亿元)', '农林牧渔业增加值_当季值(亿元)', '工业增加值_当季值(亿元)',\n",
       "       '建筑业增加值_当季值(亿元)', '批发和零售业增加值_当季值(亿元)', '交通运输、仓储和邮政业增加值_当季值(亿元)',\n",
       "       '住宿和餐饮业增加值_当季值(亿元)', '金融业增加值_当季值(亿元)', '房地产业增加值_当季值(亿元)',\n",
       "       '其他行业增加值_当季值(亿元)'], dtype=object)"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(name.shape,value.shape)\n",
    "name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 子图绘制，细分各产业生产总值\n",
    "- 由name可知value的15列与之相对应"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12f66504518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "#设置显示中文\n",
    "plt.rcParams['font.sans-serif'] = 'SimHei'\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "#画布、子图\n",
    "pl = plt.figure(figsize=(9,12))\n",
    "ax1 = pl.add_subplot(3,1,1)\n",
    "\n",
    "data = np.load('../国民经济核算季度数据.npz')\n",
    "name = data['columns']\n",
    "value = data['values']\n",
    "#散点图\n",
    "plt.scatter(value[:,0],value[:,3],c='b',marker='o')\n",
    "# plt.scatter(value[:,0],value[:,4],c='r',marker='D')\n",
    "plt.plot(value[:,0],value[:,4],'rD-.')\n",
    "plt.scatter(value[:,0],value[:,5],c='k',marker='v')\n",
    "plt.xlabel('年份')\n",
    "plt.ylabel('生产总值（亿元）')\n",
    "plt.xticks(range(0,70,4),value[range(0,70,4),1],rotation=45)\n",
    "plt.title('00~17各产业行业生产总值散点图')\n",
    "plt.legend(['第一产业','第二产业','第三产业'])\n",
    "\n",
    "#散点图2\n",
    "ax2 = pl.add_subplot(2,1,2)\n",
    "plt.scatter(value[:,0],value[:,6],c='r',marker='o')\n",
    "plt.scatter(value[:,0],value[:,7],c='g',marker='p')\n",
    "plt.scatter(value[:,0],value[:,8],c='b',marker='+')\n",
    "plt.scatter(value[:,0],value[:,9],c='m',marker='v')\n",
    "plt.scatter(value[:,0],value[:,10],c='c',marker='*')\n",
    "plt.xlabel('年份')\n",
    "plt.ylabel('生产总值（亿元）')\n",
    "plt.xticks(range(0,70,4),value[range(0,70,4),1],rotation=45)\n",
    "plt.legend(['农林','工业','建筑','批发','交通'])\n",
    "plt.savefig('../00~17年各产业与行业国民生产总值散点图.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 柱状图\n",
    "- 分组数据的分布\n",
    "- 分组数据之间的比较\n",
    "\n",
    "> plt.bar(left,height,width=0.8,bottom=None,hold=None,data=None)\n",
    "\n",
    "凑合看一下，没找到好点的博客[Python绘图问题：Matplotlib中柱状图bar使用](https://blog.csdn.net/liangzuojiayi/article/details/78187704)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12f65d68e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif'] = 'SimHei'\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "data = np.load('../国民经济核算季度数据.npz')\n",
    "name = data['columns']\n",
    "value = data['values']\n",
    "plt.figure(figsize=(8,7))\n",
    "x = range(0,3)\n",
    "x1 = [i+0.35 for i in range(0,3)]\n",
    "plt.bar(x,value[-2,3:6],width=0.35)\n",
    "plt.bar(x1,value[-1,3:6],width=0.35)\n",
    "#为直方图每一直柱注释\n",
    "for i in range(len(x1)):\n",
    "    plt.text(x1[i],value[-1,3:6][i]+500,'%d'%value[-2,3:6][i],ha='center', va= 'bottom')\n",
    "    \n",
    "for x,y in zip(x,value[-2,3:6]):\n",
    "    plt.text(x,y+1000,'%d'%y,ha='center', va= 'bottom')\n",
    "plt.xlabel('产业')\n",
    "plt.ylabel('生产总值')\n",
    "plt.xticks(range(0,3),['第一产业','第二产业','第三产业'])\n",
    "plt.title('2016、2017年生产总值对比')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 饼图\n",
    "- 数据内部占比情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12f65d68630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6))#画布正方形，画出的圆是正圆\n",
    "label = ['第一产业','第二产业','第三产业']\n",
    "explode = [0.01,0.01,.01]\n",
    "plt.pie(value[-1,3:6],explode=explode,labels=label,autopct='%1.1f%%')\n",
    "plt.title('17年第一季度各产业国民生成总值')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Matplotlib整理\n",
    "- plot()画直线，常见的三种样式参数\n",
    "    - 颜色：'bgrcykm'\n",
    "    - 线条：-实线，--虚线，-.点划线，:点线\n",
    "    - 点：'.ov<>sp*h+xD'\n",
    "    - 三个参数可用一个字符串表示\n",
    "- 注释图表：当一个数据点不在预估的范围内，这时要标注它，可以使用`plt.annotate()`\n",
    "> 如plt.annotate(xy=[1,1],s='Look')\n",
    "- scatter()画散点图，前面记得挺清楚\n",
    "- pie()画饼图\n",
    "    - values：一组数据用来测百分比\n",
    "    - colors：可以指定values中的数据的颜色（也表示和values等长）\n",
    "    - labels：标签，标记values中的数据\n",
    "    - explode：凸显值列表，代表values中数据所占的扇形离圆心的距离（默认都是0）\n",
    "    - autopct：常见的是 `%1.1f%%` 将百分比显示到对应扇形图上\n",
    "        - 格式化的形式，1.1f代表保留一位小数的浮点型，后面的%代表以%结尾（同理可用`%d%%`）\n",
    "    - shadow:True/False，是否显示阴影\n",
    "- bar()画柱状图\n",
    "    - left：x轴数据\n",
    "    - height：对应y轴数据\n",
    "    - width：柱的宽度\n",
    "    - color：颜色\n",
    "- plt.text()标注（看示例）\n",
    "- plt.grid()加网格"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12f6517a8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif'] = 'SimHei'\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "data = np.load('../国民经济核算季度数据.npz')\n",
    "name = data['columns']\n",
    "value = data['values']\n",
    "p = plt.figure(figsize=(16,20))\n",
    "ax1 = p.add_subplot(211)\n",
    "x = range(0,len(value),4)\n",
    "y1 = value[x,3]\n",
    "y2 = value[x,4]\n",
    "#facecolor：背景颜色 ，edgecolor：边框颜色\n",
    "plt.bar(x,y2,width=1.25,facecolor='#9999ff',edgecolor='w')\n",
    "plt.bar(x,-y1,width=1.25,facecolor='#ff9999',edgecolor='w')\n",
    "plt.grid()\n",
    "#标注\n",
    "for i,j in zip(x,y1):\n",
    "    #ha水平对齐方式，center水平居中对齐\n",
    "    #va垂直对齐方式，bottom垂直向下对齐，top向上对齐\n",
    "    #这里的i+0.25，-j-3000仅仅是图中的位置，按你看着舒服调动\n",
    "    plt.text(i+0.25,-j-3000,'%d亿'%j,ha='center',va='bottom')\n",
    "    \n",
    "for i,j in zip(x,y2):\n",
    "    plt.text(i+0.25,j+3000,'%d亿'%j,ha='center',va='top')\n",
    "plt.legend(['第二产业','第一产业（刻度负数不用管）'],loc=2)\n",
    "plt.yticks(range(-10000,70000,5000))\n",
    "plt.xticks(x,value[x,1],rotation=45)\n",
    "\n",
    "ax2 = p.add_subplot(212)\n",
    "x1 = range(1,len(value)+1,4)\n",
    "y3 = value[x,5]\n",
    "plt.bar(x,y2,width=1.25,facecolor='#9999ff',edgecolor='w')\n",
    "plt.bar(x1,y3,width=1.25,facecolor='r',edgecolor='w')\n",
    "plt.grid()\n",
    "for i,j in zip(x,y2):\n",
    "    plt.text(i-0.5,j+2000,'%d'%j,ha='center',va='top')\n",
    "for i,j in zip(x1,y3):\n",
    "    plt.text(i+0.5,j+3500,'%d'%j,ha='center',va='top')\n",
    "plt.ylabel('生产总值（亿元）')\n",
    "plt.xlabel('年份')\n",
    "plt.yticks(range(0,120000,5000))\n",
    "plt.xticks(x,value[x,1],rotation=45)\n",
    "plt.legend(['第二产业','第三产业'],loc=2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pandas\n",
    "[【【Python实战】Pandas：让你像写SQL一样做数据分析（一）】](http://www.cnblogs.com/en-heng/p/5630849.html)\n",
    "\n",
    "[【【Python实战】Pandas：让你像写SQL一样做数据分析（二）】](http://www.cnblogs.com/en-heng/p/5686062.html)\n",
    "#### 一、数据读写\n",
    "- 文本文件（csv、txt）\n",
    "- 数据库数据\n",
    "- Excel文件"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 读写数据库（MySQL为例）\n",
    "- 必须要sqlalchemy库配合数据库连接工具（如pymysql）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'sqlalchemy'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-357-172ae2805bcd>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0msqlalchemy\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mcreate_engine\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'sqlalchemy'"
     ]
    }
   ],
   "source": [
    "from sqlalchemy import create_engine"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 读写文本文件\n",
    "- read_table()/read_csv()\n",
    "    - 两者用法相似，参数一样，仅有区别再用前者默认分隔符为制表符Tab键，后者为逗号\n",
    "    - 与我而言，习惯前者读取txt，后者读取csv\n",
    "    - 常用参数列举\n",
    "        - filepath\n",
    "        - sep：分隔符\n",
    "        - header='infer'：接收int，以某行数据为列名，默认infer（自动识别）\n",
    "        - names=None：接收array，指定列名\n",
    "        - index_col：接收int或sequence，前者指定行索引在那一列，后者代表多重索引\n",
    "        - encoding=utf8：可指定编码\n",
    "        - nrows=None：接收int，读取前n行\n",
    "- to_csv()\n",
    "    - path\n",
    "    - sep\n",
    "    - na_rep=''：指定缺失值，默认''\n",
    "    - columns：接收list，指定写入的列名\n",
    "    - header=True：是否写入列名\n",
    "    - index=True：是否写入索引\n",
    "    - encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "path = '../data/meal_order_info.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['info_id', 'emp_id', 'number_consumers', 'mode', 'dining_table_id',\n",
       "       'dining_table_name', 'expenditure', 'dishes_count', 'accounts_payable',\n",
       "       'use_start_time', 'check_closed', 'lock_time', 'cashier_id', 'pc_id',\n",
       "       'order_number', 'org_id', 'print_doc_bill_num', 'lock_table_info',\n",
       "       'order_status', 'phone', 'name'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 363,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#读取菜品订单信息\n",
    "order = pd.read_csv(path,encoding='gbk')\n",
    "# order1 = pd.read_table(path,sep=',',encoding='gbk')\n",
    "order.columns "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 读写Excel\n",
    "- read_excel()\n",
    "    - filepath\n",
    "    - sheetname=0：接收str、int指定Excel中哪张表（0代表第一张表，str就是明确指出表名）\n",
    "    - header：同read_csv\n",
    "    - names：同read_csv\n",
    "    - index_col：同read_csv\n",
    "- to_excel()\n",
    "    - filepath\n",
    "    - sheetname：默认使用Sheet1\n",
    "    - na_rep=''：同to_csv\n",
    "    - header/index/index_label/encoding：同to_csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3611, 19)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "excel_path = '../data/meal_order_detail.xlsx'\n",
    "mealInfo = pd.read_excel(excel_path,sheet_name='meal_order_detail3')\n",
    "# mealInfo = pd.read_excel(excel_path,sheet_name=0)\n",
    "mealInfo.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['detail_id', 'order_id', 'dishes_id', 'logicprn_name',\n",
       "       'parent_class_name', 'dishes_name', 'itemis_add', 'counts', 'amounts',\n",
       "       'cost', 'place_order_time', 'discount_amt', 'discount_reason',\n",
       "       'kick_back', 'add_inprice', 'add_info', 'bar_code', 'picture_file',\n",
       "       'emp_id'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 370,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mealInfo.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 二、基本对象\n",
    "- DataFrame\n",
    "- Series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### DataFrame对象（重点）\n",
    "- 基本属性\n",
    "    - shape：形状\n",
    "    - size：大小（数据个数）\n",
    "    - index：行索引\n",
    "    - columns：列索引\n",
    "    - ndim：维度\n",
    "    - dtypes：类型\n",
    "    - **T：转置**\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 基本增删改查\n",
    "1. 查询（访问数据）\n",
    "- 访问行、列\n",
    "    - df[列名]/df.列名：访问某列，推荐前者\n",
    "    - df[[列1，列2，...]]：访问多列\n",
    "    - df[a:b]：类似list，访问某(多)行\n",
    "    - 综合以上，可访问特定某个数据\n",
    "- 使用loc，iloc查询\n",
    "    - **注：loc针对索引名称，iloc针对索引位置**\n",
    "    - loc[行索引名/条件，列索引名]\n",
    "        - **注：不同于索引位置，对于行索引名称而言如 2:6 是第2到第6行（如果是位置则是2到5行）**\n",
    "    \n",
    "    - iloc[行索引位置，列索引位置]\n",
    "    - loc/iloc对于列都可以使用[列1，列2，...]，对于行也可以类似[0,2,5]\n",
    "        - 如 `mealInfo.loc[2:6,['detail_id', 'order_id','','amounts']]`\n",
    "        - `mealInfo.loc[[0,2],:]/mealInfo.iloc[[0,2],[5,7,8]]`\n",
    "    - loc对于行可以接收条件判断（series），iloc可以变相达到同一效果\n",
    "        - 所谓条件，其实就是利用布尔索引\n",
    "        - 如获取单价超过100的菜品\n",
    "            - 常规：`mealInfo[mealInfo['amounts']>100][['dishes_name','amounts','counts']]`\n",
    "            - loc：`mealInfo.loc[mealInfo['amounts']>100,['dishes_name','amounts','counts']]`\n",
    "            - iloc：`mealInfo.iloc[(mealInfo['amounts']>100).values,[5,8,7]]`\n",
    "        - iloc只接受int，integer/boolean arrdary/list\n",
    "- 常用的head/tail(n=5)查看首尾n条数据\n",
    "- 综合loc和iloc的ix\n",
    "    - ix[行索引的名称/位置/条件，列索引名称/位置]\n",
    "    - ix对于行索引区间一律都是闭区间（对于2:6，loc可以理解为闭区间，iloc为左闭右开）\n",
    "    - 数据量大时，ix切片效率慢\n",
    "- 还是使用loc吧，可读性更好"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dishes_name</th>\n",
       "      <th>counts</th>\n",
       "      <th>amounts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>芝士烩波士顿龙虾</td>\n",
       "      <td>1</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>麻辣小龙虾</td>\n",
       "      <td>1</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  dishes_name  counts  amounts\n",
       "0    芝士烩波士顿龙虾       1      175\n",
       "2       麻辣小龙虾       1       99"
      ]
     },
     "execution_count": 414,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mealInfo.loc[[0,2],:]\n",
    "mealInfo.iloc[[0,2],[5,7,8]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dishes_name</th>\n",
       "      <th>amounts</th>\n",
       "      <th>counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>芝士烩波士顿龙虾</td>\n",
       "      <td>175</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>53度茅台</td>\n",
       "      <td>128</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>倒立蒸梭子蟹</td>\n",
       "      <td>169</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>葱姜炒蟹</td>\n",
       "      <td>109</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>芝士烩波士顿龙虾</td>\n",
       "      <td>175</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>芝士烩波士顿龙虾</td>\n",
       "      <td>175</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>葱姜炒蟹</td>\n",
       "      <td>109</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>52度泸州老窖</td>\n",
       "      <td>159</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>倒立蒸梭子蟹</td>\n",
       "      <td>169</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>芝士烩波士顿龙虾</td>\n",
       "      <td>175</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>芝士烩波士顿龙虾</td>\n",
       "      <td>175</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>倒立蒸梭子蟹</td>\n",
       "      <td>169</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>芝士烩波士顿龙虾</td>\n",
       "      <td>175</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>葱姜炒蟹</td>\n",
       "      <td>109</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>红酒炖羊肉</td>\n",
       "      <td>108</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>52度泸州老窖</td>\n",
       "      <td>159</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>芝士烩波士顿龙虾</td>\n",
       "      <td>175</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>芝士烩波士顿龙虾</td>\n",
       "      <td>175</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>倒立蒸梭子蟹</td>\n",
       "      <td>169</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>芝士烩波士顿龙虾</td>\n",
       "      <td>175</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>倒立蒸梭子蟹</td>\n",
       "      <td>169</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>路易拉菲红酒干红</td>\n",
       "      <td>158</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>芝士烩波士顿龙虾</td>\n",
       "      <td>175</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>红酒炖羊肉</td>\n",
       "      <td>108</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>芝士烩波士顿龙虾</td>\n",
       "      <td>175</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>路易拉菲红酒干红</td>\n",
       "      <td>158</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>百里香奶油烤红酒牛肉</td>\n",
       "      <td>178</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>373</th>\n",
       "      <td>法国波尔多AOC干红葡萄酒原</td>\n",
       "      <td>159</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>380</th>\n",
       "      <td>葱姜炒蟹</td>\n",
       "      <td>109</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>红酒炖羊肉</td>\n",
       "      <td>108</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3180</th>\n",
       "      <td>路易拉菲红酒干红</td>\n",
       "      <td>158</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3189</th>\n",
       "      <td>红酒炖羊肉</td>\n",
       "      <td>108</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3214</th>\n",
       "      <td>路易拉菲红酒干红</td>\n",
       "      <td>158</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3241</th>\n",
       "      <td>芝士烩波士顿龙虾</td>\n",
       "      <td>175</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3244</th>\n",
       "      <td>倒立蒸梭子蟹</td>\n",
       "      <td>169</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3252</th>\n",
       "      <td>红酒炖羊肉</td>\n",
       "      <td>108</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3283</th>\n",
       "      <td>红酒炖羊肉</td>\n",
       "      <td>108</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3308</th>\n",
       "      <td>芝士烩波士顿龙虾</td>\n",
       "      <td>175</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3317</th>\n",
       "      <td>芝士烩波士顿龙虾</td>\n",
       "      <td>175</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3322</th>\n",
       "      <td>倒立蒸梭子蟹</td>\n",
       "      <td>169</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3356</th>\n",
       "      <td>百里香奶油烤红酒牛肉</td>\n",
       "      <td>178</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3360</th>\n",
       "      <td>53度茅台</td>\n",
       "      <td>128</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3369</th>\n",
       "      <td>红酒炖羊肉</td>\n",
       "      <td>108</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3370</th>\n",
       "      <td>葱姜炒蟹</td>\n",
       "      <td>109</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3371</th>\n",
       "      <td>芝士烩波士顿龙虾</td>\n",
       "      <td>175</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3373</th>\n",
       "      <td>53度茅台</td>\n",
       "      <td>128</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3436</th>\n",
       "      <td>红酒炖羊肉</td>\n",
       "      <td>108</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3446</th>\n",
       "      <td>52度泸州老窖</td>\n",
       "      <td>159</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3461</th>\n",
       "      <td>法国波尔多AOC干红葡萄酒原</td>\n",
       "      <td>159</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3494</th>\n",
       "      <td>52度泸州老窖</td>\n",
       "      <td>159</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3504</th>\n",
       "      <td>倒立蒸梭子蟹</td>\n",
       "      <td>169</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3506</th>\n",
       "      <td>红酒炖羊肉</td>\n",
       "      <td>108</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3508</th>\n",
       "      <td>芝士烩波士顿龙虾</td>\n",
       "      <td>175</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3518</th>\n",
       "      <td>倒立蒸梭子蟹</td>\n",
       "      <td>169</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3520</th>\n",
       "      <td>百里香奶油烤红酒牛肉</td>\n",
       "      <td>178</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3543</th>\n",
       "      <td>红酒炖羊肉</td>\n",
       "      <td>108</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3546</th>\n",
       "      <td>倒立蒸梭子蟹</td>\n",
       "      <td>169</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3567</th>\n",
       "      <td>芝士烩波士顿龙虾</td>\n",
       "      <td>175</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3572</th>\n",
       "      <td>倒立蒸梭子蟹</td>\n",
       "      <td>169</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3582</th>\n",
       "      <td>红酒炖羊肉</td>\n",
       "      <td>108</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>207 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         dishes_name  amounts  counts\n",
       "0           芝士烩波士顿龙虾      175       1\n",
       "7              53度茅台      128       1\n",
       "11            倒立蒸梭子蟹      169       1\n",
       "12              葱姜炒蟹      109       1\n",
       "13          芝士烩波士顿龙虾      175       1\n",
       "15          芝士烩波士顿龙虾      175       1\n",
       "30              葱姜炒蟹      109       1\n",
       "37          52度泸州老窖       159       1\n",
       "45            倒立蒸梭子蟹      169       1\n",
       "61          芝士烩波士顿龙虾      175       1\n",
       "108         芝士烩波士顿龙虾      175       1\n",
       "109           倒立蒸梭子蟹      169       1\n",
       "136         芝士烩波士顿龙虾      175       1\n",
       "140             葱姜炒蟹      109       1\n",
       "154            红酒炖羊肉      108       1\n",
       "166         52度泸州老窖       159       1\n",
       "192         芝士烩波士顿龙虾      175       1\n",
       "218         芝士烩波士顿龙虾      175       1\n",
       "219           倒立蒸梭子蟹      169       1\n",
       "222         芝士烩波士顿龙虾      175       1\n",
       "260           倒立蒸梭子蟹      169       1\n",
       "277         路易拉菲红酒干红      158       1\n",
       "278         芝士烩波士顿龙虾      175       1\n",
       "283            红酒炖羊肉      108       1\n",
       "302         芝士烩波士顿龙虾      175       1\n",
       "344         路易拉菲红酒干红      158       1\n",
       "347       百里香奶油烤红酒牛肉      178       1\n",
       "373   法国波尔多AOC干红葡萄酒原      159       1\n",
       "380             葱姜炒蟹      109       1\n",
       "399            红酒炖羊肉      108       1\n",
       "...              ...      ...     ...\n",
       "3180        路易拉菲红酒干红      158       1\n",
       "3189           红酒炖羊肉      108       1\n",
       "3214        路易拉菲红酒干红      158       1\n",
       "3241        芝士烩波士顿龙虾      175       1\n",
       "3244          倒立蒸梭子蟹      169       1\n",
       "3252           红酒炖羊肉      108       1\n",
       "3283           红酒炖羊肉      108       1\n",
       "3308        芝士烩波士顿龙虾      175       1\n",
       "3317        芝士烩波士顿龙虾      175       2\n",
       "3322          倒立蒸梭子蟹      169       4\n",
       "3356      百里香奶油烤红酒牛肉      178       1\n",
       "3360           53度茅台      128       1\n",
       "3369           红酒炖羊肉      108       1\n",
       "3370            葱姜炒蟹      109       1\n",
       "3371        芝士烩波士顿龙虾      175       1\n",
       "3373           53度茅台      128       1\n",
       "3436           红酒炖羊肉      108       1\n",
       "3446        52度泸州老窖       159       1\n",
       "3461  法国波尔多AOC干红葡萄酒原      159       1\n",
       "3494        52度泸州老窖       159       1\n",
       "3504          倒立蒸梭子蟹      169       1\n",
       "3506           红酒炖羊肉      108       1\n",
       "3508        芝士烩波士顿龙虾      175       1\n",
       "3518          倒立蒸梭子蟹      169       1\n",
       "3520      百里香奶油烤红酒牛肉      178       1\n",
       "3543           红酒炖羊肉      108       1\n",
       "3546          倒立蒸梭子蟹      169       1\n",
       "3567        芝士烩波士顿龙虾      175       1\n",
       "3572          倒立蒸梭子蟹      169       1\n",
       "3582           红酒炖羊肉      108       1\n",
       "\n",
       "[207 rows x 3 columns]"
      ]
     },
     "execution_count": 405,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#获取金额超过100的单品\n",
    "mealInfo[mealInfo['amounts']>100][['dishes_name','amounts','counts']]\n",
    "mealInfo.loc[mealInfo['amounts']>100,['dishes_name','amounts','counts']]\n",
    "mealInfo.iloc[(mealInfo['amounts']>100).values,[5,8,7]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 更改数据：就是通过访问数据并重新赋值（是对原数据更改）\n",
    "- 增添数据\n",
    "    - 增添一列：新建一个列索引就OK\n",
    "        - 该列赋定值：mealInfo['pay_time'] = '2018-08-04'\n",
    "        - 不赋定值：给一个对应的Series即可\n",
    "    - 增加一行 [Efficiently add single row to Pandas Series or DataFrame\n",
    "](https://stackoverflow.com/questions/13751926/efficiently-add-single-row-to-pandas-series-or-dataframe)\n",
    "        - 我看到有很多推荐Series转置后再pd.concat()，将两个df链接起来（**不推荐，看到有说数据量大时耗时长**）\n",
    "        - 直接追加写`df.loc[df.shape[0]] = {'列1':value,'列2':value,...}`。**如果columns明确，可以直接[value1,value2,...]代替**\n",
    "- 删除数据\n",
    "    - df.drop(labels,axis=0,level=None,inplace=False,errors='raise')\n",
    "        - axis为0删除行，为1删除列\n",
    "        - labels接收str(索引名)/array，指明删除的行/列\n",
    "        - inplace：是否对原数据修改"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 423,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>detail_id</th>\n",
       "      <th>order_id</th>\n",
       "      <th>dishes_id</th>\n",
       "      <th>logicprn_name</th>\n",
       "      <th>parent_class_name</th>\n",
       "      <th>dishes_name</th>\n",
       "      <th>itemis_add</th>\n",
       "      <th>counts</th>\n",
       "      <th>amounts</th>\n",
       "      <th>cost</th>\n",
       "      <th>place_order_time</th>\n",
       "      <th>discount_amt</th>\n",
       "      <th>discount_reason</th>\n",
       "      <th>kick_back</th>\n",
       "      <th>add_inprice</th>\n",
       "      <th>add_info</th>\n",
       "      <th>bar_code</th>\n",
       "      <th>picture_file</th>\n",
       "      <th>emp_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3085</td>\n",
       "      <td>431</td>\n",
       "      <td>609966</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>芝士烩波士顿龙虾</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>175</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-08-21 11:16:07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>caipu/101001.jpg</td>\n",
       "      <td>1579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3086</td>\n",
       "      <td>431</td>\n",
       "      <td>609942</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>辣炒鱿鱼</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>58</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-08-21 11:17:32</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>caipu/103003.jpg</td>\n",
       "      <td>1579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3088</td>\n",
       "      <td>431</td>\n",
       "      <td>609970</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>麻辣小龙虾</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>99</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-08-21 11:18:49</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>caipu/101006.jpg</td>\n",
       "      <td>1579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3091</td>\n",
       "      <td>431</td>\n",
