{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "mQFbageR7eSe"
   },
   "source": [
    "# 3. Plotting for Exploratory data analysis (EDA)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "KRHB4cUi7eSg"
   },
   "source": [
    "# (3.1) Basic Terminology"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "EahE8lDl7eSj"
   },
   "source": [
    "* What is EDA?\n",
    "* Data-point/vector/Observation\n",
    "* Data-set.\n",
    "* Feature/Variable/Input-variable/Dependent-varibale\n",
    "* Label/Indepdendent-variable/Output-varible/Class/Class-label/Response label\n",
    "* Vector: 2-D, 3-D, 4-D,.... n-D\n",
    "\n",
    "Q. What is a 1-D vector: Scalar\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "IYm45v1y7eSj"
   },
   "source": [
    "## Iris Flower dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "iW8l_61b7eSk"
   },
   "source": [
    "Toy  Dataset: Iris Dataset: [https://en.wikipedia.org/wiki/Iris_flower_data_set]\n",
    "* A simple dataset to learn the basics.\n",
    "* 3 flowers of Iris species. [see images on wikipedia link above]\n",
    "* 1936 by Ronald Fisher.\n",
    "* Petal and Sepal: http://terpconnect.umd.edu/~petersd/666/html/iris_with_labels.jpg\n",
    "*  Objective: Classify a new flower as belonging to one of the 3 classes given the 4 features.\n",
    "* Importance of domain knowledge.\n",
    "* Why use petal and sepal dimensions as features?\n",
    "* Why do we not use 'color' as a feature?\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "p2oKsGVW7eSm"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "'''downlaod iris.csv from https://raw.githubusercontent.com/uiuc-cse/data-fa14/gh-pages/data/iris.csv'''\n",
    "#Load Iris.csv into a pandas dataFrame.\n",
    "iris = pd.read_csv(\"iris.csv\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "qs8rT4Up7eSz",
    "outputId": "376f286f-2e9e-4df1-d145-aba2dc4b5c3c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(150, 5)\n"
     ]
    }
   ],
   "source": [
    "# (Q) how many data-points and features?\n",
    "print (iris.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3RT2Gdlq7eTC",
    "outputId": "9e162731-b25e-4169-97eb-a66b9c6df076",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['sepal_length', 'sepal_width', 'petal_length', 'petal_width',\n",
      "       'species'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "#(Q) What are the column names in our dataset?\n",
    "print (iris.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4ARdqlVj7eTH",
    "outputId": "c8b7c898-35af-4328-e91e-393d5ce361a1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "virginica     50\n",
       "setosa        50\n",
       "versicolor    50\n",
       "Name: species, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#(Q) How many data points for each class are present? \n",
    "#(or) How many flowers for each species are present?\n",
    "\n",
    "iris[\"species\"].value_counts()\n",
    "# balanced-dataset vs imbalanced datasets\n",
    "#Iris is a balanced dataset as the number of data points for every class is 50."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "uExhLJ1-7eTT"
   },
   "source": [
    "# (3.2) 2-D Scatter Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "m8fu7kKy7eTU",
    "outputId": "201c4f80-4071-4e22-db53-402547ccb725",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ioeL7bwPzJC2uOcw7gDsi4tri8Xpav4Any2E8O8aZyXhOeAVwXURsnea5HMZz\nwoxxZjKefwTcGhHjEfEY8DXgBVPWeWI8i8tNzwC2lX0DJ4YZRMS7I2JZRKykdVp5eUS0ZeUp10DX\n0vqQunaS9pS0YOJ74KXAz6esdgmwrpj9cQSt08+7cotT0rMmroVKOpzWMVr6gE4hIn4DbJG0f7Ho\nGGDzlNX6Pp5l4sxhPCf5b8x8eabv4znJjHFmMp63A0dIeloRyzE89XfPJcDriu9PoPX7q3TRmmcl\ndUnSWcBoRFwCnCZpLbADuBc4uU9hLQX+qThedwO+FBHflfRnABHxWeDbwCuBMeB3wOszjfME4M2S\ndgAPAyd2c0An9Dbgi8VlhV8Dr89wPMvEmcV4Fn8IvAT475OWZTeeJeLs+3hGxLWS1tO6rLUDuB44\nZ8rvps8BF0gao/W76cRu3sOVz2Zm1saXkszMrI0Tg5mZtXFiMDOzNk4MZmbWxonBzMzaODGYmVkb\nJwazLhS3XX7KLdgnPX+ypLMreN+TJT1n0uPb+ljBbAPOicGsGU6mdV8cs8o5MdjAKW698a3iDpg/\nl/RaSYdJ+mFxV9fvTdzORNIPJH1SraYrPy9uc4CkwyX9pLhr6Y8n3XaimziWSLpY0s+KryOL5e+V\n9PnivX8t6bRJr/lrSb+UdLVaDVjeIekEYIRWhfNGSXsUq79N0nVqNT76/Z4HzqzgxGCD6OXAv0XE\nwRHxfOC7tO6EeUJEHAZ8HnjfpPWfFhGrgbcUzwH8AjiquGvpmcD75xDHJ4GPR8R/BI6nva/H7wMv\no3UTwfdImidpYr2Dad3IbQQgItYDo8CfRMTqiHi42MY9xZ1qPwO8Yw7xmU3L90qyQXQj8FFJHwS+\nCdwHPB+4rLhP06607mM/4csAEXGlpKdLWggsAM6TtB+tHhLz5hDHHwEH6Mn+KE+XtFfx/beKO/M+\nKuluWveROhL454h4BHhE0jc6bP9rxb8bgNfMIT6zaTkx2MCJiF+p1TP4lcDfApcDN0XEmpleMs3j\n/wVcERF/rFa/3B/MIZRdgCOKX/RPKBLFo5MWPc7cfhYntjHX15tNy5eSbOAUs3d+FxEXAh+m1Tt4\niaQ1xfPz1N5567XF8hfSut3zA7TuXz/RD+DkOYZyKa27n07EtbrD+j8CXi1p9+LMYnJf4e20zmLM\nKue/MmwQHQh8WNJO4DHgzbRuT/wptfok70arbetNxfqPSLqe1uWiNxTLPkTrUtL/BL41xzhOA/5O\n0qbiPa/2Hb8nAAAAkUlEQVQE/mymlSPiZ5IuATYBW2ldEnugePoLwGclPQzMdOZjloRvu21DTdIP\ngHdExGi/YwGQtFdEPCTpabQSySkRcV2/47Lh4jMGs7ycI+kAYHfgPCcF6wefMZjNgaTXA6dPWfyj\niDi1H/GYpeTEYGZmbTwryczM2jgxmJlZGycGMzNr48RgZmZtnBjMzKzN/wfC6H2uVk+WVwAAAABJ\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106c74e48>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#2-D scatter plot:\n",
    "#ALWAYS understand the axis: labels and scale.\n",
    "\n",
    "iris.plot(kind='scatter', x='sepal_length', y='sepal_width') ;\n",
    "plt.show()\n",
    "\n",
    "#cannot make much sense out it. \n",
    "#What if we color the points by thier class-label/flower-type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "fCu1FPvO7eTc",
    "outputId": "f6c71a2f-0947-421c-a50a-221df66f4f8c",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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y08ux68rjrZc6Vqd/TcHYNmRlZeHYsWNmnZRm0Olr3bp1w7x585CQkIA5c+bghRdekHpc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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109b44f60>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2-D Scatter plot with color-coding for each flower type/class.\n",
    "# Here 'sns' corresponds to seaborn. \n",
    "sns.set_style(\"whitegrid\");\n",
    "sns.FacetGrid(iris, hue=\"species\", size=4) \\\n",
    "   .map(plt.scatter, \"sepal_length\", \"sepal_width\") \\\n",
    "   .add_legend();\n",
    "plt.show();\n",
    "\n",
    "# Notice that the blue points can be easily seperated \n",
    "# from red and green by drawing a line. \n",
    "# But red and green data points cannot be easily seperated.\n",
    "# Can we draw multiple 2-D scatter plots for each combination of features?\n",
    "# How many cobinations exist? 4C2 = 6."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "2L5YF3Tj7eTl"
   },
   "source": [
    "**Observation(s):**\n",
    "1. Using sepal_length and sepal_width features, we can distinguish Setosa flowers from others.\n",