       "      <td>609956</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>孜然羊排</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>88</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-08-21 11:20:33</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>caipu/202002.jpg</td>\n",
       "      <td>1579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3094</td>\n",
       "      <td>431</td>\n",
       "      <td>609957</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>蒙古烤羊腿\\r\\n\\r\\n\\r\\n</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-08-21 11:20:36</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>caipu/202003.jpg</td>\n",
       "      <td>1579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3099</td>\n",
       "      <td>431</td>\n",
       "      <td>610003</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>蒜香包</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-08-21 11:23:24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>caipu/503002.jpg</td>\n",
       "      <td>1579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3102</td>\n",
       "      <td>431</td>\n",
       "      <td>610027</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>清炒菊花菜</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-08-21 11:25:48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>caipu/304001.jpg</td>\n",
       "      <td>1579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3106</td>\n",
       "      <td>431</td>\n",
       "      <td>610072</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>53度茅台</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>128</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-08-21 11:26:24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>caipu/401005.jpg</td>\n",
       "      <td>1579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3104</td>\n",
       "      <td>431</td>\n",
       "      <td>610011</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>白饭/大碗</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-08-21 11:26:27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>caipu/601005.jpg</td>\n",
       "      <td>1579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3109</td>\n",
       "      <td>431</td>\n",
       "      <td>609992</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>番茄炖牛腩\\r\\n\\r\\n\\r\\n</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-08-21 11:27:14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>caipu/201002.jpg</td>\n",
       "      <td>1579</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   detail_id  order_id  dishes_id  logicprn_name  parent_class_name  \\\n",
       "0       3085       431     609966            NaN                NaN   \n",
       "1       3086       431     609942            NaN                NaN   \n",
       "2       3088       431     609970            NaN                NaN   \n",
       "3       3091       431     609956            NaN                NaN   \n",
       "4       3094       431     609957            NaN                NaN   \n",
       "5       3099       431     610003            NaN                NaN   \n",
       "6       3102       431     610027            NaN                NaN   \n",
       "7       3106       431     610072            NaN                NaN   \n",
       "8       3104       431     610011            NaN                NaN   \n",
       "9       3109       431     609992            NaN                NaN   \n",
       "\n",
       "         dishes_name  itemis_add  counts  amounts  cost    place_order_time  \\\n",
       "0           芝士烩波士顿龙虾           0       1      175   NaN 2016-08-21 11:16:07   \n",
       "1               辣炒鱿鱼           0       1       58   NaN 2016-08-21 11:17:32   \n",
       "2              麻辣小龙虾           0       1       99   NaN 2016-08-21 11:18:49   \n",
       "3               孜然羊排           0       1       88   NaN 2016-08-21 11:20:33   \n",
       "4  蒙古烤羊腿\\r\\n\\r\\n\\r\\n           0       1       48   NaN 2016-08-21 11:20:36   \n",
       "5                蒜香包           0       1       13   NaN 2016-08-21 11:23:24   \n",
       "6              清炒菊花菜           0       1       30   NaN 2016-08-21 11:25:48   \n",
       "7              53度茅台           0       1      128   NaN 2016-08-21 11:26:24   \n",
       "8              白饭/大碗           0       4       10   NaN 2016-08-21 11:26:27   \n",
       "9  番茄炖牛腩\\r\\n\\r\\n\\r\\n           0       1       35   NaN 2016-08-21 11:27:14   \n",
       "\n",
       "   discount_amt  discount_reason  kick_back  add_inprice  add_info  bar_code  \\\n",
       "0           NaN              NaN        NaN            0       NaN       NaN   \n",
       "1           NaN              NaN        NaN            0       NaN       NaN   \n",
       "2           NaN              NaN        NaN            0       NaN       NaN   \n",
       "3           NaN              NaN        NaN            0       NaN       NaN   \n",
       "4           NaN              NaN        NaN            0       NaN       NaN   \n",
       "5           NaN              NaN        NaN            0       NaN       NaN   \n",
       "6           NaN              NaN        NaN            0       NaN       NaN   \n",
       "7           NaN              NaN        NaN            0       NaN       NaN   \n",
       "8           NaN              NaN        NaN            0       NaN       NaN   \n",
       "9           NaN              NaN        NaN            0       NaN       NaN   \n",
       "\n",
       "       picture_file  emp_id  \n",
       "0  caipu/101001.jpg    1579  \n",
       "1  caipu/103003.jpg    1579  \n",
       "2  caipu/101006.jpg    1579  \n",
       "3  caipu/202002.jpg    1579  \n",
       "4  caipu/202003.jpg    1579  \n",
       "5  caipu/503002.jpg    1579  \n",
       "6  caipu/304001.jpg    1579  \n",
       "7  caipu/401005.jpg    1579  \n",
       "8  caipu/601005.jpg    1579  \n",
       "9  caipu/201002.jpg    1579  "
      ]
     },
     "execution_count": 423,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = mealInfo.head(10).copy()\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**注意Bug**\n",
    "> SettingWithCopyWarning:  A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead\n",
    "\n",
    "报了一个警告，解决：向副本增加新的内容或是删改数据，之前应该将副本拷贝一下（所以前面用来copy方法）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['detail_id', 'order_id', 'dishes_id', 'logicprn_name',\n",
       "       'parent_class_name', 'dishes_name', 'itemis_add', 'counts', 'amounts',\n",
       "       'cost', 'place_order_time', 'discount_amt', 'discount_reason',\n",
       "       'kick_back', 'add_inprice', 'add_info', 'bar_code', 'picture_file',\n",
       "       'emp_id'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 372,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mealInfo.columns\n",
    "#dishes_name菜名，counts菜数量，amounts菜金额，order_id订单号（一个订单可以有多个菜），dishes_id菜品id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>detail_id</th>\n",
       "      <th>order_id</th>\n",
       "      <th>dishes_id</th>\n",
       "      <th>logicprn_name</th>\n",
       "      <th>parent_class_name</th>\n",
       "      <th>dishes_name</th>\n",
       "      <th>itemis_add</th>\n",
       "      <th>counts</th>\n",
       "      <th>amounts</th>\n",
       "      <th>cost</th>\n",
       "      <th>place_order_time</th>\n",
       "      <th>discount_amt</th>\n",
       "      <th>discount_reason</th>\n",
       "      <th>kick_back</th>\n",
       "      <th>add_inprice</th>\n",
       "      <th>add_info</th>\n",
       "      <th>bar_code</th>\n",
       "      <th>picture_file</th>\n",
       "      <th>emp_id</th>\n",
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       "      <td>2016-08-21 11:17:32</td>\n",
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       "      <td>caipu/103003.jpg</td>\n",
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       "      <td>3088</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3091</td>\n",
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       "      <td>609956</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>caipu/202003.jpg</td>\n",
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       "</div>"
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      "text/plain": [
       "   detail_id  order_id  dishes_id  logicprn_name  parent_class_name  \\\n",
       "0       3085       431     609966            NaN                NaN   \n",
       "1       3086       431     609942            NaN                NaN   \n",
       "2       3088       431     609970            NaN                NaN   \n",
       "3       3091       431     609956            NaN                NaN   \n",
       "4       3094       431     609957            NaN                NaN   \n",
       "\n",
       "         dishes_name  itemis_add  counts  amounts  cost    place_order_time  \\\n",
       "0           芝士烩波士顿龙虾           0       1      175   NaN 2016-08-21 11:16:07   \n",
       "1               辣炒鱿鱼           0       1       58   NaN 2016-08-21 11:17:32   \n",
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       "4  蒙古烤羊腿\\r\\n\\r\\n\\r\\n           0       1       48   NaN 2016-08-21 11:20:36   \n",
       "\n",
       "   discount_amt  discount_reason  kick_back  add_inprice  add_info  bar_code  \\\n",
       "0           NaN              NaN        NaN            0       NaN       NaN   \n",
       "1           NaN              NaN        NaN            0       NaN       NaN   \n",
       "2           NaN              NaN        NaN            0       NaN       NaN   \n",
       "3           NaN              NaN        NaN            0       NaN       NaN   \n",
       "4           NaN              NaN        NaN            0       NaN       NaN   \n",
       "\n",
       "       picture_file  emp_id  \n",
       "0  caipu/101001.jpg    1579  \n",
       "1  caipu/103003.jpg    1579  \n",
       "2  caipu/101006.jpg    1579  \n",
       "3  caipu/202002.jpg    1579  \n",
       "4  caipu/202003.jpg    1579  "
      ]
     },
     "execution_count": 400,
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   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 424,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "<p>5 rows × 21 columns</p>\n",
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       "   detail_id  order_id  dishes_id  logicprn_name  parent_class_name  \\\n",
       "0       3085       431     609966            NaN                NaN   \n",
       "1       3086       431     609942            NaN                NaN   \n",
       "2       3088       431     609970            NaN                NaN   \n",
       "3       3091       431     609956            NaN                NaN   \n",
       "4       3094       431     609957            NaN                NaN   \n",
       "\n",
       "         dishes_name  itemis_add  counts  amounts  cost     ...      \\\n",
       "0           芝士烩波士顿龙虾           0       1      175   NaN     ...       \n",
       "1               辣炒鱿鱼           0       1       58   NaN     ...       \n",
       "2              麻辣小龙虾           0       1       99   NaN     ...       \n",
       "3               孜然羊排           0       1       88   NaN     ...       \n",
       "4  蒙古烤羊腿\\r\\n\\r\\n\\r\\n           0       1       48   NaN     ...       \n",
       "\n",
       "  discount_amt  discount_reason  kick_back  add_inprice  add_info  bar_code  \\\n",
       "0          NaN              NaN        NaN            0       NaN       NaN   \n",
       "1          NaN              NaN        NaN            0       NaN       NaN   \n",
       "2          NaN              NaN        NaN            0       NaN       NaN   \n",
       "3          NaN              NaN        NaN            0       NaN       NaN   \n",
       "4          NaN              NaN        NaN            0       NaN       NaN   \n",
       "\n",
       "       picture_file emp_id  pay    pay_time  \n",
       "0  caipu/101001.jpg   1579  175  2018-08-04  \n",
       "1  caipu/103003.jpg   1579   58  2018-08-04  \n",
       "2  caipu/101006.jpg   1579   99  2018-08-04  \n",
       "3  caipu/202002.jpg   1579   88  2018-08-04  \n",
       "4  caipu/202003.jpg   1579   48  2018-08-04  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 424,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#增加\n",
    "df['pay'] = df['counts']*df['amounts']\n",
    "# df\n",
    "df['pay_time'] = '2018-08-04'\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 427,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3086</td>\n",
       "      <td>431</td>\n",
       "      <td>609942</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>辣炒鱿鱼</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>58</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-08-21 11:17:32</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>caipu/103003.jpg</td>\n",
       "      <td>1579</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3088</td>\n",
       "      <td>431</td>\n",
       "      <td>609970</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>麻辣小龙虾</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>99</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-08-21 11:18:49</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>caipu/101006.jpg</td>\n",
       "      <td>1579</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3091</td>\n",
       "      <td>431</td>\n",
       "      <td>609956</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>孜然羊排</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>88</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-08-21 11:20:33</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>caipu/202002.jpg</td>\n",
       "      <td>1579</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3094</td>\n",
       "      <td>431</td>\n",
       "      <td>609957</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>蒙古烤羊腿\\r\\n\\r\\n\\r\\n</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-08-21 11:20:36</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>caipu/202003.jpg</td>\n",
       "      <td>1579</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3099</td>\n",
       "      <td>431</td>\n",
       "      <td>610003</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>蒜香包</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-08-21 11:23:24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>caipu/503002.jpg</td>\n",
       "      <td>1579</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3102</td>\n",
       "      <td>431</td>\n",
       "      <td>610027</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>清炒菊花菜</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-08-21 11:25:48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>caipu/304001.jpg</td>\n",
       "      <td>1579</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3106</td>\n",
       "      <td>431</td>\n",
       "      <td>610072</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>53度茅台</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>128</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-08-21 11:26:24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>caipu/401005.jpg</td>\n",
       "      <td>1579</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3104</td>\n",
       "      <td>431</td>\n",
       "      <td>610011</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>白饭/大碗</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-08-21 11:26:27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>caipu/601005.jpg</td>\n",
       "      <td>1579</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3109</td>\n",
       "      <td>431</td>\n",
       "      <td>609992</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>番茄炖牛腩\\r\\n\\r\\n\\r\\n</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-08-21 11:27:14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>caipu/201002.jpg</td>\n",
       "      <td>1579</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   detail_id  order_id  dishes_id  logicprn_name  parent_class_name  \\\n",
       "0       3085       431     609966            NaN                NaN   \n",
       "1       3086       431     609942            NaN                NaN   \n",
       "2       3088       431     609970            NaN                NaN   \n",
       "3       3091       431     609956            NaN                NaN   \n",
       "4       3094       431     609957            NaN                NaN   \n",
       "5       3099       431     610003            NaN                NaN   \n",
       "6       3102       431     610027            NaN                NaN   \n",
       "7       3106       431     610072            NaN                NaN   \n",
       "8       3104       431     610011            NaN                NaN   \n",
       "9       3109       431     609992            NaN                NaN   \n",
       "\n",
       "         dishes_name  itemis_add  counts  amounts  cost    place_order_time  \\\n",
       "0           芝士烩波士顿龙虾           0       1      175   NaN 2016-08-21 11:16:07   \n",
       "1               辣炒鱿鱼           0       1       58   NaN 2016-08-21 11:17:32   \n",
       "2              麻辣小龙虾           0       1       99   NaN 2016-08-21 11:18:49   \n",
       "3               孜然羊排           0       1       88   NaN 2016-08-21 11:20:33   \n",
       "4  蒙古烤羊腿\\r\\n\\r\\n\\r\\n           0       1       48   NaN 2016-08-21 11:20:36   \n",
       "5                蒜香包           0       1       13   NaN 2016-08-21 11:23:24   \n",
       "6              清炒菊花菜           0       1       30   NaN 2016-08-21 11:25:48   \n",
       "7              53度茅台           0       1      128   NaN 2016-08-21 11:26:24   \n",
       "8              白饭/大碗           0       4       10   NaN 2016-08-21 11:26:27   \n",
       "9  番茄炖牛腩\\r\\n\\r\\n\\r\\n           0       1       35   NaN 2016-08-21 11:27:14   \n",
       "\n",
       "   discount_amt  discount_reason  kick_back  add_inprice  add_info  bar_code  \\\n",
       "0           NaN              NaN        NaN            0       NaN       NaN   \n",
       "1           NaN              NaN        NaN            0       NaN       NaN   \n",
       "2           NaN              NaN        NaN            0       NaN       NaN   \n",
       "3           NaN              NaN        NaN            0       NaN       NaN   \n",
       "4           NaN              NaN        NaN            0       NaN       NaN   \n",
       "5           NaN              NaN        NaN            0       NaN       NaN   \n",
       "6           NaN              NaN        NaN            0       NaN       NaN   \n",
       "7           NaN              NaN        NaN            0       NaN       NaN   \n",
       "8           NaN              NaN        NaN            0       NaN       NaN   \n",
       "9           NaN              NaN        NaN            0       NaN       NaN   \n",
       "\n",
       "       picture_file  emp_id  pay  \n",
       "0  caipu/101001.jpg    1579  175  \n",
       "1  caipu/103003.jpg    1579   58  \n",
       "2  caipu/101006.jpg    1579   99  \n",
       "3  caipu/202002.jpg    1579   88  \n",
       "4  caipu/202003.jpg    1579   48  \n",
       "5  caipu/503002.jpg    1579   13  \n",
       "6  caipu/304001.jpg    1579   30  \n",
       "7  caipu/401005.jpg    1579  128  \n",
       "8  caipu/601005.jpg    1579   40  \n",
       "9  caipu/201002.jpg    1579   35  "
      ]
     },
     "execution_count": 427,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df.head()\n",
    "#删除选定的列\n",
    "df.drop('pay_time',axis=1,inplace=True)\n",
    "# df.head()\n",
    "#删除选定的行\n",
    "# df.drop([0,1,2],axis=0,inplace=True)\n",
    "df\n",
    "#注意，不能重复执行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 428,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>detail_id</th>\n",
       "      <th>order_id</th>\n",
       "      <th>dishes_id</th>\n",
       "      <th>logicprn_name</th>\n",
       "      <th>parent_class_name</th>\n",
       "      <th>dishes_name</th>\n",
       "      <th>itemis_add</th>\n",
       "      <th>counts</th>\n",
       "      <th>amounts</th>\n",
       "      <th>cost</th>\n",
       "      <th>place_order_time</th>\n",
       "      <th>discount_amt</th>\n",
       "      <th>discount_reason</th>\n",
       "      <th>kick_back</th>\n",
       "      <th>add_inprice</th>\n",
       "      <th>add_info</th>\n",
       "      <th>bar_code</th>\n",
       "      <th>picture_file</th>\n",
       "      <th>emp_id</th>\n",
       "      <th>pay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3085</td>\n",
       "      <td>431</td>\n",
       "      <td>609966</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>芝士烩波士顿龙虾</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>175</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-08-21 11:16:07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>caipu/101001.jpg</td>\n",
       "      <td>1579</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3086</td>\n",
       "      <td>431</td>\n",
       "      <td>609942</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>辣炒鱿鱼</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>58</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-08-21 11:17:32</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>caipu/103003.jpg</td>\n",
       "      <td>1579</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3088</td>\n",
       "      <td>431</td>\n",
       "      <td>609970</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>麻辣小龙虾</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>99</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-08-21 11:18:49</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>caipu/101006.jpg</td>\n",
       "      <td>1579</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3091</td>\n",
       "      <td>431</td>\n",
       "      <td>609956</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>孜然羊排</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>88</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-08-21 11:20:33</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>caipu/202002.jpg</td>\n",
       "      <td>1579</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3094</td>\n",
       "      <td>431</td>\n",
       "      <td>609957</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>蒙古烤羊腿\\r\\n\\r\\n\\r\\n</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-08-21 11:20:36</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>caipu/202003.jpg</td>\n",
       "      <td>1579</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3099</td>\n",
       "      <td>431</td>\n",
       "      <td>610003</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>蒜香包</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-08-21 11:23:24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>caipu/503002.jpg</td>\n",
       "      <td>1579</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3102</td>\n",
       "      <td>431</td>\n",
       "      <td>610027</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>清炒菊花菜</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-08-21 11:25:48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>caipu/304001.jpg</td>\n",
       "      <td>1579</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3106</td>\n",
       "      <td>431</td>\n",
       "      <td>610072</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>53度茅台</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>128</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-08-21 11:26:24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>caipu/401005.jpg</td>\n",
       "      <td>1579</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3104</td>\n",
       "      <td>431</td>\n",
       "      <td>610011</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>白饭/大碗</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-08-21 11:26:27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>caipu/601005.jpg</td>\n",
       "      <td>1579</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3109</td>\n",
       "      <td>431</td>\n",
       "      <td>609992</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>番茄炖牛腩\\r\\n\\r\\n\\r\\n</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-08-21 11:27:14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>caipu/201002.jpg</td>\n",
       "      <td>1579</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   detail_id  order_id  dishes_id  logicprn_name  parent_class_name  \\\n",
       "0       3085       431     609966            NaN                NaN   \n",
       "1       3086       431     609942            NaN                NaN   \n",
       "2       3088       431     609970            NaN                NaN   \n",
       "3       3091       431     609956            NaN                NaN   \n",
       "4       3094       431     609957            NaN                NaN   \n",
       "5       3099       431     610003            NaN                NaN   \n",
       "6       3102       431     610027            NaN                NaN   \n",
       "7       3106       431     610072            NaN                NaN   \n",
       "8       3104       431     610011            NaN                NaN   \n",
       "9       3109       431     609992            NaN                NaN   \n",
       "\n",
       "         dishes_name  itemis_add  counts  amounts  cost    place_order_time  \\\n",
       "0           芝士烩波士顿龙虾           0       1      175   NaN 2016-08-21 11:16:07   \n",
       "1               辣炒鱿鱼           0       1       58   NaN 2016-08-21 11:17:32   \n",
       "2              麻辣小龙虾           0       1       99   NaN 2016-08-21 11:18:49   \n",
       "3               孜然羊排           0       1       88   NaN 2016-08-21 11:20:33   \n",
       "4  蒙古烤羊腿\\r\\n\\r\\n\\r\\n           0       1       48   NaN 2016-08-21 11:20:36   \n",
       "5                蒜香包           0       1       13   NaN 2016-08-21 11:23:24   \n",
       "6              清炒菊花菜           0       1       30   NaN 2016-08-21 11:25:48   \n",
       "7              53度茅台           0       1      128   NaN 2016-08-21 11:26:24   \n",
       "8              白饭/大碗           0       4       10   NaN 2016-08-21 11:26:27   \n",
       "9  番茄炖牛腩\\r\\n\\r\\n\\r\\n           0       1       35   NaN 2016-08-21 11:27:14   \n",
       "\n",
       "   discount_amt  discount_reason  kick_back  add_inprice  add_info  bar_code  \\\n",
       "0           NaN              NaN        NaN            0       NaN       NaN   \n",
       "1           NaN              NaN        NaN            0       NaN       NaN   \n",
       "2           NaN              NaN        NaN            0       NaN       NaN   \n",
       "3           NaN              NaN        NaN            0       NaN       NaN   \n",
       "4           NaN              NaN        NaN            0       NaN       NaN   \n",
       "5           NaN              NaN        NaN            0       NaN       NaN   \n",
       "6           NaN              NaN        NaN            0       NaN       NaN   \n",
       "7           NaN              NaN        NaN            0       NaN       NaN   \n",
       "8           NaN              NaN        NaN            0       NaN       NaN   \n",
       "9           NaN              NaN        NaN            0       NaN       NaN   \n",
       "\n",
       "       picture_file  emp_id  pay  \n",
       "0  caipu/101001.jpg    1579  175  \n",
       "1  caipu/103003.jpg    1579   58  \n",
       "2  caipu/101006.jpg    1579   99  \n",
       "3  caipu/202002.jpg    1579   88  \n",
       "4  caipu/202003.jpg    1579   48  \n",
       "5  caipu/503002.jpg    1579   13  \n",
       "6  caipu/304001.jpg    1579   30  \n",
       "7  caipu/401005.jpg    1579  128  \n",
       "8  caipu/601005.jpg    1579   40  \n",
       "9  caipu/201002.jpg    1579   35  "
      ]
     },
     "execution_count": 428,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 429,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df.shape[0]] = [3106,431,610073,np.NaN,np.NaN,'哇哈哈',0,1,5,np.nan,'2016-08-21 11:26:40',np.nan,np.nan,np.nan,0,np.nan,np.nan,'caipu/401006.jpg',1579,5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 431,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>detail_id</th>\n",
       "      <th>order_id</th>\n",
       "      <th>dishes_id</th>\n",
       "      <th>logicprn_name</th>\n",
       "      <th>parent_class_name</th>\n",
       "      <th>dishes_name</th>\n",
       "      <th>itemis_add</th>\n",
       "      <th>counts</th>\n",
       "      <th>amounts</th>\n",
       "      <th>cost</th>\n",
       "      <th>place_order_time</th>\n",
       "      <th>discount_amt</th>\n",
       "      <th>discount_reason</th>\n",
       "      <th>kick_back</th>\n",
       "      <th>add_inprice</th>\n",
       "      <th>add_info</th>\n",
       "      <th>bar_code</th>\n",
       "      <th>picture_file</th>\n",
       "      <th>emp_id</th>\n",
       "      <th>pay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3085</td>\n",
       "      <td>431</td>\n",
       "      <td>609966</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>芝士烩波士顿龙虾</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>175</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-08-21 11:16:07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>caipu/101001.jpg</td>\n",
       "      <td>1579</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3086</td>\n",
       "      <td>431</td>\n",
       "      <td>609942</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>辣炒鱿鱼</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>58</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-08-21 11:17:32</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>caipu/103003.jpg</td>\n",
       "      <td>1579</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3088</td>\n",
       "      <td>431</td>\n",
       "      <td>609970</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>麻辣小龙虾</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>99</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-08-21 11:18:49</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>caipu/101006.jpg</td>\n",
       "      <td>1579</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3091</td>\n",
       "      <td>431</td>\n",
       "      <td>609956</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>孜然羊排</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>88</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-08-21 11:20:33</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>caipu/202002.jpg</td>\n",
       "      <td>1579</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3094</td>\n",
       "      <td>431</td>\n",
       "      <td>609957</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>蒙古烤羊腿\\r\\n\\r\\n\\r\\n</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-08-21 11:20:36</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>caipu/202003.jpg</td>\n",
       "      <td>1579</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3099</td>\n",
       "      <td>431</td>\n",
       "      <td>610003</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>蒜香包</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-08-21 11:23:24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>caipu/503002.jpg</td>\n",
       "      <td>1579</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3102</td>\n",
       "      <td>431</td>\n",
       "      <td>610027</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>清炒菊花菜</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-08-21 11:25:48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>caipu/304001.jpg</td>\n",
       "      <td>1579</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3106</td>\n",
       "      <td>431</td>\n",
       "      <td>610072</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>53度茅台</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>128</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-08-21 11:26:24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>caipu/401005.jpg</td>\n",
       "      <td>1579</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3104</td>\n",
       "      <td>431</td>\n",
       "      <td>610011</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>白饭/大碗</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-08-21 11:26:27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>caipu/601005.jpg</td>\n",
       "      <td>1579</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3109</td>\n",
       "      <td>431</td>\n",
       "      <td>609992</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>番茄炖牛腩\\r\\n\\r\\n\\r\\n</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-08-21 11:27:14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>caipu/201002.jpg</td>\n",
       "      <td>1579</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3106</td>\n",
       "      <td>431</td>\n",
       "      <td>610073</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>哇哈哈</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-08-21 11:26:40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>caipu/401006.jpg</td>\n",
       "      <td>1579</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    detail_id  order_id  dishes_id  logicprn_name  parent_class_name  \\\n",
       "0        3085       431     609966            NaN                NaN   \n",
       "1        3086       431     609942            NaN                NaN   \n",
       "2        3088       431     609970            NaN                NaN   \n",
       "3        3091       431     609956            NaN                NaN   \n",
       "4        3094       431     609957            NaN                NaN   \n",
       "5        3099       431     610003            NaN                NaN   \n",
       "6        3102       431     610027            NaN                NaN   \n",
       "7        3106       431     610072            NaN                NaN   \n",
       "8        3104       431     610011            NaN                NaN   \n",
       "9        3109       431     609992            NaN                NaN   \n",
       "10       3106       431     610073            NaN                NaN   \n",
       "\n",
       "          dishes_name  itemis_add  counts  amounts  cost     place_order_time  \\\n",
       "0            芝士烩波士顿龙虾           0       1      175   NaN  2016-08-21 11:16:07   \n",
       "1                辣炒鱿鱼           0       1       58   NaN  2016-08-21 11:17:32   \n",
       "2               麻辣小龙虾           0       1       99   NaN  2016-08-21 11:18:49   \n",
       "3                孜然羊排           0       1       88   NaN  2016-08-21 11:20:33   \n",
       "4   蒙古烤羊腿\\r\\n\\r\\n\\r\\n           0       1       48   NaN  2016-08-21 11:20:36   \n",
       "5                 蒜香包           0       1       13   NaN  2016-08-21 11:23:24   \n",
       "6               清炒菊花菜           0       1       30   NaN  2016-08-21 11:25:48   \n",
       "7               53度茅台           0       1      128   NaN  2016-08-21 11:26:24   \n",
       "8               白饭/大碗           0       4       10   NaN  2016-08-21 11:26:27   \n",
       "9   番茄炖牛腩\\r\\n\\r\\n\\r\\n           0       1       35   NaN  2016-08-21 11:27:14   \n",
       "10                哇哈哈           0       1        5   NaN  2016-08-21 11:26:40   \n",
       "\n",
       "    discount_amt  discount_reason  kick_back  add_inprice  add_info  bar_code  \\\n",
       "0            NaN              NaN        NaN            0       NaN       NaN   \n",
       "1            NaN              NaN        NaN            0       NaN       NaN   \n",
       "2            NaN              NaN        NaN            0       NaN       NaN   \n",
       "3            NaN              NaN        NaN            0       NaN       NaN   \n",
       "4            NaN              NaN        NaN            0       NaN       NaN   \n",
       "5            NaN              NaN        NaN            0       NaN       NaN   \n",
       "6            NaN              NaN        NaN            0       NaN       NaN   \n",
       "7            NaN              NaN        NaN            0       NaN       NaN   \n",
       "8            NaN              NaN        NaN            0       NaN       NaN   \n",
       "9            NaN              NaN        NaN            0       NaN       NaN   \n",
       "10           NaN              NaN        NaN            0       NaN       NaN   \n",
       "\n",
       "        picture_file  emp_id  pay  \n",
       "0   caipu/101001.jpg    1579  175  \n",
       "1   caipu/103003.jpg    1579   58  \n",
       "2   caipu/101006.jpg    1579   99  \n",
       "3   caipu/202002.jpg    1579   88  \n",
       "4   caipu/202003.jpg    1579   48  \n",
       "5   caipu/503002.jpg    1579   13  \n",
       "6   caipu/304001.jpg    1579   30  \n",
       "7   caipu/401005.jpg    1579  128  \n",
       "8   caipu/601005.jpg    1579   40  \n",
       "9   caipu/201002.jpg    1579   35  \n",
       "10  caipu/401006.jpg    1579    5  "
      ]
     },
     "execution_count": 431,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df.shape[0]\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### DateFrame常用的一些方法\n",
    "- 层次化索引\n",
    "    - set_index([列1，列2，...],drop=True)：将某些列作为行索引来用，并生成新的df。drop=True代表所选的列会删除\n",
    "    - reset_index()：与上面的相反层次化的索引级别会被转到列中\n",
    "- "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>one</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>one</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>one</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>two</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>two</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>two</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>two</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a  b    c  d\n",
       "0  0  7  one  0\n",
       "1  1  6  one  1\n",
       "2  2  5  one  2\n",
       "3  3  4  two  0\n",
       "4  4  3  two  1\n",
       "5  5  2  two  2\n",
       "6  6  1  two  3"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'a': range(7), 'b': range(7, 0, -1),\n",
    " 'c': ['one', 'one', 'one', 'two', 'two', 'two', 'two'],\n",
    " 'd': [0, 1, 2, 0, 1, 2, 3]})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   a  b    c  d\n",
      "0  0  7  one  0\n",
      "1  1  6  one  1\n",
      "2  2  5  one  2\n",
      "3  3  4  two  0\n",
      "4  4  3  two  1\n",
      "5  5  2  two  2\n",
      "6  6  1  two  3\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>a</th>\n",
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       "      <th rowspan=\"3\" valign=\"top\">one</th>\n",
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      "text/plain": [
       "       a  b\n",
       "c   d      \n",
       "one 0  0  7\n",
       "    1  1  6\n",
       "    2  2  5\n",
       "two 0  3  4\n",
       "    1  4  3\n",
       "    2  5  2\n",
       "    3  6  1"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df.set_index(['c','d'])\n",
    "print(df)\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
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       "      <th>b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>one</td>\n",
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       "      <td>7</td>\n",
       "    </tr>\n",
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       "      <th>1</th>\n",
       "      <td>one</td>\n",
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       "      <td>6</td>\n",
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       "      <th>2</th>\n",
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       "      <td>2</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     c  d  a  b\n",
       "0  one  0  0  7\n",
       "1  one  1  1  6\n",
       "2  one  2  2  5\n",
       "3  two  0  3  4\n",
       "4  two  1  4  3\n",
       "5  two  2  5  2\n",
       "6  two  3  6  1"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 对df描述性统计分析\n",
    "- 对数值进行统计【基于numpy实现的，所以以下函数同理可通过np.func()实现】\n",
    "    - df.describe()：描述性统计（常用）\n",
    "    - df.max/min/mean/median/mode()：最大/小值、平均值、中位数、众数\n",
    "    - df.std/var/cov()：标准差、方差、协方差"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 451,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "178 --->\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0    35\n",
       "dtype: int64"
      ]
     },
     "execution_count": 451,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(mealInfo['amounts'].max(),'--->',)\n",
    "# mealInfo[mealInfo['amounts']==mealInfo['amounts'].min()]['dishes_name']\n",
    "a= mealInfo['amounts'].mode()\n",
    "#mode返回的是排好序的DateFrame，因为众数可能出现多个\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 453,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>amounts</th>\n",
       "      <th>counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3611.000000</td>\n",
       "      <td>3611.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>44.748823</td>\n",
       "      <td>1.104403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>35.698775</td>\n",
       "      <td>0.600471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>22.500000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>35.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>56.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>178.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           amounts       counts\n",
       "count  3611.000000  3611.000000\n",
       "mean     44.748823     1.104403\n",
       "std      35.698775     0.600471\n",
       "min       1.000000     1.000000\n",
       "25%      22.500000     1.000000\n",
       "50%      35.000000     1.000000\n",
       "75%      56.000000     1.000000\n",
       "max     178.000000    10.000000"
      ]
     },
     "execution_count": 453,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mealInfo[['amounts','counts']].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 基于类别的描述性统计\n",
    "    - 统计频数value_counts()\n",
    "    - 将目标数据的类型转为category类型，describe支持对category类型数据进行描述性统计\n",
    "        - 利用astype('category')方法转换"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 458,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "白饭/大碗                109\n",
       "凉拌菠菜                  99\n",
       "谷稻小庄                  84\n",
       "麻辣小龙虾                 77\n",
       "辣炒鱿鱼                  70\n",
       "芝士烩波士顿龙虾              70\n",
       "香酥两吃大虾                67\n",
       "水煮鱼                   59\n",
       "白饭/小碗                 58\n",
       "焖猪手                   57\n",
       "五色糯米饭(七色)             55\n",
       "蒙古烤羊腿\\r\\n\\r\\n\\r\\n     49\n",
       "凉拌蒜蓉西兰花               49\n",
       "西瓜胡萝卜沙拉               48\n",
       "清蒸海鱼                  48\n",
       "自制猪肉脯                 46\n",
       "炝炒大白菜                 46\n",
       "美妙绝伦之白莲花              45\n",
       "意文柠檬汁                 44\n",
       "香烤牛排\\r\\n\\r\\n\\r\\n      44\n",
       "油焖麻辣虾                 44\n",
       "番茄炖牛腩\\r\\n\\r\\n\\r\\n     43\n",
       "孜然羊排                  42\n",
       "白胡椒胡萝卜羊肉汤             41\n",
       "爆炒猪肝                  41\n",
       "桂圆肉红豆八宝饭              40\n",
       "剁椒鱼头                  40\n",
       "糖醋蕃茄溜青花               37\n",
       "清蒸蝶鱼                  37\n",
       "芹菜炒腰花                 36\n",
       "                    ... \n",
       " 42度海之蓝                8\n",
       "酸辣汤面                   8\n",
       "特醇嘉士伯啤酒罐装              7\n",
       "牛奶卷                    7\n",
       "咖啡奶香面包                 7\n",
       "快炒黄鳝                   7\n",
       "小炒羊腰\\r\\n\\r\\n\\r\\n       7\n",
       "52度泸州老窖                6\n",
       "长城窖酿解百纳红酒干红葡萄酒         6\n",
       "百威啤酒罐装                 6\n",
       "鲜美鳝鱼                   6\n",
       "广式虾仁肠粉                 6\n",
       "番茄炖牛腩\\r\\n              6\n",
       "凉拌海带豆芽紫菜               5\n",
       "鸡蛋、肉末肠粉                5\n",
       "香烤牛排\\r\\n               5\n",
       "土豆西红柿汤面                5\n",
       "玉竹南北杏鸭腿汤               5\n",
       "宝宝版牛肉肠粉                4\n",
       "三丝鳝鱼                   4\n",
       "党参黄芪炖牛尾\\r\\n            4\n",
       "照烧鸡腿\\r\\n               4\n",
       "芹黄鳝丝                   3\n",
       "红酒土豆烧鸭腿\\r\\n            2\n",
       "小炒羊腰\\r\\n               2\n",
       "百里香奶油烤紅酒牛肉             2\n",
       "独家薄荷鲜虾牛肉卷\\r\\n          2\n",
       "五香酱驴肉\\r\\n              1\n",
       "蒙古烤羊腿\\r\\n              1\n",
       "香辣爆羊肝\\r\\n              1\n",
       "Name: dishes_name, Length: 154, dtype: int64"
      ]
     },
     "execution_count": 458,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mealInfo['dishes_name'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 464,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      3611\n",
       "unique      154\n",
       "top       白饭/大碗\n",
       "freq        109\n",
       "Name: dishes_name, dtype: object"
      ]
     },
     "execution_count": 464,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mealInfo['dishes_name'] = mealInfo['dishes_name'].astype('category')\n",
    "# mealInfo['dishes_name'].dtypes\n",
    "mealInfo['dishes_name'].describe()\n",
    "#count:非空数据数目\n",
    "#unique:有多少类别\n",
    "#top:数目最多的类别\n",
    "#freq:top的次数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 分组groupby\n",
    "- groupby(by=None, axis=0, level=None, as_index=True, sort=True, group_keys=True, squeeze=False, **kwargs)\n",
    "    - by：分组的依据，一般是字段（str），list（多个字段）,DateFrame,Series，函数等\n",
    "    - axis=0：轴向，默认对列进行分组\n",
    "    - as_index：聚合标签是否以DateFrame索引形式输出\n",
    "    - sort：对分组标签是否排序\n",
    "    - group_keys：是否显示分组标签的名称\n",
    "    - squeeze：返回数据是否允许降维\n",
    "    - return：DataFrameGroupBy对象\n",
    "- 分组键中的任何缺失值都会被排除在结果之外\n",
    "- DataFrameGroupBy对象是可迭代的\n",
    "> GroupBy对象是一个迭代对象，每次迭代结果是一个元组，元组的第一个元素是该组的名称(就是groupby的列的元素名称)，第二个元素是该组的具体信息，是一个数据框，索引是以前的数据框的总索引\n",
    ">\n",
    "> 对于多重键的情况，元组的第一个元素将会是由键值组成的元组\n",
    "\n",
    "- DataFrameGroupBy对象的属性、方法\n",
    "    - groups：返回字典，显示分组后每组的组名，以及所对应的索引\n",
    "    - get_group(name)：通过组名获取每组的信息\n",
    "    \n",
    "#### 聚合agg、apply"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.DataFrameGroupBy object at 0x000002020A691BA8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#通过订单号分组\n",
    "mealGroup = mealInfo[['order_id','dishes_name','counts','amounts']].groupby(by='order_id')\n",
    "mealGroup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.DataFrameGroupBy object at 0x000002020BFAB908>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#下面的写法和上面的是等效的\n",
    "mealInfo.groupby(by=[mealInfo['order_id']])[['order_id','dishes_name','counts','amounts']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "163\n",
      "      order_id dishes_name  counts  amounts\n",
      "1286       163       麻辣小龙虾       1       99\n",
      "1287       163       香菇鹌鹑蛋       1       39\n",
      "1288       163        凉拌菠菜       1       27\n",
      "1289       163     哈尔滨啤酒罐装       6        7\n",
      "1290       163       白饭/大碗       1       10\n",
      "******\n",
      "167\n",
      "      order_id  dishes_name  counts  amounts\n",
      "1132       167  党参黄芪炖牛尾\\r\\n       1       35\n",
      "1133       167      三色凉拌手撕兔       1       66\n",
      "1134       167          白斩鸡       1       88\n",
      "1135       167         爆炒鸡翅       1       56\n",
      "1136       167        清炒菊花菜       1       30\n",
      "1137       167         培根花菜       1       35\n",
      "1138       167       北冰洋汽水        4        5\n",
      "1139       167         老式面包       1       19\n",
      "1140       167        白饭/大碗       2       10\n",
      "******\n",
      "168\n",
      "      order_id dishes_name  counts  amounts\n",
      "3317       168    芝士烩波士顿龙虾       2      175\n",
      "3319       168        凉拌菠菜       1       27\n",
      "3321       168     核桃葡萄干土司       1       19\n",
      "3322       168      倒立蒸梭子蟹       4      169\n",
      "3323       168        番茄甘蓝       1       33\n",
      "******\n",
      "176\n",
      "      order_id dishes_name  counts  amounts\n",
      "3124       176    芝士烩波士顿龙虾       1      175\n",
      "3125       176       姜葱炒花蟹       1       45\n",
      "3126       176        剁椒鱼头       1       55\n",
      "3129       176        辣炒田螺       1       35\n",
      "3136       176  不加一滴油的酸奶蛋糕       1        7\n",
      "3137       176        木须豌豆       1       32\n",
      "3141       176        酸辣汤面       1       16\n",
      "******\n",
      "178\n",
      "      order_id  dishes_name  counts  amounts\n",
      "1605       178  党参黄芪炖牛尾\\r\\n       1       35\n",
      "1608       178         辣炒鱿鱼       1       58\n",
      "1609       178     照烧鸡腿\\r\\n       1       16\n",
      "1612       178         青炒扁豆       1       28\n",
      "1614       178        谷稻小庄        1       38\n",
      "1616       178        白饭/大碗       1       10\n",
      "******\n",
      "179\n",
      "      order_id dishes_name  counts  amounts\n",
      "3149       179        番茄蛋汤       1       25\n",
      "3150       179        凉拌菠菜       1       27\n",
      "3151       179       白饭/大碗       4       10\n",
      "3152       179          蛋挞       1        8\n",
      "3153       179        清蒸海鱼       1       78\n",
      "3154       179     重庆特色油烧兔       1       69\n",
      "******\n"
     ]
    }
   ],
   "source": [
    "# 迭代\n",
    "i = 0\n",
    "for name,group in mealGroup:\n",
    "    print(name)\n",
    "    print(group)\n",
    "    i+=1\n",
    "    print(\"******\")\n",
    "    if(i>5):\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(163,       order_id dishes_name  counts  amounts\n",
      "1286       163       麻辣小龙虾       1       99\n",
      "1287       163       香菇鹌鹑蛋       1       39\n",
      "1288       163        凉拌菠菜       1       27\n",
      "1289       163     哈尔滨啤酒罐装       6        7\n",
      "1290       163       白饭/大碗       1       10)\n",
      "<class 'tuple'>\n",
      "(167,       order_id  dishes_name  counts  amounts\n",
      "1132       167  党参黄芪炖牛尾\\r\\n       1       35\n",
      "1133       167      三色凉拌手撕兔       1       66\n",
      "1134       167          白斩鸡       1       88\n",
      "1135       167         爆炒鸡翅       1       56\n",
      "1136       167        清炒菊花菜       1       30\n",
      "1137       167         培根花菜       1       35\n",
      "1138       167       北冰洋汽水        4        5\n",
      "1139       167         老式面包       1       19\n",
      "1140       167        白饭/大碗       2       10)\n",
      "<class 'tuple'>\n",
      "(168,       order_id dishes_name  counts  amounts\n",
      "3317       168    芝士烩波士顿龙虾       2      175\n",
      "3319       168        凉拌菠菜       1       27\n",
      "3321       168     核桃葡萄干土司       1       19\n",
      "3322       168      倒立蒸梭子蟹       4      169\n",
      "3323       168        番茄甘蓝       1       33)\n",
      "<class 'tuple'>\n"
     ]
    }
   ],
   "source": [
    "len(mealGroup)\n",
    "#更清楚地可看到tuple的格式\n",
    "num = 0\n",
    "for i in mealGroup:\n",
    "    print(i)\n",
    "    print(type(i))\n",
    "    num+=1\n",
    "    if(num>2):\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "332 组\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[163, 167, 168, 176, 178, 179, 180, 187, 191, 199]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(len(mealGroup.groups),'组')\n",
    "# mealGroup.groups.keys()#可查看分组的依据\n",
    "list(mealGroup.groups.keys())[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>amounts</th>\n",
       "      <th>counts</th>\n",
       "      <th>dishes_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1286</th>\n",
       "      <td>99</td>\n",
       "      <td>1</td>\n",
       "      <td>麻辣小龙虾</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1287</th>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>香菇鹌鹑蛋</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1288</th>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>凉拌菠菜</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1289</th>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>哈尔滨啤酒罐装</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1290</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>白饭/大碗</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      amounts  counts dishes_name\n",
       "1286       99       1       麻辣小龙虾\n",
       "1287       39       1       香菇鹌鹑蛋\n",
       "1288       27       1        凉拌菠菜\n",
       "1289        7       6     哈尔滨啤酒罐装\n",
       "1290       10       1       白饭/大碗"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看oder_id=163的信息\n",
    "mealGroup.get_group(163)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 通过函数分组\n",
    "不得不说，很抽象，也非常好奇。看个《用Python进行数据分析》上的例子"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\python36\\lib\\site-packages\\ipykernel_launcher.py:4: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Joe</th>\n",
       "      <td>-0.407677</td>\n",
       "      <td>-0.703696</td>\n",
       "      <td>-0.260592</td>\n",
       "      <td>0.717705</td>\n",
       "      <td>0.568029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Steve</th>\n",
       "      <td>0.431345</td>\n",
       "      <td>-0.144505</td>\n",
       "      <td>-0.904492</td>\n",
       "      <td>0.162042</td>\n",
       "      <td>-1.570241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wes</th>\n",
       "      <td>0.134617</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.090503</td>\n",
       "      <td>-0.481605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jim</th>\n",
       "      <td>-0.781967</td>\n",
       "      <td>-0.443579</td>\n",
       "      <td>0.338144</td>\n",
       "      <td>0.881984</td>\n",
       "      <td>0.124704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Travis</th>\n",
       "      <td>0.062606</td>\n",
       "      <td>0.341923</td>\n",
       "      <td>0.262477</td>\n",
       "      <td>-2.217693</td>\n",
       "      <td>1.347879</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               a         b         c         d         e\n",
       "Joe    -0.407677 -0.703696 -0.260592  0.717705  0.568029\n",
       "Steve   0.431345 -0.144505 -0.904492  0.162042 -1.570241\n",
       "Wes     0.134617       NaN       NaN -0.090503 -0.481605\n",
       "Jim    -0.781967 -0.443579  0.338144  0.881984  0.124704\n",
       "Travis  0.062606  0.341923  0.262477 -2.217693  1.347879"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "people = pd.DataFrame(np.random.randn(5, 5),\n",
    "                   columns=['a', 'b', 'c', 'd', 'e'],\n",
    "                   index=['Joe', 'Steve', 'Wes', 'Jim', 'Travis'])\n",
    "people.ix[2:3, ['b', 'c']] = np.nan # 添加几个NA值\n",
    "people"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a  b  c  d  e\n",
       "3  3  2  2  3  3\n",
       "5  1  1  1  1  1\n",
       "6  1  1  1  1  1"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#作为分组键的函数，会在每一个索引名(值)上进行调用，所得结果会作为分组名\n",
    "people.groupby(len).count()\n",
    "#因为默认是axis=0，joe、wes、jim长度都为3就分为一组\n",
    "#感觉会有用处"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### groupby对象的描述统计\n",
    "- count：计算*不包括缺失值*在内的每组的数目\n",
    "- head/head：返回每组的前/后n个值\n",
    "- max/min/mean/median/std/var/size：返回每组的最大/小/平均/中位数/标准差/方差/大小 (像平均值、方差都不包括缺失值)\n",
    "- 等等统计方法都OK\n",
    "\n",
    "搜到个这种问题：[pandas如何获取groupby分组里最大值所在的行](http://www.cnblogs.com/dylan9/p/9226957.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>counts</th>\n",
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       "    <tr>\n",
       "      <th>order_id</th>\n",
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       "  <tbody>\n",
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       "      <th>163</th>\n",
       "      <td>10</td>\n",
       "      <td>182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>13</td>\n",
       "      <td>344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>9</td>\n",
       "      <td>423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>7</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>6</td>\n",
       "      <td>185</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          counts  amounts\n",
       "order_id                 \n",
       "163           10      182\n",
       "167           13      344\n",
       "168            9      423\n",
       "176            7      365\n",
       "178            6      185"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#前5组的每组和\n",
    "mealGroup.sum().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          dishes_name  counts  amounts\n",
       "order_id                              \n",
       "163                 5       5        5\n",
       "167                 9       9        9\n",
       "168                 5       5        5\n",
       "176                 7       7        7\n",
       "178                 6       6        6"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mealGroup.count().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "order_id\n",
       "163    5\n",
       "167    9\n",
       "168    5\n",
       "176    7\n",
       "178    6\n",
       "dtype: int64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mealGroup.size().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>dishes_name</th>\n",
       "      <th>counts</th>\n",
       "      <th>amounts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>431</td>\n",
       "      <td>豌豆薯仔猪骨汤</td>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>431</td>\n",
       "      <td>倒立蒸梭子蟹</td>\n",
       "      <td>1</td>\n",
       "      <td>169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>431</td>\n",
       "      <td>葱姜炒蟹</td>\n",
       "      <td>1</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>879</td>\n",
       "      <td>拌土豆丝</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>879</td>\n",
       "      <td>52度泸州老窖</td>\n",
       "      <td>1</td>\n",
       "      <td>159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>879</td>\n",
       "      <td>爆炒双丝</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>815</td>\n",
       "      <td>炝炒大白菜</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>815</td>\n",
       "      <td>美妙绝伦之白莲花</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>815</td>\n",
       "      <td>凉拌菠菜</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>370</td>\n",
       "      <td>糖醋蕃茄溜青花</td>\n",
       "      <td>1</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>370</td>\n",
       "      <td>蛋挞</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>370</td>\n",
       "      <td>小米南瓜粥</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>425</td>\n",
       "      <td>小米南瓜粥</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>425</td>\n",
       "      <td>黑米恋上葡萄</td>\n",
       "      <td>1</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>784</td>\n",
       "      <td>美妙绝伦之白莲花</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>784</td>\n",
       "      <td>五色糯米饭(七色)</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>425</td>\n",
       "      <td>辣炒鱿鱼</td>\n",
       "      <td>1</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>784</td>\n",
       "      <td>42度海之蓝</td>\n",
       "      <td>1</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>877</td>\n",
       "      <td>白饭/小碗</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>877</td>\n",
       "      <td>谷稻小庄</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>877</td>\n",
       "      <td>木须豌豆</td>\n",
       "      <td>1</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>212</td>\n",
       "      <td>谷稻小庄</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>212</td>\n",
       "      <td>冰糖红豆薏米粥</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>286</td>\n",
       "      <td>啤酒鸭</td>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>212</td>\n",
       "      <td>广式虾仁肠粉</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>286</td>\n",
       "      <td>木须豌豆</td>\n",
       "      <td>1</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>286</td>\n",
       "      <td>白饭/小碗</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>200</td>\n",
       "      <td>油焖麻辣虾</td>\n",
       "      <td>1</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>200</td>\n",
       "      <td>金玉良缘</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>200</td>\n",
       "      <td>超人气广式肠粉</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3487</th>\n",
       "      <td>599</td>\n",
       "      <td>香辣爆羊肝\\r\\n\\r\\n\\r\\n</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3488</th>\n",
       "      <td>599</td>\n",
       "      <td>肉丁茄子</td>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3489</th>\n",
       "      <td>599</td>\n",
       "      <td>辣炒鱿鱼</td>\n",
       "      <td>1</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3502</th>\n",
       "      <td>721</td>\n",
       "      <td>炝炒大白菜</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3503</th>\n",
       "      <td>721</td>\n",
       "      <td>凉拌菠菜</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3505</th>\n",
       "      <td>721</td>\n",
       "      <td>泡椒凤爪\\r\\n\\r\\n\\r\\n</td>\n",
       "      <td>1</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3513</th>\n",
       "      <td>533</td>\n",
       "      <td>黄尾袋鼠西拉子红葡萄酒</td>\n",
       "      <td>1</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3514</th>\n",
       "      <td>533</td>\n",
       "      <td>芝麻烤紫菜</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3515</th>\n",
       "      <td>533</td>\n",
       "      <td>青炒扁豆</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3525</th>\n",
       "      <td>551</td>\n",
       "      <td>香菇鹌鹑蛋</td>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3526</th>\n",
       "      <td>551</td>\n",
       "      <td>白斩鸡</td>\n",
       "      <td>1</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3527</th>\n",
       "      <td>551</td>\n",
       "      <td>超人气广式肠粉</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3541</th>\n",
       "      <td>695</td>\n",
       "      <td>蒜香辣花甲</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3542</th>\n",
       "      <td>695</td>\n",
       "      <td>美妙绝伦之白莲花</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3543</th>\n",
       "      <td>695</td>\n",
       "      <td>红酒炖羊肉</td>\n",
       "      <td>1</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3566</th>\n",
       "      <td>570</td>\n",
       "      <td>蒙古烤羊腿\\r\\n\\r\\n\\r\\n</td>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3568</th>\n",
       "      <td>641</td>\n",
       "      <td>香辣腐乳炒虾</td>\n",
       "      <td>1</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3569</th>\n",
       "      <td>570</td>\n",
       "      <td>香辣爆羊肝\\r\\n\\r\\n\\r\\n</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3570</th>\n",
       "      <td>641</td>\n",
       "      <td>北冰洋汽水</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3572</th>\n",
       "      <td>641</td>\n",
       "      <td>倒立蒸梭子蟹</td>\n",
       "      <td>1</td>\n",
       "      <td>169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3574</th>\n",
       "      <td>570</td>\n",
       "      <td>五色糯米饭(七色)</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3575</th>\n",
       "      <td>692</td>\n",
       "      <td>白斩鸡</td>\n",
       "      <td>1</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3576</th>\n",
       "      <td>692</td>\n",
       "      <td>桂圆肉红豆八宝饭</td>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3577</th>\n",
       "      <td>692</td>\n",
       "      <td>香烤牛排\\r\\n\\r\\n\\r\\n</td>\n",
       "      <td>1</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3604</th>\n",
       "      <td>647</td>\n",
       "      <td>西瓜胡萝卜沙拉</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3606</th>\n",
       "      <td>672</td>\n",
       "      <td>爆炒双丝</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3607</th>\n",
       "      <td>672</td>\n",
       "      <td>小炒羊腰\\r\\n\\r\\n\\r\\n</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3608</th>\n",
       "      <td>647</td>\n",
       "      <td>香菇鹌鹑蛋</td>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3609</th>\n",
       "      <td>647</td>\n",
       "      <td>不加一滴油的酸奶蛋糕</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3610</th>\n",
       "      <td>672</td>\n",
       "      <td>凉拌菠菜</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>994 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      order_id        dishes_name  counts  amounts\n",
       "10         431            豌豆薯仔猪骨汤       1       39\n",
       "11         431             倒立蒸梭子蟹       1      169\n",
       "12         431               葱姜炒蟹       1      109\n",
       "35         879               拌土豆丝       1       25\n",
       "37         879           52度泸州老窖        1      159\n",
       "38         879               爆炒双丝       1       35\n",
       "42         815              炝炒大白菜       1       29\n",
       "44         815           美妙绝伦之白莲花       1       29\n",
       "46         815               凉拌菠菜       1       27\n",
       "53         370            糖醋蕃茄溜青花       1       33\n",
       "55         370                 蛋挞       1        8\n",
       "56         370              小米南瓜粥       1       13\n",
       "59         425              小米南瓜粥       1       13\n",
       "66         425             黑米恋上葡萄       1       33\n",
       "69         784           美妙绝伦之白莲花       1       29\n",
       "70         784          五色糯米饭(七色)       1       35\n",
       "71         425               辣炒鱿鱼       1       58\n",
       "72         784             42度海之蓝       1       99\n",
       "76         877              白饭/小碗       3        1\n",
       "77         877              谷稻小庄        1       38\n",
       "78         877               木须豌豆       1       32\n",
       "81         212              谷稻小庄        1       38\n",
       "82         212            冰糖红豆薏米粥       1       15\n",
       "84         286                啤酒鸭       1       48\n",
       "85         212             广式虾仁肠粉       1       18\n",
       "86         286               木须豌豆       1       32\n",
       "87         286              白饭/小碗       2        1\n",
       "88         200              油焖麻辣虾       1       65\n",
       "89         200               金玉良缘       1       30\n",
       "90         200            超人气广式肠粉       2       18\n",
       "...        ...                ...     ...      ...\n",
       "3487       599  香辣爆羊肝\\r\\n\\r\\n\\r\\n       1       28\n",
       "3488       599               肉丁茄子       1       39\n",
       "3489       599               辣炒鱿鱼       1       58\n",
       "3502       721              炝炒大白菜       1       29\n",
       "3503       721               凉拌菠菜       1       27\n",
       "3505       721   泡椒凤爪\\r\\n\\r\\n\\r\\n       1       58\n",
       "3513       533        黄尾袋鼠西拉子红葡萄酒       1       46\n",
       "3514       533              芝麻烤紫菜       1       25\n",
       "3515       533               青炒扁豆       1       28\n",
       "3525       551              香菇鹌鹑蛋       1       39\n",
       "3526       551                白斩鸡       1       88\n",
       "3527       551            超人气广式肠粉       5       18\n",
       "3541       695              蒜香辣花甲       1       45\n",
       "3542       695           美妙绝伦之白莲花       1       29\n",
       "3543       695              红酒炖羊肉       1      108\n",
       "3566       570  蒙古烤羊腿\\r\\n\\r\\n\\r\\n       1       48\n",
       "3568       641             香辣腐乳炒虾       1       56\n",
       "3569       570  香辣爆羊肝\\r\\n\\r\\n\\r\\n       1       28\n",
       "3570       641             北冰洋汽水        1        5\n",
       "3572       641             倒立蒸梭子蟹       1      169\n",
       "3574       570          五色糯米饭(七色)       1       35\n",
       "3575       692                白斩鸡       1       88\n",
       "3576       692           桂圆肉红豆八宝饭       1       39\n",
       "3577       692   香烤牛排\\r\\n\\r\\n\\r\\n       1       55\n",
       "3604       647            西瓜胡萝卜沙拉       1       26\n",
       "3606       672               爆炒双丝       1       35\n",
       "3607       672   小炒羊腰\\r\\n\\r\\n\\r\\n       1       36\n",
       "3608       647              香菇鹌鹑蛋       1       39\n",
       "3609       647         不加一滴油的酸奶蛋糕       1        7\n",
       "3610       672               凉拌菠菜       1       27\n",
       "\n",
       "[994 rows x 4 columns]"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mealGroup.tail(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 聚合\n",
    "##### agg\n",
    "- 参数\n",
    "    - 接收func、list、dict表示用于行或列的函数\n",
    "    - axis=0：默认0，表示多行或列操作\n",
    "\n",
    "##### apply\n",
    "- 同agg用法，不过区别在于agg可用于特定字段，apply只用于整个DateFrame/Series\n",
    "\n",
    "##### transform\n",
    "- 只接收func一个参数\n",
    "- 只用于DateFrame（含Groupby）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(945, 21)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "path = '../data/meal_order_info.csv'\n",
    "order = pd.read_csv(path,encoding='gbk')\n",
    "order.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>info_id</th>\n",
       "      <th>emp_id</th>\n",
       "      <th>number_consumers</th>\n",
       "      <th>mode</th>\n",
       "      <th>dining_table_id</th>\n",
       "      <th>dining_table_name</th>\n",
       "      <th>expenditure</th>\n",
       "      <th>dishes_count</th>\n",
       "      <th>accounts_payable</th>\n",
       "      <th>use_start_time</th>\n",
       "      <th>...</th>\n",
       "      <th>lock_time</th>\n",
       "      <th>cashier_id</th>\n",
       "      <th>pc_id</th>\n",
       "      <th>order_number</th>\n",
       "      <th>org_id</th>\n",
       "      <th>print_doc_bill_num</th>\n",
       "      <th>lock_table_info</th>\n",
       "      <th>order_status</th>\n",
       "      <th>phone</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>417</td>\n",
       "      <td>1442</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1501</td>\n",
       "      <td>1022</td>\n",
       "      <td>165</td>\n",
       "      <td>5</td>\n",
       "      <td>165</td>\n",
       "      <td>2016/8/1 11:05:36</td>\n",
       "      <td>...</td>\n",
       "      <td>2016/8/1 11:11:46</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>330</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>18688880641</td>\n",
       "      <td>苗宇怡</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>301</td>\n",
       "      <td>1095</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1430</td>\n",
       "      <td>1031</td>\n",
       "      <td>321</td>\n",
       "      <td>6</td>\n",
       "      <td>321</td>\n",
       "      <td>2016/8/1 11:15:57</td>\n",
       "      <td>...</td>\n",
       "      <td>2016/8/1 11:31:55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>328</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>18688880174</td>\n",
       "      <td>赵颖</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>413</td>\n",
       "      <td>1147</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1488</td>\n",
       "      <td>1009</td>\n",
       "      <td>854</td>\n",
       "      <td>15</td>\n",
       "      <td>854</td>\n",
       "      <td>2016/8/1 12:42:52</td>\n",
       "      <td>...</td>\n",
       "      <td>2016/8/1 12:54:37</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>330</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>18688880276</td>\n",
       "      <td>徐毅凡</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>415</td>\n",
       "      <td>1166</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1502</td>\n",
       "      <td>1023</td>\n",
       "      <td>466</td>\n",
       "      <td>10</td>\n",
       "      <td>466</td>\n",
       "      <td>2016/8/1 12:51:38</td>\n",
       "      <td>...</td>\n",
       "      <td>2016/8/1 13:08:20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>330</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>18688880231</td>\n",
       "      <td>张大鹏</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>392</td>\n",
       "      <td>1094</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1499</td>\n",
       "      <td>1020</td>\n",
       "      <td>704</td>\n",
       "      <td>24</td>\n",
       "      <td>704</td>\n",
       "      <td>2016/8/1 12:58:44</td>\n",
       "      <td>...</td>\n",
       "      <td>2016/8/1 13:07:16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>330</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>18688880173</td>\n",
       "      <td>孙熙凯</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   info_id  emp_id  number_consumers  mode  dining_table_id  \\\n",
       "0      417    1442                 4   NaN             1501   \n",
       "1      301    1095                 3   NaN             1430   \n",
       "2      413    1147                 6   NaN             1488   \n",
       "3      415    1166                 4   NaN             1502   \n",
       "4      392    1094                10   NaN             1499   \n",
       "\n",
       "   dining_table_name  expenditure  dishes_count  accounts_payable  \\\n",
       "0               1022          165             5               165   \n",
       "1               1031          321             6               321   \n",
       "2               1009          854            15               854   \n",
       "3               1023          466            10               466   \n",
       "4               1020          704            24               704   \n",
       "\n",
       "      use_start_time  ...           lock_time cashier_id  pc_id  order_number  \\\n",
       "0  2016/8/1 11:05:36  ...   2016/8/1 11:11:46        NaN    NaN           NaN   \n",
       "1  2016/8/1 11:15:57  ...   2016/8/1 11:31:55        NaN    NaN           NaN   \n",
       "2  2016/8/1 12:42:52  ...   2016/8/1 12:54:37        NaN    NaN           NaN   \n",
       "3  2016/8/1 12:51:38  ...   2016/8/1 13:08:20        NaN    NaN           NaN   \n",
       "4  2016/8/1 12:58:44  ...   2016/8/1 13:07:16        NaN    NaN           NaN   \n",
       "\n",
       "   org_id  print_doc_bill_num  lock_table_info  order_status        phone  \\\n",
       "0     330                 NaN              NaN             1  18688880641   \n",
       "1     328                 NaN              NaN             1  18688880174   \n",
       "2     330                 NaN              NaN             1  18688880276   \n",
       "3     330                 NaN              NaN             1  18688880231   \n",
       "4     330                 NaN              NaN             1  18688880173   \n",
       "\n",
       "   name  \n",
       "0   苗宇怡  \n",
       "1    赵颖  \n",
       "2   徐毅凡  \n",
       "3   张大鹏  \n",
       "4   孙熙凯  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dishes_count</th>\n",
       "      <th>accounts_payable</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>36.000000</td>\n",
       "      <td>1314.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>11.805291</td>\n",
       "      <td>492.289947</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      dishes_count  accounts_payable\n",
       "max      36.000000       1314.000000\n",
       "mean     11.805291        492.289947"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#计算订单的菜品数和总额的最大值、平均值\n",
    "order[['dishes_count','accounts_payable']].agg(['max','mean'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dishes_count        11156.000000\n",
       "accounts_payable      492.289947\n",
       "dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#分字段聚合\n",
    "order[['dishes_count','accounts_payable']].agg({'dishes_count':'sum','accounts_payable':'mean'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.DataFrame([[2,11],[1,23],[5,11],[1.3,44],[5,111]],columns = ['price','quantity'],dtype = float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>quantity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.86</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sum</th>\n",
       "      <td>14.30</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      price  quantity\n",
       "mean   2.86       NaN\n",
       "sum   14.30     200.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.agg({'price':['sum','mean'],'quantity':['sum']})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "price         2.86\n",
       "quantity    200.00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.agg({'price':'mean','quantity':'sum'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dishes_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>11.805291</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      dishes_count\n",
       "mean     11.805291"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order.agg({'dishes_count':['mean']})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>accounts_payable</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1408</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   accounts_payable\n",
       "0               330\n",
       "1               642\n",
       "2              1708\n",
       "3               932\n",
       "4              1408"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#注意上面各种写法\n",
    "#使用自定义的函数\n",
    "def DoubleSum(data):\n",
    "    return np.sum(data)*2\n",
    "\n",
    "order.agg({'accounts_payable':DoubleSum},axis=0).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dishes_count</th>\n",
       "      <th>accounts_payable</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12</td>\n",
       "      <td>642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30</td>\n",
       "      <td>1708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20</td>\n",
       "      <td>932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>48</td>\n",
       "      <td>1408</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   dishes_count  accounts_payable\n",
       "0            10               330\n",
       "1            12               642\n",
       "2            30              1708\n",
       "3            20               932\n",
       "4            48              1408"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order[['dishes_count','accounts_payable']].transform(lambda x : x*2).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 类似Groupby分组聚合功能的透视表实现\n",
    "- pd.pivot_table()方法，常用参数\n",
    "    - data：DateFrame对象\n",
    "    - value：接收str，指定要聚合的字段名\n",
    "    - index：接收str/list，指定行分组键\n",
    "    - columns：接收str/list，指定列分组键\n",
    "    - aggfunc=mean：接收聚合函数，默认是np.mean\n",
    "    - dropna=False：是否删除全是nan的列\n",
    "    - fill_value：对缺失值进行填充，如fill_value=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "excel_path = '../data/meal_order_detail.xlsx'\n",
    "mealOrderInfo = pd.read_excel(excel_path,sheet_name='meal_order_detail3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "orderInfoPivot = pd.pivot_table(mealOrderInfo[['order_id','counts','amounts']],index = 'order_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>amounts</th>\n",
       "      <th>counts</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>order_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>36.400000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>38.222222</td>\n",
       "      <td>1.444444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>84.600000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>52.142857</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>30.833333</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            amounts    counts\n",
       "order_id                     \n",
       "163       36.400000  2.000000\n",
       "167       38.222222  1.444444\n",
       "168       84.600000  1.800000\n",
       "176       52.142857  1.000000\n",
       "178       30.833333  1.000000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orderInfoPivot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "每一单总额：amounts  数量：counts  \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>amounts</th>\n",
       "      <th>counts</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>order_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>182</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>344</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>423</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>365</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>185</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          amounts  counts\n",
       "order_id                 \n",
       "163           182      10\n",
       "167           344      13\n",
       "168           423       9\n",
       "176           365       7\n",
       "178           185       6"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#指定aggfunc\n",
    "orderInfoPivot1 = pd.pivot_table(mealOrderInfo[['order_id','counts','amounts']],index = 'order_id',aggfunc=np.sum)\n",
    "print('每一单总额：amounts  数量：counts  ')\n",
    "orderInfoPivot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>amounts</th>\n",
       "      <th>counts</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>order_id</th>\n",
       "      <th>dishes_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">163</th>\n",
       "      <th>凉拌菠菜</th>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>哈尔滨啤酒罐装</th>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>白饭/大碗</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>香菇鹌鹑蛋</th>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>麻辣小龙虾</th>\n",
       "      <td>99</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">167</th>\n",
       "      <th>北冰洋汽水</th>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>三色凉拌手撕兔</th>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>党参黄芪炖牛尾\\r\\n</th>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>培根花菜</th>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>清炒菊花菜</th>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      amounts  counts\n",
       "order_id dishes_name                 \n",
       "163      凉拌菠菜              27       1\n",
       "         哈尔滨啤酒罐装            7       6\n",
       "         白饭/大碗             10       1\n",
       "         香菇鹌鹑蛋             39       1\n",
       "         麻辣小龙虾             99       1\n",
       "167       北冰洋汽水             5       4\n",
       "         三色凉拌手撕兔           66       1\n",
       "         党参黄芪炖牛尾\\r\\n       35       1\n",
       "         培根花菜              35       1\n",
       "         清炒菊花菜             30       1"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#index(行分组键)可以通过列表形式指定多个聚合键\n",
    "orderInfoPivot2 =  pd.pivot_table(mealOrderInfo[['order_id','counts','amounts','dishes_name']],index = ['order_id','dishes_name'],aggfunc=np.sum)\n",
    "orderInfoPivot2.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"10\" halign=\"left\">amounts</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"10\" halign=\"left\">counts</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dishes_name</th>\n",
       "      <th>42度海之蓝</th>\n",
       "      <th>北冰洋汽水</th>\n",
       "      <th>38度剑南春</th>\n",
       "      <th>50度古井贡酒</th>\n",
       "      <th>52度泸州老窖</th>\n",
       "      <th>53度茅台</th>\n",
       "      <th>一品香酥藕</th>\n",
       "      <th>三丝鳝鱼</th>\n",
       "      <th>三色凉拌手撕兔</th>\n",
       "      <th>不加一滴油的酸奶蛋糕</th>\n",
       "      <th>...</th>\n",
       "      <th>香辣腐乳炒虾</th>\n",
       "      <th>香酥两吃大虾</th>\n",
       "      <th>鱼香肉丝拌面</th>\n",
       "      <th>鲜美鳝鱼</th>\n",
       "      <th>鸡蛋、肉末肠粉</th>\n",
       "      <th>麻辣小龙虾</th>\n",
       "      <th>黄尾袋鼠西拉子红葡萄酒</th>\n",
       "      <th>黄油曲奇饼干</th>\n",
       "      <th>黄花菜炒木耳</th>\n",
       "      <th>黑米恋上葡萄</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>order_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>66.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 308 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            amounts                                                            \\\n",
       "dishes_name  42度海之蓝  北冰洋汽水  38度剑南春  50度古井贡酒 52度泸州老窖  53度茅台 一品香酥藕 三丝鳝鱼 三色凉拌手撕兔   \n",
       "order_id                                                                        \n",
       "163             NaN     NaN     NaN     NaN      NaN   NaN   NaN  NaN     NaN   \n",
       "167             NaN     5.0     NaN     NaN      NaN   NaN   NaN  NaN    66.0   \n",
       "168             NaN     NaN     NaN     NaN      NaN   NaN   NaN  NaN     NaN   \n",
       "176             NaN     NaN     NaN     NaN      NaN   NaN   NaN  NaN     NaN   \n",
       "178             NaN     NaN     NaN     NaN      NaN   NaN   NaN  NaN     NaN   \n",
       "\n",
       "                        ...   counts                                   \\\n",
       "dishes_name 不加一滴油的酸奶蛋糕  ...   香辣腐乳炒虾 香酥两吃大虾 鱼香肉丝拌面 鲜美鳝鱼 鸡蛋、肉末肠粉 麻辣小龙虾   \n",
       "order_id                ...                                             \n",
       "163                NaN  ...      NaN    NaN    NaN  NaN     NaN   1.0   \n",
       "167                NaN  ...      NaN    NaN    NaN  NaN     NaN   NaN   \n",
       "168                NaN  ...      NaN    NaN    NaN  NaN     NaN   NaN   \n",
       "176                7.0  ...      NaN    NaN    NaN  NaN     NaN   NaN   \n",
       "178                NaN  ...      NaN    NaN    NaN  NaN     NaN   NaN   \n",
       "\n",
       "                                              \n",
       "dishes_name 黄尾袋鼠西拉子红葡萄酒 黄油曲奇饼干 黄花菜炒木耳 黑米恋上葡萄  \n",
       "order_id                                      \n",
       "163                 NaN    NaN    NaN    NaN  \n",
       "167                 NaN    NaN    NaN    NaN  \n",
       "168                 NaN    NaN    NaN    NaN  \n",
       "176                 NaN    NaN    NaN    NaN  \n",
       "178                 NaN    NaN    NaN    NaN  \n",
       "\n",
       "[5 rows x 308 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#column列分组键\n",
    "orderInfoPivot3 = pd.pivot_table(mealOrderInfo[['order_id','counts','amounts','dishes_name']],index = 'order_id',columns='dishes_name',aggfunc=np.sum)\n",
    "orderInfoPivot3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>不加一滴油的酸奶蛋糕</th>\n",
       "      <th>...</th>\n",
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       "      <th>香酥两吃大虾</th>\n",
       "      <th>鱼香肉丝拌面</th>\n",
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       "</table>\n",
       "<p>5 rows × 308 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            amounts                                                            \\\n",
       "dishes_name  42度海之蓝  北冰洋汽水  38度剑南春  50度古井贡酒 52度泸州老窖  53度茅台 一品香酥藕 三丝鳝鱼 三色凉拌手撕兔   \n",
       "order_id                                                                        \n",
       "163               0       0       0       0        0     0     0    0       0   \n",
       "167               0       5       0       0        0     0     0    0      66   \n",
       "168               0       0       0       0        0     0     0    0       0   \n",
       "176               0       0       0       0        0     0     0    0       0   \n",
       "178               0       0       0       0        0     0     0    0       0   \n",
       "\n",
       "                        ...   counts                                   \\\n",
       "dishes_name 不加一滴油的酸奶蛋糕  ...   香辣腐乳炒虾 香酥两吃大虾 鱼香肉丝拌面 鲜美鳝鱼 鸡蛋、肉末肠粉 麻辣小龙虾   \n",
       "order_id                ...                                             \n",
       "163                  0  ...        0      0      0    0       0     1   \n",
       "167                  0  ...        0      0      0    0       0     0   \n",
       "168                  0  ...        0      0      0    0       0     0   \n",
       "176                  7  ...        0      0      0    0       0     0   \n",
       "178                  0  ...        0      0      0    0       0     0   \n",
       "\n",
       "                                              \n",
       "dishes_name 黄尾袋鼠西拉子红葡萄酒 黄油曲奇饼干 黄花菜炒木耳 黑米恋上葡萄  \n",
       "order_id                                      \n",
       "163                   0      0      0      0  \n",
       "167                   0      0      0      0  \n",
       "168                   0      0      0      0  \n",
       "176                   0      0      0      0  \n",
       "178                   0      0      0      0  \n",
       "\n",
       "[5 rows x 308 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orderInfoPivot3 = pd.pivot_table(mealOrderInfo[['order_id','counts','amounts','dishes_name']],index = 'order_id',columns='dishes_name',aggfunc=np.sum,fill_value=0)\n",
    "orderInfoPivot3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>amounts</th>\n",
       "      <th>counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1132</th>\n",
       "      <td>党参黄芪炖牛尾\\r\\n</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1133</th>\n",
       "      <td>三色凉拌手撕兔</td>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1134</th>\n",
       "      <td>白斩鸡</td>\n",
       "      <td>88</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1135</th>\n",
       "      <td>爆炒鸡翅</td>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136</th>\n",
       "      <td>清炒菊花菜</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1137</th>\n",
       "      <td>培根花菜</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1138</th>\n",
       "      <td>北冰洋汽水</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1139</th>\n",
       "      <td>老式面包</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1140</th>\n",
       "      <td>白饭/大碗</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      dishes_name  amounts  counts\n",
       "1132  党参黄芪炖牛尾\\r\\n       35       1\n",
       "1133      三色凉拌手撕兔       66       1\n",
       "1134          白斩鸡       88       1\n",
       "1135         爆炒鸡翅       56       1\n",
       "1136        清炒菊花菜       30       1\n",
       "1137         培根花菜       35       1\n",
       "1138       北冰洋汽水         5       4\n",
       "1139         老式面包       19       1\n",
       "1140        白饭/大碗       10       2"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mealOrderInfo[mealOrderInfo['order_id']==167][['dishes_name','amounts','counts']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### crosstab创建交叉表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "path = '../data/Training_Master.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2014/2/27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 228 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Idx  UserInfo_1 UserInfo_2  UserInfo_3 UserInfo_4  WeblogInfo_1  \\\n",
       "0  10001         1.0         深圳         4.0         深圳           NaN   \n",
       "1  10002         1.0         温州         4.0         温州           NaN   \n",
       "2  10003         1.0         宜昌         3.0         宜昌           NaN   \n",
       "3  10006         4.0         南平         1.0         南平           NaN   \n",
       "4  10007         5.0         辽阳         1.0         辽阳           NaN   \n",
       "\n",
       "   WeblogInfo_2  WeblogInfo_3  WeblogInfo_4  WeblogInfo_5     ...       \\\n",
       "0           1.0           NaN           1.0           1.0     ...        \n",
       "1           0.0           NaN           1.0           1.0     ...        \n",
       "2           0.0           NaN           2.0           2.0     ...        \n",
       "3           NaN           NaN           NaN           NaN     ...        \n",
       "4           0.0           NaN           1.0           1.0     ...        \n",
       "\n",
       "   SocialNetwork_10  SocialNetwork_11  SocialNetwork_12  SocialNetwork_13  \\\n",
       "0               222                -1                 0                 0   \n",
       "1                 1                -1                 0                 0   \n",
       "2                -1                -1                -1                 1   \n",
       "3                -1                -1                -1                 0   \n",
       "4                -1                -1                -1                 0   \n",
       "\n",
       "   SocialNetwork_14  SocialNetwork_15  SocialNetwork_16  SocialNetwork_17  \\\n",
       "0                 0                 0                 0                 1   \n",
       "1                 0                 0                 0                 2   \n",
       "2                 0                 0                 0                 0   \n",
       "3                 0                 0                 0                 0   \n",
       "4                 0                 0                 0                 0   \n",
       "\n",
       "   target  ListingInfo  \n",
       "0       0     2014/3/5  \n",
       "1       0    2014/2/26  \n",
       "2       0    2014/2/28  \n",
       "3       0    2014/2/25  \n",
       "4       0    2014/2/27  \n",
       "\n",
       "[5 rows x 228 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_master = pd.read_csv(path,encoding='gbk')\n",
    "df_master.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 删除列值相同或全为空的列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dropByDescribe(df):\n",
    "#     列为空，列值相同方差为0\n",
    "    isNull = df.describe().loc['count']==0\n",
    "    stdIsZero = df.describe().loc['std']==0\n",
    "    columns = df.describe().columns\n",
    "    for i in range(len(columns)):\n",
    "        if isNull[i]:\n",
    "            df.drop(columns[i],axis=1,inplace=True)\n",
    "    \n",
    "    for i in range(len(columns)):\n",
    "        if stdIsZero[i]:\n",
    "            df.drop(columns[i],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Idx</th>\n",
       "      <th>UserInfo_1</th>\n",
       "      <th>UserInfo_2</th>\n",
       "      <th>UserInfo_3</th>\n",
       "      <th>UserInfo_4</th>\n",
       "      <th>WeblogInfo_1</th>\n",
       "      <th>WeblogInfo_2</th>\n",
       "      <th>WeblogInfo_3</th>\n",
       "      <th>WeblogInfo_4</th>\n",
       "      <th>WeblogInfo_5</th>\n",
       "      <th>...</th>\n",
       "      <th>SocialNetwork_10</th>\n",
       "      <th>SocialNetwork_11</th>\n",
       "      <th>SocialNetwork_12</th>\n",
       "      <th>SocialNetwork_13</th>\n",
       "      <th>SocialNetwork_14</th>\n",
       "      <th>SocialNetwork_15</th>\n",
       "      <th>SocialNetwork_16</th>\n",
       "      <th>SocialNetwork_17</th>\n",
       "      <th>target</th>\n",
       "      <th>ListingInfo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10001</td>\n",
       "      <td>1.0</td>\n",
       "      <td>深圳</td>\n",
       "      <td>4.0</td>\n",
       "      <td>深圳</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>222</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2014/3/5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10002</td>\n",
       "      <td>1.0</td>\n",
       "      <td>温州</td>\n",
       "      <td>4.0</td>\n",
       "      <td>温州</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2014/2/26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10003</td>\n",
       "      <td>1.0</td>\n",
       "      <td>宜昌</td>\n",
       "      <td>3.0</td>\n",
       "      <td>宜昌</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2014/2/28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10006</td>\n",
       "      <td>4.0</td>\n",
       "      <td>南平</td>\n",
       "      <td>1.0</td>\n",
       "      <td>南平</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2014/2/25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10007</td>\n",
       "      <td>5.0</td>\n",
       "      <td>辽阳</td>\n",
       "      <td>1.0</td>\n",
       "      <td>辽阳</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2014/2/27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 226 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Idx  UserInfo_1 UserInfo_2  UserInfo_3 UserInfo_4  WeblogInfo_1  \\\n",
       "0  10001         1.0         深圳         4.0         深圳           NaN   \n",
       "1  10002         1.0         温州         4.0         温州           NaN   \n",
       "2  10003         1.0         宜昌         3.0         宜昌           NaN   \n",
       "3  10006         4.0         南平         1.0         南平           NaN   \n",
       "4  10007         5.0         辽阳         1.0         辽阳           NaN   \n",
       "\n",
       "   WeblogInfo_2  WeblogInfo_3  WeblogInfo_4  WeblogInfo_5     ...       \\\n",
       "0           1.0           NaN           1.0           1.0     ...        \n",
       "1           0.0           NaN           1.0           1.0     ...        \n",
       "2           0.0           NaN           2.0           2.0     ...        \n",
       "3           NaN           NaN           NaN           NaN     ...        \n",
       "4           0.0           NaN           1.0           1.0     ...        \n",
       "\n",
       "   SocialNetwork_10  SocialNetwork_11  SocialNetwork_12  SocialNetwork_13  \\\n",
       "0               222                -1                 0                 0   \n",
       "1                 1                -1                 0                 0   \n",
       "2                -1                -1                -1                 1   \n",
       "3                -1                -1                -1                 0   \n",
       "4                -1                -1                -1                 0   \n",
       "\n",
       "   SocialNetwork_14  SocialNetwork_15  SocialNetwork_16  SocialNetwork_17  \\\n",
       "0                 0                 0                 0                 1   \n",
       "1                 0                 0                 0                 2   \n",
       "2                 0                 0                 0                 0   \n",
       "3                 0                 0                 0                 0   \n",
       "4                 0                 0                 0                 0   \n",
       "\n",
       "   target  ListingInfo  \n",
       "0       0     2014/3/5  \n",
       "1       0    2014/2/26  \n",
       "2       0    2014/2/28  \n",
       "3       0    2014/2/25  \n",
       "4       0    2014/2/27  \n",
       "\n",
       "[5 rows x 226 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dropByDescribe(df_master)\n",
    "df_master.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Idx</th>\n",
       "      <th>Listinginfo1</th>\n",
       "      <th>LogInfo1</th>\n",
       "      <th>LogInfo2</th>\n",
       "      <th>LogInfo3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10001</td>\n",
       "      <td>2014-03-05</td>\n",
       "      <td>107</td>\n",
       "      <td>6</td>\n",
       "      <td>2014-02-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10001</td>\n",
       "      <td>2014-03-05</td>\n",
       "      <td>107</td>\n",
       "      <td>6</td>\n",
       "      <td>2014-02-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10001</td>\n",
       "      <td>2014-03-05</td>\n",
       "      <td>107</td>\n",
       "      <td>6</td>\n",
       "      <td>2014-02-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10001</td>\n",
       "      <td>2014-03-05</td>\n",
       "      <td>107</td>\n",
       "      <td>6</td>\n",
       "      <td>2014-02-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10001</td>\n",
       "      <td>2014-03-05</td>\n",
       "      <td>107</td>\n",
       "      <td>6</td>\n",
       "      <td>2014-02-27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Idx Listinginfo1  LogInfo1  LogInfo2    LogInfo3\n",
       "0  10001   2014-03-05       107         6  2014-02-20\n",
       "1  10001   2014-03-05       107         6  2014-02-23\n",
       "2  10001   2014-03-05       107         6  2014-02-24\n",
       "3  10001   2014-03-05       107         6  2014-02-25\n",
       "4  10001   2014-03-05       107         6  2014-02-27"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_loginInFo = pd.read_csv('../data/Training_LogInfo.csv',encoding='gbk')\n",
    "df_userUpdate = pd.read_csv('../data/Training_Userupdate.csv',encoding='gbk')\n",
    "df_loginInFo.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Idx</th>\n",
       "      <th>Listinginfo1</th>\n",
       "      <th>LogInfo1</th>\n",
       "      <th>LogInfo2</th>\n",
       "      <th>LogInfo3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10001</td>\n",
       "      <td>2014-03-05</td>\n",
       "      <td>107</td>\n",
       "      <td>6</td>\n",
       "      <td>2014-02-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10001</td>\n",
       "      <td>2014-03-05</td>\n",
       "      <td>107</td>\n",
       "      <td>6</td>\n",
       "      <td>2014-02-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10001</td>\n",
       "      <td>2014-03-05</td>\n",
       "      <td>107</td>\n",
       "      <td>6</td>\n",
       "      <td>2014-02-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10001</td>\n",
       "      <td>2014-03-05</td>\n",
       "      <td>107</td>\n",
       "      <td>6</td>\n",
       "      <td>2014-02-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10001</td>\n",
       "      <td>2014-03-05</td>\n",
       "      <td>107</td>\n",
       "      <td>6</td>\n",
       "      <td>2014-02-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10001</td>\n",
       "      <td>2014-03-05</td>\n",
       "      <td>107</td>\n",
       "      <td>6</td>\n",
       "      <td>2014-03-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10001</td>\n",
       "      <td>2014-03-05</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-02-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10001</td>\n",
       "      <td>2014-03-05</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>2014-02-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10001</td>\n",
       "      <td>2014-03-05</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>2014-02-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10001</td>\n",
       "      <td>2014-03-05</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2014-02-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10001</td>\n",
       "      <td>2014-03-05</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-02-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>10001</td>\n",
       "      <td>2014-03-05</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-02-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>10001</td>\n",
       "      <td>2014-03-05</td>\n",
       "      <td>-4</td>\n",
       "      <td>6</td>\n",
       "      <td>2014-02-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>10001</td>\n",
       "      <td>2014-03-05</td>\n",
       "      <td>-4</td>\n",
       "      <td>6</td>\n",
       "      <td>2014-02-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>10001</td>\n",
       "      <td>2014-03-05</td>\n",
       "      <td>-4</td>\n",
       "      <td>6</td>\n",
       "      <td>2014-02-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>10001</td>\n",
       "      <td>2014-03-05</td>\n",
       "      <td>-4</td>\n",
       "      <td>6</td>\n",
       "      <td>2014-02-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>10001</td>\n",
       "      <td>2014-03-05</td>\n",
       "      <td>-4</td>\n",
       "      <td>6</td>\n",
       "      <td>2014-02-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>10001</td>\n",
       "      <td>2014-03-05</td>\n",
       "      <td>-4</td>\n",
       "      <td>6</td>\n",
       "      <td>2014-02-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>10001</td>\n",
       "      <td>2014-03-05</td>\n",
       "      <td>-4</td>\n",
       "      <td>6</td>\n",
       "      <td>2014-03-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>10002</td>\n",
       "      <td>2014-02-26</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>2013-06-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>10002</td>\n",
       "      <td>2014-02-26</td>\n",
       "      <td>107</td>\n",
       "      <td>6</td>\n",
       "      <td>2013-07-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>10002</td>\n",
       "      <td>2014-02-26</td>\n",
       "      <td>107</td>\n",
       "      <td>6</td>\n",
       "      <td>2013-07-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>10002</td>\n",
       "      <td>2014-02-26</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2013-06-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>10002</td>\n",
       "      <td>2014-02-26</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2014-02-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>10002</td>\n",
       "      <td>2014-02-26</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>2013-06-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>10002</td>\n",
       "      <td>2014-02-26</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2013-06-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>10002</td>\n",
       "      <td>2014-02-26</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2013-06-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>10002</td>\n",
       "      <td>2014-02-26</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>2013-07-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>10002</td>\n",
       "      <td>2014-02-26</td>\n",
       "      <td>-4</td>\n",
       "      <td>6</td>\n",
       "      <td>2014-02-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>10002</td>\n",
       "      <td>2014-02-26</td>\n",
       "      <td>-4</td>\n",
       "      <td>6</td>\n",
       "      <td>2014-02-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580521</th>\n",
       "      <td>9997</td>\n",
       "      <td>2014-02-28</td>\n",
       "      <td>107</td>\n",
       "      <td>6</td>\n",
       "      <td>2014-02-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580522</th>\n",
       "      <td>9997</td>\n",
       "      <td>2014-02-28</td>\n",
       "      <td>107</td>\n",
       "      <td>6</td>\n",
       "      <td>2014-02-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580523</th>\n",
       "      <td>9997</td>\n",
       "      <td>2014-02-28</td>\n",
       "      <td>107</td>\n",
       "      <td>6</td>\n",
       "      <td>2014-02-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580524</th>\n",
       "      <td>9997</td>\n",
       "      <td>2014-02-28</td>\n",
       "      <td>107</td>\n",
       "      <td>6</td>\n",
       "      <td>2014-02-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580525</th>\n",
       "      <td>9997</td>\n",
       "      <td>2014-02-28</td>\n",
       "      <td>107</td>\n",
       "      <td>6</td>\n",
       "      <td>2014-02-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580526</th>\n",
       "      <td>9997</td>\n",
       "      <td>2014-02-28</td>\n",
       "      <td>107</td>\n",
       "      <td>6</td>\n",
       "      <td>2014-02-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580527</th>\n",
       "      <td>9997</td>\n",
       "      <td>2014-02-28</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-02-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580528</th>\n",
       "      <td>9997</td>\n",
       "      <td>2014-02-28</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>2014-02-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580529</th>\n",
       "      <td>9997</td>\n",
       "      <td>2014-02-28</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>2014-02-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580530</th>\n",
       "      <td>9997</td>\n",
       "      <td>2014-02-28</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2014-02-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580531</th>\n",
       "      <td>9997</td>\n",
       "      <td>2014-02-28</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-02-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580532</th>\n",
       "      <td>9997</td>\n",
       "      <td>2014-02-28</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-02-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580533</th>\n",
       "      <td>9997</td>\n",
       "      <td>2014-02-28</td>\n",
       "      <td>-4</td>\n",
       "      <td>6</td>\n",
       "      <td>2014-02-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580534</th>\n",
       "      <td>9997</td>\n",
       "      <td>2014-02-28</td>\n",
       "      <td>-4</td>\n",
       "      <td>6</td>\n",
       "      <td>2014-02-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580535</th>\n",
       "      <td>9997</td>\n",
       "      <td>2014-02-28</td>\n",
       "      <td>-4</td>\n",
       "      <td>6</td>\n",
       "      <td>2014-02-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580536</th>\n",
       "      <td>9997</td>\n",
       "      <td>2014-02-28</td>\n",
       "      <td>-4</td>\n",
       "      <td>6</td>\n",
       "      <td>2014-02-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580537</th>\n",
       "      <td>9997</td>\n",
       "      <td>2014-02-28</td>\n",
       "      <td>-4</td>\n",
       "      <td>6</td>\n",
       "      <td>2014-02-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580538</th>\n",
       "      <td>9997</td>\n",
       "      <td>2014-02-28</td>\n",
       "      <td>-4</td>\n",
       "      <td>6</td>\n",
       "      <td>2014-02-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580539</th>\n",
       "      <td>9997</td>\n",
       "      <td>2014-02-28</td>\n",
       "      <td>-4</td>\n",
       "      <td>6</td>\n",
       "      <td>2014-02-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580540</th>\n",
       "      <td>9997</td>\n",
       "      <td>2014-02-28</td>\n",
       "      <td>-4</td>\n",
       "      <td>6</td>\n",
       "      <td>2014-02-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580541</th>\n",
       "      <td>9997</td>\n",
       "      <td>2014-02-28</td>\n",
       "      <td>-4</td>\n",
       "      <td>6</td>\n",
       "      <td>2014-02-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580542</th>\n",
       "      <td>9998</td>\n",
       "      <td>2014-03-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2014-02-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580543</th>\n",
       "      <td>9998</td>\n",
       "      <td>2014-03-05</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-02-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580544</th>\n",
       "      <td>9998</td>\n",
       "      <td>2014-03-05</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>2014-02-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580545</th>\n",
       "      <td>9998</td>\n",
       "      <td>2014-03-05</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2014-02-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580546</th>\n",
       "      <td>9998</td>\n",
       "      <td>2014-03-05</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-02-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580547</th>\n",
       "      <td>9998</td>\n",
       "      <td>2014-03-05</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-02-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580548</th>\n",
       "      <td>9998</td>\n",
       "      <td>2014-03-05</td>\n",
       "      <td>-4</td>\n",
       "      <td>6</td>\n",
       "      <td>2014-02-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580549</th>\n",
       "      <td>9998</td>\n",
       "      <td>2014-03-05</td>\n",
       "      <td>-4</td>\n",
       "      <td>6</td>\n",
       "      <td>2014-03-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580550</th>\n",
       "      <td>9998</td>\n",
       "      <td>2014-03-05</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>2014-03-05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>580551 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Idx Listinginfo1  LogInfo1  LogInfo2    LogInfo3\n",
       "0       10001   2014-03-05       107         6  2014-02-20\n",
       "1       10001   2014-03-05       107         6  2014-02-23\n",
       "2       10001   2014-03-05       107         6  2014-02-24\n",
       "3       10001   2014-03-05       107         6  2014-02-25\n",
       "4       10001   2014-03-05       107         6  2014-02-27\n",
       "5       10001   2014-03-05       107         6  2014-03-04\n",
       "6       10001   2014-03-05         1         1  2014-02-20\n",
       "7       10001   2014-03-05         1        20  2014-02-20\n",
       "8       10001   2014-03-05        12         0  2014-02-20\n",
       "9       10001   2014-03-05         1         2  2014-02-20\n",
       "10      10001   2014-03-05         2         1  2014-02-20\n",
       "11      10001   2014-03-05         4         1  2014-02-20\n",
       "12      10001   2014-03-05        -4         6  2014-02-20\n",
       "13      10001   2014-03-05        -4         6  2014-02-20\n",
       "14      10001   2014-03-05        -4         6  2014-02-23\n",
       "15      10001   2014-03-05        -4         6  2014-02-24\n",
       "16      10001   2014-03-05        -4         6  2014-02-25\n",
       "17      10001   2014-03-05        -4         6  2014-02-27\n",
       "18      10001   2014-03-05        -4         6  2014-03-04\n",
       "19      10002   2014-02-26        10         0  2013-06-21\n",
       "20      10002   2014-02-26       107         6  2013-07-08\n",
       "21      10002   2014-02-26       107         6  2013-07-08\n",
       "22      10002   2014-02-26         1         1  2013-06-21\n",
       "23      10002   2014-02-26         1         2  2014-02-20\n",
       "24      10002   2014-02-26         1         6  2013-06-21\n",
       "25      10002   2014-02-26         2         1  2013-06-21\n",
       "26      10002   2014-02-26         4         1  2013-06-21\n",
       "27      10002   2014-02-26         4        19  2013-07-08\n",
       "28      10002   2014-02-26        -4         6  2014-02-20\n",
       "29      10002   2014-02-26        -4         6  2014-02-25\n",
       "...       ...          ...       ...       ...         ...\n",
       "580521   9997   2014-02-28       107         6  2014-02-20\n",
       "580522   9997   2014-02-28       107         6  2014-02-21\n",
       "580523   9997   2014-02-28       107         6  2014-02-24\n",
       "580524   9997   2014-02-28       107         6  2014-02-26\n",
       "580525   9997   2014-02-28       107         6  2014-02-27\n",
       "580526   9997   2014-02-28       107         6  2014-02-28\n",
       "580527   9997   2014-02-28         1         1  2014-02-20\n",
       "580528   9997   2014-02-28         1        20  2014-02-20\n",
       "580529   9997   2014-02-28        12         0  2014-02-20\n",
       "580530   9997   2014-02-28         1         2  2014-02-20\n",
       "580531   9997   2014-02-28         2         1  2014-02-20\n",
       "580532   9997   2014-02-28         4         1  2014-02-20\n",
       "580533   9997   2014-02-28        -4         6  2014-02-20\n",
       "580534   9997   2014-02-28        -4         6  2014-02-20\n",
       "580535   9997   2014-02-28        -4         6  2014-02-20\n",
       "580536   9997   2014-02-28        -4         6  2014-02-21\n",
       "580537   9997   2014-02-28        -4         6  2014-02-24\n",
       "580538   9997   2014-02-28        -4         6  2014-02-26\n",
       "580539   9997   2014-02-28        -4         6  2014-02-27\n",
       "580540   9997   2014-02-28        -4         6  2014-02-27\n",
       "580541   9997   2014-02-28        -4         6  2014-02-28\n",
       "580542   9998   2014-03-05         1         0  2014-02-20\n",
       "580543   9998   2014-03-05         1         1  2014-02-20\n",
       "580544   9998   2014-03-05         1        20  2014-02-20\n",
       "580545   9998   2014-03-05         1         2  2014-02-20\n",
       "580546   9998   2014-03-05         2         1  2014-02-20\n",
       "580547   9998   2014-03-05         4         1  2014-02-20\n",
       "580548   9998   2014-03-05        -4         6  2014-02-20\n",
       "580549   9998   2014-03-05        -4         6  2014-03-05\n",
       "580550   9998   2014-03-05         4         7  2014-03-05\n",
       "\n",
       "[580551 rows x 5 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_loginInFo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Listinginfo1</th>\n",
       "      <th>LogInfo1</th>\n",
       "      <th>LogInfo2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Idx</th>\n",
       "      <th>LogInfo3</th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">3</th>\n",
       "      <th>2013-08-30</th>\n",
       "      <td>2013-11-05</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-08-31</th>\n",
       "      <td>2013-11-05</td>\n",
       "      <td>-4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-09-03</th>\n",
       "      <td>2013-11-05</td>\n",
       "      <td>-4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-09-04</th>\n",
       "      <td>2013-11-05</td>\n",
       "      <td>-4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-10-23</th>\n",
       "      <td>2013-11-05</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-10-24</th>\n",
       "      <td>2013-11-05</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-10-31</th>\n",
       "      <td>2013-11-05</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-11-01</th>\n",
       "      <td>2013-11-05</td>\n",
       "      <td>-4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">5</th>\n",
       "      <th>2013-10-24</th>\n",
       "      <td>2013-11-07</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-10-29</th>\n",
       "      <td>2013-11-07</td>\n",
       "      <td>-4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-10-30</th>\n",
       "      <td>2013-11-07</td>\n",
       "      <td>-4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-11-06</th>\n",
       "      <td>2013-11-07</td>\n",
       "      <td>-4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"13\" valign=\"top\">8</th>\n",
       "      <th>2013-10-25</th>\n",
       "      <td>2013-11-06</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-10-26</th>\n",
       "      <td>2013-11-06</td>\n",
       "      <td>-4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-10-27</th>\n",
       "      <td>2013-11-06</td>\n",
       "      <td>-4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-10-28</th>\n",
       "      <td>2013-11-06</td>\n",
       "      <td>-4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-10-29</th>\n",
       "      <td>2013-11-06</td>\n",
       "      <td>-4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-10-30</th>\n",
       "      <td>2013-11-06</td>\n",
       "      <td>-4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-10-31</th>\n",
       "      <td>2013-11-06</td>\n",
       "      <td>99</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-11-01</th>\n",
       "      <td>2013-11-06</td>\n",
       "      <td>-4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-11-02</th>\n",
       "      <td>2013-11-06</td>\n",
       "      <td>99</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-11-03</th>\n",
       "      <td>2013-11-06</td>\n",
       "      <td>-4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-11-04</th>\n",
       "      <td>2013-11-06</td>\n",
       "      <td>99</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-11-05</th>\n",
       "      <td>2013-11-06</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-11-06</th>\n",
       "      <td>2013-11-06</td>\n",
       "      <td>99</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">12</th>\n",
       "      <th>2012-12-08</th>\n",
       "      <td>2013-11-01</td>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-10-02</th>\n",
       "      <td>2013-11-01</td>\n",
       "      <td>100</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-10-03</th>\n",
       "      <td>2013-11-01</td>\n",
       "      <td>100</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-10-22</th>\n",
       "      <td>2013-11-01</td>\n",
       "      <td>100</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-10-26</th>\n",
       "      <td>2013-11-01</td>\n",
       "      <td>100</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">91688</th>\n",
       "      <th>2014-10-28</th>\n",
       "      <td>2014-10-30</td>\n",
       "      <td>-4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-29</th>\n",
       "      <td>2014-10-30</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-30</th>\n",
       "      <td>2014-10-30</td>\n",
       "      <td>-4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">91689</th>\n",
       "      <th>2014-10-26</th>\n",
       "      <td>2014-10-29</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-29</th>\n",
       "      <td>2014-10-29</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"9\" valign=\"top\">91693</th>\n",
       "      <th>2014-10-26</th>\n",
       "      <td>2014-11-06</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-28</th>\n",
       "      <td>2014-11-06</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-30</th>\n",
       "      <td>2014-11-06</td>\n",
       "      <td>-4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-31</th>\n",
       "      <td>2014-11-06</td>\n",
       "      <td>-4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-01</th>\n",
       "      <td>2014-11-06</td>\n",
       "      <td>-4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-02</th>\n",
       "      <td>2014-11-06</td>\n",
       "      <td>99</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-03</th>\n",
       "      <td>2014-11-06</td>\n",
       "      <td>-4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-04</th>\n",
       "      <td>2014-11-06</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-05</th>\n",
       "      <td>2014-11-06</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">91695</th>\n",
       "      <th>2014-09-26</th>\n",
       "      <td>2014-10-30</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-22</th>\n",
       "      <td>2014-10-30</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-26</th>\n",
       "      <td>2014-10-30</td>\n",
       "      <td>302</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-27</th>\n",
       "      <td>2014-10-30</td>\n",
       "      <td>-4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-28</th>\n",
       "      <td>2014-10-30</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-29</th>\n",
       "      <td>2014-10-30</td>\n",
       "      <td>-4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-30</th>\n",
       "      <td>2014-10-30</td>\n",
       "      <td>-4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">91702</th>\n",
       "      <th>2014-10-26</th>\n",
       "      <td>2014-11-02</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-28</th>\n",
       "      <td>2014-11-02</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">91703</th>\n",
       "      <th>2014-10-26</th>\n",
       "      <td>2014-11-03</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-29</th>\n",
       "      <td>2014-11-03</td>\n",
       "      <td>-4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-30</th>\n",
       "      <td>2014-11-03</td>\n",
       "      <td>-4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-31</th>\n",
       "      <td>2014-11-03</td>\n",
       "      <td>-4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-01</th>\n",
       "      <td>2014-11-03</td>\n",
       "      <td>-4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-02</th>\n",
       "      <td>2014-11-03</td>\n",
       "      <td>307</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-03</th>\n",
       "      <td>2014-11-03</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150564 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Listinginfo1  LogInfo1  LogInfo2\n",
       "Idx   LogInfo3                                   \n",
       "3     2013-08-30   2013-11-05         4         6\n",
       "      2013-08-31   2013-11-05        -4         6\n",
       "      2013-09-03   2013-11-05        -4         6\n",
       "      2013-09-04   2013-11-05        -4         6\n",
       "      2013-10-23   2013-11-05         4         7\n",
       "      2013-10-24   2013-11-05         4         7\n",
       "      2013-10-31   2013-11-05         4         7\n",
       "      2013-11-01   2013-11-05        -4         6\n",
       "5     2013-10-24   2013-11-07         4        13\n",
       "      2013-10-29   2013-11-07        -4         6\n",
       "      2013-10-30   2013-11-07        -4         6\n",
       "      2013-11-06   2013-11-07        -4         6\n",
       "8     2013-10-25   2013-11-06         4         6\n",
       "      2013-10-26   2013-11-06        -4         6\n",
       "      2013-10-27   2013-11-06        -4         6\n",
       "      2013-10-28   2013-11-06        -4         6\n",
       "      2013-10-29   2013-11-06        -4         6\n",
       "      2013-10-30   2013-11-06        -4         6\n",
       "      2013-10-31   2013-11-06        99         6\n",
       "      2013-11-01   2013-11-06        -4         6\n",
       "      2013-11-02   2013-11-06        99         6\n",
       "      2013-11-03   2013-11-06        -4         6\n",
       "      2013-11-04   2013-11-06        99         6\n",
       "      2013-11-05   2013-11-06         3        13\n",
       "      2013-11-06   2013-11-06        99        21\n",
       "12    2012-12-08   2013-11-01        10        20\n",
       "      2013-10-02   2013-11-01       100        13\n",
       "      2013-10-03   2013-11-01       100         6\n",
       "      2013-10-22   2013-11-01       100         6\n",
       "      2013-10-26   2013-11-01       100         6\n",
       "...                       ...       ...       ...\n",
       "91688 2014-10-28   2014-10-30        -4         6\n",
       "      2014-10-29   2014-10-30         2        21\n",
       "      2014-10-30   2014-10-30        -4         6\n",
       "91689 2014-10-26   2014-10-29         4        20\n",
       "      2014-10-29   2014-10-29         2        21\n",
       "91693 2014-10-26   2014-11-06         4        20\n",
       "      2014-10-28   2014-11-06         4         7\n",
       "      2014-10-30   2014-11-06        -4         6\n",
       "      2014-10-31   2014-11-06        -4         6\n",
       "      2014-11-01   2014-11-06        -4         6\n",
       "      2014-11-02   2014-11-06        99         6\n",
       "      2014-11-03   2014-11-06        -4         6\n",
       "      2014-11-04   2014-11-06         3        13\n",
       "      2014-11-05   2014-11-06         2        21\n",
       "91695 2014-09-26   2014-10-30         4        20\n",
       "      2014-10-22   2014-10-30         4         7\n",
       "      2014-10-26   2014-10-30       302        20\n",
       "      2014-10-27   2014-10-30        -4         6\n",
       "      2014-10-28   2014-10-30         2        21\n",
       "      2014-10-29   2014-10-30        -4         6\n",
       "      2014-10-30   2014-10-30        -4         6\n",
       "91702 2014-10-26   2014-11-02         4        20\n",
       "      2014-10-28   2014-11-02         2        21\n",
       "91703 2014-10-26   2014-11-03         4        20\n",
       "      2014-10-29   2014-11-03        -4         6\n",
       "      2014-10-30   2014-11-03        -4         6\n",
       "      2014-10-31   2014-11-03        -4         6\n",
       "      2014-11-01   2014-11-03        -4         6\n",
       "      2014-11-02   2014-11-03       307        20\n",
       "      2014-11-03   2014-11-03         2        21\n",
       "\n",
       "[150564 rows x 3 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_loginInFo.groupby(['Idx','LogInfo3']).max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Idx</th>\n",
       "      <th>ListingInfo1</th>\n",
       "      <th>UserupdateInfo1</th>\n",
       "      <th>UserupdateInfo2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10001</td>\n",
       "      <td>2014/03/05</td>\n",
       "      <td>_EducationId</td>\n",
       "      <td>2014/02/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10001</td>\n",
       "      <td>2014/03/05</td>\n",
       "      <td>_HasBuyCar</td>\n",
       "      <td>2014/02/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10001</td>\n",
       "      <td>2014/03/05</td>\n",
       "      <td>_LastUpdateDate</td>\n",
       "      <td>2014/02/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10001</td>\n",
       "      <td>2014/03/05</td>\n",
       "      <td>_MarriageStatusId</td>\n",
       "      <td>2014/02/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10001</td>\n",
       "      <td>2014/03/05</td>\n",
       "      <td>_MobilePhone</td>\n",
       "      <td>2014/02/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10001</td>\n",
       "      <td>2014/03/05</td>\n",
       "      <td>_MobilePhone</td>\n",
       "      <td>2014/02/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10001</td>\n",
       "      <td>2014/03/05</td>\n",
       "      <td>_QQ</td>\n",
       "      <td>2014/02/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10001</td>\n",
       "      <td>2014/03/05</td>\n",
       "      <td>_ResidenceAddress</td>\n",
       "      <td>2014/02/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10001</td>\n",
       "      <td>2014/03/05</td>\n",
       "      <td>_ResidencePhone</td>\n",
       "      <td>2014/02/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10001</td>\n",
       "      <td>2014/03/05</td>\n",
       "      <td>_ResidenceTypeId</td>\n",
       "      <td>2014/02/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10001</td>\n",
       "      <td>2014/03/05</td>\n",
       "      <td>_ResidenceYears</td>\n",
       "      <td>2014/02/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>10002</td>\n",
       "      <td>2014/02/26</td>\n",
       "      <td>_age</td>\n",
       "      <td>2013/06/21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>10002</td>\n",
       "      <td>2014/02/26</td>\n",
       "      <td>_educationId</td>\n",
       "      <td>2013/06/21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>10002</td>\n",
       "      <td>2014/02/26</td>\n",
       "      <td>_gender</td>\n",
       "      <td>2013/06/21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>10002</td>\n",
       "      <td>2014/02/26</td>\n",
       "      <td>_hasBuyCar</td>\n",
       "      <td>2013/06/21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>10002</td>\n",
       "      <td>2014/02/26</td>\n",
       "      <td>_idNumber</td>\n",
       "      <td>2013/06/21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>10002</td>\n",
       "      <td>2014/02/26</td>\n",
       "      <td>_lastUpdateDate</td>\n",
       "      <td>2013/06/21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>10002</td>\n",
       "      <td>2014/02/26</td>\n",
       "      <td>_lastUpdateDate</td>\n",
       "      <td>2013/07/08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>10002</td>\n",
       "      <td>2014/02/26</td>\n",
       "      <td>_lastUpdateDate</td>\n",
       "      <td>2013/07/08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>10002</td>\n",
       "      <td>2014/02/26</td>\n",
       "      <td>_lastUpdateDate</td>\n",
       "      <td>2013/07/08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>10002</td>\n",
       "      <td>2014/02/26</td>\n",
       "      <td>_lastUpdateDate</td>\n",
       "      <td>2013/07/08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>10002</td>\n",
       "      <td>2014/02/26</td>\n",
       "      <td>_marriageStatusId</td>\n",
       "      <td>2013/06/21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>10002</td>\n",
       "      <td>2014/02/26</td>\n",
       "      <td>_mobilePhone</td>\n",
       "      <td>2013/06/21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>10002</td>\n",
       "      <td>2014/02/26</td>\n",
       "      <td>_MobilePhone</td>\n",
       "      <td>2014/02/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>10002</td>\n",
       "      <td>2014/02/26</td>\n",
       "      <td>_qQ</td>\n",
       "      <td>2013/06/21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>10002</td>\n",
       "      <td>2014/02/26</td>\n",
       "      <td>_realName</td>\n",
       "      <td>2013/06/21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>10002</td>\n",
       "      <td>2014/02/26</td>\n",
       "      <td>_regStepId</td>\n",
       "      <td>2013/06/21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>10002</td>\n",
       "      <td>2014/02/26</td>\n",
       "      <td>_regStepId</td>\n",
       "      <td>2013/06/21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>10002</td>\n",
       "      <td>2014/02/26</td>\n",
       "      <td>_residenceAddress</td>\n",
       "      <td>2013/06/21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>10002</td>\n",
       "      <td>2014/02/26</td>\n",
       "      <td>_residencePhone</td>\n",
       "      <td>2013/06/21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372433</th>\n",
       "      <td>9995</td>\n",
       "      <td>2014/02/24</td>\n",
       "      <td>_EducationId</td>\n",
       "      <td>2014/02/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372434</th>\n",
       "      <td>9995</td>\n",
       "      <td>2014/02/24</td>\n",
       "      <td>_HasBuyCar</td>\n",
       "      <td>2014/02/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372435</th>\n",
       "      <td>9995</td>\n",
       "      <td>2014/02/24</td>\n",
       "      <td>_MarriageStatusId</td>\n",
       "      <td>2014/02/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372436</th>\n",
       "      <td>9995</td>\n",
       "      <td>2014/02/24</td>\n",
       "      <td>_MobilePhone</td>\n",
       "      <td>2014/02/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372437</th>\n",
       "      <td>9995</td>\n",
       "      <td>2014/02/24</td>\n",
       "      <td>_QQ</td>\n",
       "      <td>2014/02/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372438</th>\n",
       "      <td>9995</td>\n",
       "      <td>2014/02/24</td>\n",
       "      <td>_ResidenceAddress</td>\n",
       "      <td>2014/02/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372439</th>\n",
       "      <td>9995</td>\n",
       "      <td>2014/02/24</td>\n",
       "      <td>_ResidencePhone</td>\n",
       "      <td>2014/02/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372440</th>\n",
       "      <td>9995</td>\n",
       "      <td>2014/02/24</td>\n",
       "      <td>_ResidenceTypeId</td>\n",
       "      <td>2014/02/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372441</th>\n",
       "      <td>9995</td>\n",
       "      <td>2014/02/24</td>\n",
       "      <td>_ResidenceYears</td>\n",
       "      <td>2014/02/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372442</th>\n",
       "      <td>9997</td>\n",
       "      <td>2014/02/28</td>\n",
       "      <td>_EducationId</td>\n",
       "      <td>2014/02/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372443</th>\n",
       "      <td>9997</td>\n",
       "      <td>2014/02/28</td>\n",
       "      <td>_HasBuyCar</td>\n",
       "      <td>2014/02/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372444</th>\n",
       "      <td>9997</td>\n",
       "      <td>2014/02/28</td>\n",
       "      <td>_MarriageStatusId</td>\n",
       "      <td>2014/02/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372445</th>\n",
       "      <td>9997</td>\n",
       "      <td>2014/02/28</td>\n",
       "      <td>_MobilePhone</td>\n",
       "      <td>2014/02/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372446</th>\n",
       "      <td>9997</td>\n",
       "      <td>2014/02/28</td>\n",
       "      <td>_MobilePhone</td>\n",
       "      <td>2014/02/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372447</th>\n",
       "      <td>9997</td>\n",
       "      <td>2014/02/28</td>\n",
       "      <td>_QQ</td>\n",
       "      <td>2014/02/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372448</th>\n",
       "      <td>9997</td>\n",
       "      <td>2014/02/28</td>\n",
       "      <td>_ResidenceAddress</td>\n",
       "      <td>2014/02/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372449</th>\n",
       "      <td>9997</td>\n",
       "      <td>2014/02/28</td>\n",
       "      <td>_ResidencePhone</td>\n",
       "      <td>2014/02/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372450</th>\n",
       "      <td>9997</td>\n",
       "      <td>2014/02/28</td>\n",
       "      <td>_ResidenceTypeId</td>\n",
       "      <td>2014/02/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372451</th>\n",
       "      <td>9997</td>\n",
       "      <td>2014/02/28</td>\n",
       "      <td>_ResidenceYears</td>\n",
       "      <td>2014/02/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372452</th>\n",
       "      <td>9998</td>\n",
       "      <td>2014/03/05</td>\n",
       "      <td>_EducationId</td>\n",
       "      <td>2014/02/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372453</th>\n",
       "      <td>9998</td>\n",
       "      <td>2014/03/05</td>\n",
       "      <td>_EducationId</td>\n",
       "      <td>2014/02/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372454</th>\n",
       "      <td>9998</td>\n",
       "      <td>2014/03/05</td>\n",
       "      <td>_HasBuyCar</td>\n",
       "      <td>2014/02/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372455</th>\n",
       "      <td>9998</td>\n",
       "      <td>2014/03/05</td>\n",
       "      <td>_MarriageStatusId</td>\n",
       "      <td>2014/02/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372456</th>\n",
       "      <td>9998</td>\n",
       "      <td>2014/03/05</td>\n",
       "      <td>_MobilePhone</td>\n",
       "      <td>2014/02/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372457</th>\n",
       "      <td>9998</td>\n",
       "      <td>2014/03/05</td>\n",
       "      <td>_MobilePhone</td>\n",
       "      <td>2014/02/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372458</th>\n",
       "      <td>9998</td>\n",
       "      <td>2014/03/05</td>\n",
       "      <td>_QQ</td>\n",
       "      <td>2014/02/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372459</th>\n",
       "      <td>9998</td>\n",
       "      <td>2014/03/05</td>\n",
       "      <td>_ResidenceAddress</td>\n",
       "      <td>2014/02/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372460</th>\n",
       "      <td>9998</td>\n",
       "      <td>2014/03/05</td>\n",
       "      <td>_ResidencePhone</td>\n",
       "      <td>2014/02/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372461</th>\n",
       "      <td>9998</td>\n",
       "      <td>2014/03/05</td>\n",
       "      <td>_ResidenceTypeId</td>\n",
       "      <td>2014/02/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372462</th>\n",
       "      <td>9998</td>\n",
       "      <td>2014/03/05</td>\n",
       "      <td>_ResidenceYears</td>\n",
       "      <td>2014/02/20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>372463 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Idx ListingInfo1    UserupdateInfo1 UserupdateInfo2\n",
       "0       10001   2014/03/05       _EducationId      2014/02/20\n",
       "1       10001   2014/03/05         _HasBuyCar      2014/02/20\n",
       "2       10001   2014/03/05    _LastUpdateDate      2014/02/20\n",
       "3       10001   2014/03/05  _MarriageStatusId      2014/02/20\n",
       "4       10001   2014/03/05       _MobilePhone      2014/02/20\n",
       "5       10001   2014/03/05       _MobilePhone      2014/02/20\n",
       "6       10001   2014/03/05                _QQ      2014/02/20\n",
       "7       10001   2014/03/05  _ResidenceAddress      2014/02/20\n",
       "8       10001   2014/03/05    _ResidencePhone      2014/02/20\n",
       "9       10001   2014/03/05   _ResidenceTypeId      2014/02/20\n",
       "10      10001   2014/03/05    _ResidenceYears      2014/02/20\n",
       "11      10002   2014/02/26               _age      2013/06/21\n",
       "12      10002   2014/02/26       _educationId      2013/06/21\n",
       "13      10002   2014/02/26            _gender      2013/06/21\n",
       "14      10002   2014/02/26         _hasBuyCar      2013/06/21\n",
       "15      10002   2014/02/26          _idNumber      2013/06/21\n",
       "16      10002   2014/02/26    _lastUpdateDate      2013/06/21\n",
       "17      10002   2014/02/26    _lastUpdateDate      2013/07/08\n",
       "18      10002   2014/02/26    _lastUpdateDate      2013/07/08\n",
       "19      10002   2014/02/26    _lastUpdateDate      2013/07/08\n",
       "20      10002   2014/02/26    _lastUpdateDate      2013/07/08\n",
       "21      10002   2014/02/26  _marriageStatusId      2013/06/21\n",
       "22      10002   2014/02/26       _mobilePhone      2013/06/21\n",
       "23      10002   2014/02/26       _MobilePhone      2014/02/20\n",
       "24      10002   2014/02/26                _qQ      2013/06/21\n",
       "25      10002   2014/02/26          _realName      2013/06/21\n",
       "26      10002   2014/02/26         _regStepId      2013/06/21\n",
       "27      10002   2014/02/26         _regStepId      2013/06/21\n",
       "28      10002   2014/02/26  _residenceAddress      2013/06/21\n",
       "29      10002   2014/02/26    _residencePhone      2013/06/21\n",
       "...       ...          ...                ...             ...\n",
       "372433   9995   2014/02/24       _EducationId      2014/02/20\n",
       "372434   9995   2014/02/24         _HasBuyCar      2014/02/20\n",
       "372435   9995   2014/02/24  _MarriageStatusId      2014/02/20\n",
       "372436   9995   2014/02/24       _MobilePhone      2014/02/20\n",
       "372437   9995   2014/02/24                _QQ      2014/02/20\n",
       "372438   9995   2014/02/24  _ResidenceAddress      2014/02/20\n",
       "372439   9995   2014/02/24    _ResidencePhone      2014/02/20\n",
       "372440   9995   2014/02/24   _ResidenceTypeId      2014/02/20\n",
       "372441   9995   2014/02/24    _ResidenceYears      2014/02/20\n",
       "372442   9997   2014/02/28       _EducationId      2014/02/20\n",
       "372443   9997   2014/02/28         _HasBuyCar      2014/02/20\n",
       "372444   9997   2014/02/28  _MarriageStatusId      2014/02/20\n",
       "372445   9997   2014/02/28       _MobilePhone      2014/02/20\n",
       "372446   9997   2014/02/28       _MobilePhone      2014/02/20\n",
       "372447   9997   2014/02/28                _QQ      2014/02/20\n",
       "372448   9997   2014/02/28  _ResidenceAddress      2014/02/20\n",
       "372449   9997   2014/02/28    _ResidencePhone      2014/02/20\n",
       "372450   9997   2014/02/28   _ResidenceTypeId      2014/02/20\n",
       "372451   9997   2014/02/28    _ResidenceYears      2014/02/20\n",
       "372452   9998   2014/03/05       _EducationId      2014/02/20\n",
       "372453   9998   2014/03/05       _EducationId      2014/02/20\n",
       "372454   9998   2014/03/05         _HasBuyCar      2014/02/20\n",
       "372455   9998   2014/03/05  _MarriageStatusId      2014/02/20\n",
       "372456   9998   2014/03/05       _MobilePhone      2014/02/20\n",
       "372457   9998   2014/03/05       _MobilePhone      2014/02/20\n",
       "372458   9998   2014/03/05                _QQ      2014/02/20\n",
       "372459   9998   2014/03/05  _ResidenceAddress      2014/02/20\n",
       "372460   9998   2014/03/05    _ResidencePhone      2014/02/20\n",
       "372461   9998   2014/03/05   _ResidenceTypeId      2014/02/20\n",
       "372462   9998   2014/03/05    _ResidenceYears      2014/02/20\n",
       "\n",
       "[372463 rows x 4 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_userUpdate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 合并数据\n",
    "#### pd.concat()\n",
    "- 常用参数\n",
    "    - objs：连接对象（多个Series、DateFrame间）\n",
    "    - axis=0：默认0，对x轴操作，列对齐；1，行对齐\n",
    "    - join='outer'：默认outer，对应轴向上索引按并集进行合并，inner按交集合并\n",
    "    - join_axes：指定按照某df的index/columns合并。axis=0时，可以join_axes=[df3.columns]；axis=1时，可join_axes=[df3.index]\n",
    "    - ignore_index=False：是否不保留连接轴上原先索引\n",
    "    - vertify_integrity=False：是否检查新的连接轴包含重复项，如果True且包含则异常\n",
    "    - **以下三个参数只在ignore_index=False下适用**\n",
    "    - keys：为原始dataframe添加一个键，用来构成层次化索引\n",
    "    - levels：不太懂？\n",
    "    - names：设置keys/levels后指定分层级别的名称\n",
    "\n",
    "#### df.append()列对齐合并数据\n",
    "- 常用参数\n",
    "    - obj：连接对象（Series、DateFrame）\n",
    "    - ignore_index=False：是否不保留连接轴上原先索引\n",
    "    - vertify_integrity=False：是否检查新的连接轴包含重复项，如果True且包含则异常\n",
    "\n",
    "#### pd.merge()主键合并\n",
    "> pd.merge(left, right, how='inner', on=None, left_on=None, right_on=None,\n",
    "         left_index=False, right_index=False, sort=True,\n",
    "         suffixes=('_x', '_y'), copy=True, indicator=False)\n",
    "\n",
    "- 常用参数\n",
    "    - left，right：待合并的df1，df2\n",
    "    - how：连接方式\n",
    "        - inner、outer：内/外连\n",
    "        - left、right：左连/右连\n",
    "    - on：数据合并的主键（必须一致）\n",
    "    > 指定连接的列名，若两列希望连接的列名不一样，可以通过left_on和right_on 来具体指定,不指定时pandas会自动找到相同名字的列\n",
    "    - left_on/right_on：left/right用以合并的主键\n",
    "    - sort：合并后数据是否排序\n",
    "    - suffixes=('_x','_y')：left、right中列名相同时追加后缀\n",
    "    \n",
    "#### df.join()类似merge\n",
    "- 常用参数\n",
    "    - obj：连接对象（Series、df或多个df的list）\n",
    "    - on：指定连接列名\n",
    "    - hoe='left'：连接方式，同样四种\n",
    "    - lsuffix=''：追加到左侧重叠列名的后缀\n",
    "    - rsuffix=''：与上面相反\n",
    "    - sort\n",
    "    \n",
    "不指定on时，会以df的index为依据合并。如df.join(df1,how='left')，则以df的index为主键合并；how='right'，则以df1的index为依据；outter就是二者index并集；inner是交集\n",
    "\n",
    "#### df.combine_first(df1)重叠合并\n",
    "用于当两张表数据内容几乎一致时，一张表的部分数据缺失，但在另一张表上有，此时可用来重叠合并数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>letter</th>\n",
       "      <th>number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  letter  number\n",
       "0      a       1\n",
       "1      b       2"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.DataFrame([['a',1],['b',2]],columns=['letter','number'])\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>letter</th>\n",
       "      <th>number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>c</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>d</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  letter  number\n",
       "0      c       3\n",
       "1      d       4"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.DataFrame([['c',3],['d',4]],columns=['letter','number'])\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>letter</th>\n",
       "      <th>number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>c</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>d</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  letter  number\n",
       "0      a       1\n",
       "1      b       2\n",
       "0      c       3\n",
       "1      d       4"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([df1,df2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>letter</th>\n",
       "      <th>number</th>\n",
       "      <th>letter</th>\n",
       "      <th>number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>1</td>\n",
       "      <td>c</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b</td>\n",
       "      <td>2</td>\n",
       "      <td>d</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  letter  number letter  number\n",
       "0      a       1      c       3\n",
       "1      b       2      d       4"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#axis=1行对齐\n",
    "pd.concat([df1,df2],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>letter</th>\n",
       "      <th>number</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>df name</th>\n",
       "      <th>index id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">A</th>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">B</th>\n",
       "      <th>0</th>\n",
       "      <td>c</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>d</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 letter  number\n",
       "df name index id               \n",
       "A       0             a       1\n",
       "        1             b       2\n",
       "B       0             c       3\n",
       "        1             d       4"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = pd.concat([df1,df2],keys=['A','B'],names=['df name','index id'])\n",
    "temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>letter</th>\n",
       "      <th>number</th>\n",
       "      <th>animal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>c</td>\n",
       "      <td>3</td>\n",
       "      <td>cat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>d</td>\n",
       "      <td>4</td>\n",
       "      <td>dog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>e</td>\n",
       "      <td>5</td>\n",
       "      <td>bird</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  letter  number animal\n",
       "0      c       3    cat\n",
       "1      d       4    dog\n",
       "2      e       5   bird"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = pd.DataFrame([['c', 3, 'cat'], ['d', 4, 'dog'],['e',5,'bird']],columns=['letter', 'number', 'animal'])\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>animal</th>\n",
       "      <th>letter</th>\n",
       "      <th>number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>a</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>b</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>cat</td>\n",
       "      <td>c</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>dog</td>\n",
       "      <td>d</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bird</td>\n",
       "      <td>e</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  animal letter  number\n",
       "0    NaN      a       1\n",
       "1    NaN      b       2\n",
       "0    cat      c       3\n",
       "1    dog      d       4\n",
       "2   bird      e       5"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#join='outer'外联，连接轴索引求并集\n",
    "pd.concat([df1,df3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>letter</th>\n",
       "      <th>number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>c</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>d</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>e</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  letter  number\n",
       "0      a       1\n",
       "1      b       2\n",
       "0      c       3\n",
       "1      d       4\n",
       "2      e       5"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#inner内联，索引求交集\n",
    "pd.concat([df1,df3],join='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>animal</th>\n",
       "      <th>letter</th>\n",
       "      <th>number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>a</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>b</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>cat</td>\n",
       "      <td>c</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>dog</td>\n",
       "      <td>d</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>bird</td>\n",
       "      <td>e</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  animal letter  number\n",
       "0    NaN      a       1\n",
       "1    NaN      b       2\n",
       "2    cat      c       3\n",
       "3    dog      d       4\n",
       "4   bird      e       5"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([df1,df3],ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>letter</th>\n",
       "      <th>number</th>\n",
       "      <th>letter</th>\n",
       "      <th>number</th>\n",
       "      <th>animal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>1.0</td>\n",
       "      <td>c</td>\n",
       "      <td>3</td>\n",
       "      <td>cat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b</td>\n",
       "      <td>2.0</td>\n",
       "      <td>d</td>\n",
       "      <td>4</td>\n",
       "      <td>dog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>e</td>\n",
       "      <td>5</td>\n",
       "      <td>bird</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  letter  number letter  number animal\n",
       "0      a     1.0      c       3    cat\n",
       "1      b     2.0      d       4    dog\n",
       "2    NaN     NaN      e       5   bird"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([df1,df3],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>letter</th>\n",
       "      <th>number</th>\n",
       "      <th>letter</th>\n",
       "      <th>number</th>\n",
       "      <th>animal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>1</td>\n",
       "      <td>c</td>\n",
       "      <td>3</td>\n",
       "      <td>cat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b</td>\n",
       "      <td>2</td>\n",
       "      <td>d</td>\n",
       "      <td>4</td>\n",
       "      <td>dog</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  letter  number letter  number animal\n",
       "0      a       1      c       3    cat\n",
       "1      b       2      d       4    dog"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#指定按照df1.index合并\n",
    "pd.concat([df1,df3],axis=1,join_axes=[df1.index])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### append()\n",
    "**列对齐，横向和纵向同时扩充，不考虑columns和index**\n",
    "![img](https://pic1.zhimg.com/80/v2-ef48526edc6a5c061b07e87f4cdcbfa2_hd.jpg)\n",
    "df1.append(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>letter</th>\n",
       "      <th>number</th>\n",
       "      <th>animal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>c</td>\n",
       "      <td>3</td>\n",
       "      <td>cat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>d</td>\n",
       "      <td>4</td>\n",
       "      <td>dog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>e</td>\n",
       "      <td>5</td>\n",
       "      <td>bird</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  letter  number animal\n",
       "0      c       3    cat\n",
       "1      d       4    dog\n",
       "2      e       5   bird"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>animal</th>\n",
       "      <th>letter</th>\n",
       "      <th>number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>a</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>b</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>cat</td>\n",
       "      <td>c</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>dog</td>\n",
       "      <td>d</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bird</td>\n",
       "      <td>e</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  animal letter  number\n",
       "0    NaN      a       1\n",
       "1    NaN      b       2\n",
       "0    cat      c       3\n",
       "1    dog      d       4\n",
       "2   bird      e       5"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4 = df1.append(df3)\n",
    "df4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>animal</th>\n",
       "      <th>letter</th>\n",
       "      <th>number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>a</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>b</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>a</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>b</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>cat</td>\n",
       "      <td>c</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>dog</td>\n",
       "      <td>d</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>bird</td>\n",
       "      <td>e</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>cat</td>\n",
       "      <td>c</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>dog</td>\n",
       "      <td>d</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>bird</td>\n",
       "      <td>e</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  animal letter  number\n",
       "0    NaN      a       1\n",
       "1    NaN      b       2\n",
       "2    NaN      a       1\n",
       "3    NaN      b       2\n",
       "4    cat      c       3\n",
       "5    dog      d       4\n",
       "6   bird      e       5\n",
       "7    cat      c       3\n",
       "8    dog      d       4\n",
       "9   bird      e       5"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.append([df4,df3],ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### merge\n",
    "**主键合并**\n",
    "![](https://img-blog.csdn.net/20180811001121870?watermark/2/text/aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2x6dzIwMTY=/font/5a6L5L2T/fontsize/400/fill/I0JBQkFCMA==/dissolve/70)\n",
    "![](https://img-blog.csdn.net/20180811001121870?watermark/2/text/aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2x6dzIwMTY=/font/5a6L5L2T/fontsize/400/fill/I0JBQkFCMA==/dissolve/70)\n",
    "![](https://img-blog.csdn.net/20180811001137952?watermark/2/text/aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2x6dzIwMTY=/font/5a6L5L2T/fontsize/400/fill/I0JBQkFCMA==/dissolve/70)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据清洗\n",
    "针对数据重复值、缺失值、异常值\n",
    "\n",
    "#### 去重\n",
    "##### 记录重复\n",
    "> 记录重复：一列中相同数据重复出现\n",
    "\n",
    "- 一般手段使用list、set去重\n",
    "    - list代码量大、set会改变排列顺序\n",
    "- pandas提供了drop_duplicates()方法\n",
    "    - 常用参数\n",
    "        - subset：接收str，指明要去重的列。默认None，表示全部列\n",
    "        - keep='first'：重复时保留第几个数据\n",
    "            - first/lasr：第一/最后一个\n",
    "            - false：都不保留\n",
    "        - inplace=False：是否在原表上修改\n",
    "        \n",
    "##### 特征重复\n",
    "> 什么是特征重复？就是两（多）列数据完全一样，只是列名不一样\n",
    "\n",
    "- 通过相似度矩阵去重（只针对数值型）\n",
    "    1. 计算相似度\n",
    "    \n",
    "    使用pandas提供的corr(method='pearson')方法，method还支持spearman、kendall法\n",
    "    2. 删除相似度为1的特征（保留一个即可）\n",
    "\n",
    "- 通过遍历，用equals()方法去比较每个单元格\n",
    "    1. 构建以df.columns为行列索引的二维矩阵\n",
    "    2. 遍历比较\n",
    "    3. 遍历删除"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "path = '../data/detail.csv'\n",
    "detail = pd.read_csv(path,encoding='gbk')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>detail_id</th>\n",
       "      <th>order_id</th>\n",
       "      <th>dishes_id</th>\n",
       "      <th>logicprn_name</th>\n",
       "      <th>parent_class_name</th>\n",
       "      <th>dishes_name</th>\n",
       "      <th>itemis_add</th>\n",
       "      <th>counts</th>\n",
       "      <th>amounts</th>\n",
       "      <th>cost</th>\n",
       "      <th>place_order_time</th>\n",
       "      <th>discount_amt</th>\n",
       "      <th>discount_reason</th>\n",
       "      <th>kick_back</th>\n",
       "      <th>add_inprice</th>\n",
       "      <th>add_info</th>\n",
       "      <th>bar_code</th>\n",
       "      <th>picture_file</th>\n",
       "      <th>emp_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2956</td>\n",
       "      <td>417</td>\n",
       "      <td>610062</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>蒜蓉生蚝</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>49</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016/8/111:05:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>caipu/104001.jpg</td>\n",
       "      <td>1442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2958</td>\n",
       "      <td>417</td>\n",
       "      <td>609957</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>蒙古烤羊腿</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016/8/111:07:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>caipu/202003.jpg</td>\n",
       "      <td>1442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2961</td>\n",
       "      <td>417</td>\n",
       "      <td>609950</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>大蒜苋菜</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016/8/111:07:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>caipu/303001.jpg</td>\n",
       "      <td>1442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2966</td>\n",
       "      <td>417</td>\n",
       "      <td>610038</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>芝麻烤紫菜</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016/8/111:11:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>caipu/105002.jpg</td>\n",
       "      <td>1442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2968</td>\n",
       "      <td>417</td>\n",
       "      <td>610003</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>蒜香包</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016/8/111:11:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>caipu/503002.jpg</td>\n",
       "      <td>1442</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   detail_id  order_id  dishes_id  logicprn_name  parent_class_name  \\\n",
       "0       2956       417     610062            NaN                NaN   \n",
       "1       2958       417     609957            NaN                NaN   \n",
       "2       2961       417     609950            NaN                NaN   \n",
       "3       2966       417     610038            NaN                NaN   \n",
       "4       2968       417     610003            NaN                NaN   \n",
       "\n",
       "  dishes_name  itemis_add  counts  amounts  cost  place_order_time  \\\n",
       "0        蒜蓉生蚝           0       1       49   NaN  2016/8/111:05:00   \n",
       "1       蒙古烤羊腿           0       1       48   NaN  2016/8/111:07:00   \n",
       "2        大蒜苋菜           0       1       30   NaN  2016/8/111:07:00   \n",
       "3       芝麻烤紫菜           0       1       25   NaN  2016/8/111:11:00   \n",
       "4         蒜香包           0       1       13   NaN  2016/8/111:11:00   \n",
       "\n",
       "   discount_amt  discount_reason  kick_back  add_inprice  add_info  bar_code  \\\n",
       "0           NaN              NaN        NaN            0       NaN       NaN   \n",
       "1           NaN              NaN        NaN            0       NaN       NaN   \n",
       "2           NaN              NaN        NaN            0       NaN       NaN   \n",
       "3           NaN              NaN        NaN            0       NaN       NaN   \n",
       "4           NaN              NaN        NaN            0       NaN       NaN   \n",
       "\n",
       "       picture_file  emp_id  \n",
       "0  caipu/104001.jpg    1442  \n",
       "1  caipu/202003.jpg    1442  \n",
       "2  caipu/303001.jpg    1442  \n",
       "3  caipu/105002.jpg    1442  \n",
       "4  caipu/503002.jpg    1442  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "detail.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10037,)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dishes_name = detail['dishes_name']\n",
    "dishes_name.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(942, 19)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dishes_name1 = detail.drop_duplicates(subset=['order_id','emp_id'])\n",
    "dishes_name1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           counts   amounts\n",
      "counts   1.000000 -0.159264\n",
      "amounts -0.159264  1.000000\n"
     ]
    }
   ],
   "source": [
    "corrDet = detail[['counts','amounts']].corr(method='pearson')\n",
    "print(corrDet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "销量和售价的kendall相似度为：\n",
      "            counts   amounts\n",
      "counts   1.000000 -0.229968\n",
      "amounts -0.229968  1.000000\n"
     ]
    }
   ],
   "source": [
    "# from scipy.stats import kendalltau, spearmanr\n",
    "corrDet = detail[['counts','amounts']].corr(method='kendall')\n",
    "print('销量和售价的kendall相似度为：\\n',corrDet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "detail的特征相等矩阵的前5行5列为：\n",
      "                    detail_id  order_id  dishes_id  logicprn_name  \\\n",
      "detail_id               True     False      False          False   \n",
      "order_id               False      True      False          False   \n",
      "dishes_id              False     False       True          False   \n",
      "logicprn_name          False     False      False           True   \n",
      "parent_class_name      False     False      False           True   \n",
      "\n",
      "                   parent_class_name  \n",
      "detail_id                      False  \n",
      "order_id                       False  \n",
      "dishes_id                      False  \n",
      "logicprn_name                   True  \n",
      "parent_class_name               True  \n"
     ]
    }
   ],
   "source": [
    "##定义求取特征是否完全相同的矩阵的函数\n",
    "def FeatureEquals(df):\n",
    "    dfEquals=pd.DataFrame([],columns=df.columns,index=df.columns)\n",
    "    for i in df.columns:\n",
    "       for j in df.columns:\n",
    "           dfEquals.loc[i,j]=df.loc[:,i].equals(df.loc[:,j])\n",
    "    return dfEquals\n",
    "## 应用上述函数\n",
    "detEquals=FeatureEquals(detail)\n",
    "print('detail的特征相等矩阵的前5行5列为：\\n',detEquals.iloc[:5,:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "需要删除的列为： ['parent_class_name', 'cost', 'discount_amt', 'discount_reason', 'kick_back', 'add_info', 'bar_code', 'add_inprice']\n",
      "删除多余列后detail的特征数目为： 11\n"
     ]
    }
   ],
   "source": [
    "##遍历所有数据\n",
    "lenDet = detEquals.shape[0]\n",
    "dupCol = []\n",
    "for k in range(lenDet):\n",
    "    for l in range(k+1,lenDet): ##明显detEquals[k,k]是自己\n",
    "        if detEquals.iloc[k,l] & (detEquals.columns[l] not in dupCol):\n",
    "            dupCol.append(detEquals.columns[l])\n",
    "##进行去重操作\n",
    "print('需要删除的列为：',dupCol)\n",
    "detail.drop(dupCol,axis=1,inplace=True)\n",
    "print('删除多余列后detail的特征数目为：',detail.shape[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 处理缺失值\n",
    "##### 检测缺失值\n",
    "- df.isnull/notnull()方法\n",
    "\n",
    "##### 处理缺失值\n",
    "- 删除法\n",
    "    - df.dropna(axis=0,how='any',inplace=False,subset=None)\n",
    "    - axis为0删除行，为1删除列\n",
    "    - how：any表示只要存在缺失值就执行删除操作，all表示行/列全为缺失值才执行删除\n",
    "    - inplace：是否修改原表\n",
    "    - subset：表示去重的列/行，默认所有\n",
    "    \n",
    "- 替换法\n",
    "    - df.fillna(value,method=None,axis=1,inplace=False,limit=None)\n",
    "    - value：替换值\n",
    "    - method：接收特定str。bfill/ffill表示使用下/上一个非缺失值来填补缺失\n",
    "    - limit：表示替换缺失值个数上限\n",
    "替换缺失值选什么值？\n",
    "替换数值型特征值最好选择平均值、中位数、众数等的代表集中趋势的统计量；要是类别型的特征值则选择众数\n",
    "\n",
    "- 插值法\n",
    "> 删除法会影响数据结构变动，样本减少，替换法会影响数据标准差\n",
    "\n",
    "    - 线性插值：通过已知值求解线性方程来得出缺失值\n",
    "    - 多项式插值：利用已经知道的值拟合一个多项式，再求出缺失值；常用拉格朗日插值、牛顿插值\n",
    "    - 样条插值：同样是拟合多项式，但保证两个邻接多项式在邻接处的倒数相等\n",
    "```python\n",
    "from scipy.interpolate import interpld\n",
    "from scipy.interpolate import lagrange\n",
    "from scipy.interpolate import spline\n",
    "##分别用于线性、拉格朗日、样条拟合\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "detail_id               0\n",
       "order_id                0\n",
       "dishes_id               0\n",
       "logicprn_name       10037\n",
       "dishes_name             0\n",
       "itemis_add              0\n",
       "counts                  0\n",
       "amounts                 0\n",
       "place_order_time        0\n",
       "picture_file            0\n",
       "emp_id                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#detail已经删除了重复特征列\n",
    "detail.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "detail_id           10037\n",
       "order_id            10037\n",
       "dishes_id           10037\n",
       "logicprn_name           0\n",
       "dishes_name         10037\n",
       "itemis_add          10037\n",
       "counts              10037\n",
       "amounts             10037\n",
       "place_order_time    10037\n",
       "picture_file        10037\n",
       "emp_id              10037\n",
       "dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "detail.notnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10037, 10)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "detail.dropna(axis=1,how='all').shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "10037"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(detail['logicprn_name'].fillna('不知道').isnull().sum())\n",
    "detail['logicprn_name'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>detail_id</th>\n",
       "      <th>order_id</th>\n",
       "      <th>dishes_id</th>\n",
       "      <th>logicprn_name</th>\n",
       "      <th>dishes_name</th>\n",
       "      <th>itemis_add</th>\n",
       "      <th>counts</th>\n",
       "      <th>amounts</th>\n",
       "      <th>place_order_time</th>\n",
       "      <th>picture_file</th>\n",
       "      <th>emp_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2956</td>\n",
       "      <td>417</td>\n",
       "      <td>610062</td>\n",
       "      <td>NaN</td>\n",
       "      <td>蒜蓉生蚝</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>49</td>\n",
       "      <td>2016/8/111:05:00</td>\n",
       "      <td>caipu/104001.jpg</td>\n",
       "      <td>1442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2958</td>\n",
       "      <td>417</td>\n",
       "      <td>609957</td>\n",
       "      <td>NaN</td>\n",
       "      <td>蒙古烤羊腿</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>2016/8/111:07:00</td>\n",
       "      <td>caipu/202003.jpg</td>\n",
       "      <td>1442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2961</td>\n",
       "      <td>417</td>\n",
       "      <td>609950</td>\n",
       "      <td>NaN</td>\n",
       "      <td>大蒜苋菜</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>2016/8/111:07:00</td>\n",
       "      <td>caipu/303001.jpg</td>\n",
       "      <td>1442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2966</td>\n",
       "      <td>417</td>\n",
       "      <td>610038</td>\n",
       "      <td>NaN</td>\n",
       "      <td>芝麻烤紫菜</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>2016/8/111:11:00</td>\n",
       "      <td>caipu/105002.jpg</td>\n",
       "      <td>1442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2968</td>\n",
       "      <td>417</td>\n",
       "      <td>610003</td>\n",
       "      <td>NaN</td>\n",
       "      <td>蒜香包</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>2016/8/111:11:00</td>\n",
       "      <td>caipu/503002.jpg</td>\n",
       "      <td>1442</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   detail_id  order_id  dishes_id  logicprn_name dishes_name  itemis_add  \\\n",
       "0       2956       417     610062            NaN        蒜蓉生蚝           0   \n",
       "1       2958       417     609957            NaN       蒙古烤羊腿           0   \n",
       "2       2961       417     609950            NaN        大蒜苋菜           0   \n",
       "3       2966       417     610038            NaN       芝麻烤紫菜           0   \n",
       "4       2968       417     610003            NaN         蒜香包           0   \n",
       "\n",
       "   counts  amounts  place_order_time      picture_file  emp_id  \n",
       "0       1       49  2016/8/111:05:00  caipu/104001.jpg    1442  \n",
       "1       1       48  2016/8/111:07:00  caipu/202003.jpg    1442  \n",
       "2       1       30  2016/8/111:07:00  caipu/303001.jpg    1442  \n",
       "3       1       25  2016/8/111:11:00  caipu/105002.jpg    1442  \n",
       "4       1       13  2016/8/111:11:00  caipu/503002.jpg    1442  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "detail.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x=6时，y1=  76.0\n",
      "x=6,7时，y2= [ 13.  15.]\n",
      "x=6,7时，y1= [ 72.  98.]\n",
      "x=6,7时，y2= [ 13.  15.]\n",
      "x=6,7时，y= [ 72.  98.]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\python36\\lib\\site-packages\\ipykernel_launcher.py:24: DeprecationWarning: `spline` is deprecated!\n",
      "spline is deprecated in scipy 0.19.0, use Bspline class instead.\n"
     ]
    }
   ],
   "source": [
    "from scipy.interpolate import interp1d\n",
    "\n",
    "x = np.array([1,2,3,4,5,8,9,10])\n",
    "y1 = np.array([2,8,18,32,50,128,162,200])\n",
    "y2 = np.array([3,5,7,9,11,17,19,21])\n",
    "\n",
    "##线性拟合x，y1\n",
    "LinerValue1 = interp1d(x,y1,kind='linear')\n",
    "##线性拟合x，y2\n",
    "LinerValue2 = interp1d(x,y2,kind='linear')\n",
    "print('x=6时，y1= ',LinerValue1(6))\n",
    "print('x=6,7时，y2=',LinerValue2([6,7]))\n",
    "\n",
    "from scipy.interpolate import lagrange\n",
    "##拉格朗日拟合\n",
    "LargeValue1 = lagrange(x,y1)\n",
    "LargeValue2 = lagrange(x,y2)\n",
    "print('x=6,7时，y1=',LargeValue1([6,7]))\n",
    "print('x=6,7时，y2=',LargeValue2([6,7]))\n",
    "\n",
    "\n",
    "from scipy.interpolate import spline\n",
    "##样条插值拟合\n",
    "SplineValue1 = spline(x,y1,xnew=np.array([6,7]))\n",
    "print('x=6,7时，y=',SplineValue1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x=6,7时，y1= [ 72.  98.]\n",
      "x=6,7时，y2= [ 13.  15.]\n"
     ]
    }
   ],
   "source": [
    "from scipy.interpolate import lagrange\n",
    "##拉格朗日拟合\n",
    "LargeValue1 = lagrange(x,y1)\n",
    "LargeValue2 = lagrange(x,y2)\n",
    "print('x=6,7时，y1=',LargeValue1([6,7]))\n",
    "print('x=6,7时，y2=',LargeValue2([6,7]))\n",
    "\n",
    "\n",
    "from scipy.interpolate import spline\n",
    "##样条插值拟合\n",
    "SplineValue1 = spline(x,y1,xnew=np.array([6,7]))\n",
    "print('x=6,7时，y=',SplineValue1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 异常值处理\n",
    "- 用于正态分布、近似正态分布的3σ原则\n",
    "- 箱线图分析（客观常用）\n",
    "> 箱线图异常值标准：小于QL-1.5*IQR，大于QU+1.5*IQR\n",
    "\n",
    "    - QL：下四分位数，观察值样本有四分之一数据比他小\n",
    "    - QU：上四分位数，观察值样本有四分之一比他大\n",
    "    - IQR：四分位数间距（QU-QL）\n",
    "\n",
    "QU/QL如何求？\n",
    "df的describee方法中的75%、25%就是，如 `detail['amounts'].describe().loc['75%']`\n",
    "也可通过Series的quantile方法，如 `detail['counts'].quantile(0.75)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>counts</th>\n",
       "      <th>amounts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10037.000000</td>\n",
       "      <td>10037.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.108499</td>\n",
       "      <td>44.821361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.611016</td>\n",
       "      <td>35.815435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>35.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>56.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>178.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             counts       amounts\n",
       "count  10037.000000  10037.000000\n",
       "mean       1.108499     44.821361\n",
       "std        0.611016     35.815435\n",
       "min        1.000000      1.000000\n",
       "25%        1.000000     25.000000\n",
       "50%        1.000000     35.000000\n",
       "75%        1.000000     56.000000\n",
       "max       10.000000    178.000000"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "detail[['counts','amounts']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "detail['counts'].quantile(0.75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "56.0"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "detail['amounts'].describe().loc['75%']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x224b530e048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "销售量数据异常值个数为： 516\n",
      "销售量数据异常值的最大值为： 10\n",
      "销售量数据异常值的最小值为： 2\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(10,8)) \n",
    "p = plt.boxplot(detail['counts'].values,notch=True)   ##画出箱线图\n",
    "outlier1 = p['fliers'][0].get_ydata()   ##fliers为异常值的标签\n",
    "plt.savefig('../菜品异常数据识别_箱线图.png')\n",
    "plt.show()\n",
    "print('销售量数据异常值个数为：',len(outlier1))\n",
    "print('销售量数据异常值的最大值为：',max(outlier1))\n",
    "print('销售量数据异常值的最小值为：',min(outlier1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\python36\\lib\\site-packages\\pandas\\core\\indexing.py:194: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "amount最大值： 99.0  ,amount最小值： 1.0\n"
     ]
    }
   ],
   "source": [
    "##箱线图异常值处理(替换法)\n",
    "def outRange(Series):\n",
    "    oldLen = Series.shape[0]\n",
    "    QL = Series.quantile(0.25)\n",
    "    QU = Series.quantile(0.75)\n",
    "    IQR = QU-QL\n",
    "    Series.loc[Series>(QU+1.5*IQR)] = QU#大于上四分位值替换成上四分位数值\n",
    "    Series.loc[(Series<(QL-1.5*IQR))] = QL\n",
    "    return Series\n",
    "detail['amounts'] = outRange(detail['amounts'])\n",
    "print('amount最大值：',detail['amounts'].max(),' ,amount最小值：',detail['amounts'].min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\python36\\lib\\site-packages\\pandas\\core\\indexing.py:194: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "808"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Series = detail['amounts']\n",
    "QL = Series.quantile(0.25)\n",
    "QU = Series.quantile(0.75)#56\n",
    "IQR = QU-QL\n",
    "# Series.loc[Series>(QU)]#1743个\n",
    "# Series.loc[Series==QU]#808\n",
    "Series.loc[Series>(QU+1.5*IQR)] = QU\n",
    "len(Series.loc[Series==QU])"
   ]
  }
 ],
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