    "2. Seperating Versicolor from Viginica is much harder as they have considerable overlap."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "TdiZ9lLL7eTn"
   },
   "source": [
    "## 3D Scatter plot\n",
    "\n",
    "https://plot.ly/pandas/3d-scatter-plots/\n",
    "\n",
    "Needs a lot to mouse interaction to interpret data.\n",
    "\n",
    "What about 4-D, 5-D or n-D scatter plot?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ikdQY1fR7eTp"
   },
   "source": [
    "#  (3.3) Pair-plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "TkuKNKA37eTs",
    "outputId": "b0b81e40-40d7-44eb-f446-d5509c9c7d94"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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9S0ZnrrGxEX/+85/x8ssv47nnnsO3vvUtPPzww1i9erXm7zc0NGD79u2QZRmH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\ntWTwJ2NguG8LIxfaYDfbUVVXhZnbZsaE7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TJHOVDfjx/Yf4AJjaiBJrWG0aNHY8yYMThw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\nBcpXqrxnpAPLke31r3XvD1NdTUxoh8mWC5SskcI76lzAP1cC/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lj7ScwyD6z9hNnqiDKc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\nQgghnMa/AEYm24SymUJuAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109b9c978>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# pairwise scatter plot: Pair-Plot\n",
    "# Dis-advantages: \n",
    "##Can be used when number of features are high.\n",
    "##Cannot visualize higher dimensional patterns in 3-D and 4-D. \n",
    "#Only possible to view 2D patterns.\n",
    "plt.close();\n",
    "sns.set_style(\"whitegrid\");\n",
    "sns.pairplot(iris, hue=\"species\", size=3);\n",
    "plt.show()\n",
    "# NOTE: the diagnol elements are PDFs for each feature. PDFs are expalined below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "aJAPMfBU7eT1"
   },
   "source": [
    "**Observations**\n",
    "1. petal_length and petal_width are the most useful features to identify various flower types.\n",
    "2. While Setosa can be easily identified (linearly seperable), Virnica and Versicolor have some overlap (almost linearly seperable).\n",
    "3. We can find \"lines\" and \"if-else\" conditions to build a simple model to classify the flower types."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LWsvwUkL7eT4"
   },
   "source": [
    "# (3.4) Histogram, PDF, CDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wUvH2M817eT6",
    "outputId": "1f577f0d-475c-4710-b6d3-f744f558fa02",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ac84160>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What about 1-D scatter plot using just one feature?\n",
    "#1-D scatter plot of petal-length\n",
    "import numpy as np\n",
    "iris_setosa = iris.loc[iris[\"species\"] == \"setosa\"];\n",
    "iris_virginica = iris.loc[iris[\"species\"] == \"virginica\"];\n",
    "iris_versicolor = iris.loc[iris[\"species\"] == \"versicolor\"];\n",
    "#print(iris_setosa[\"petal_length\"])\n",
    "plt.plot(iris_setosa[\"petal_length\"], np.zeros_like(iris_setosa['petal_length']), 'o')\n",
    "plt.plot(iris_versicolor[\"petal_length\"], np.zeros_like(iris_versicolor['petal_length']), 'o')\n",
    "plt.plot(iris_virginica[\"petal_length\"], np.zeros_like(iris_virginica['petal_length']), 'o')\n",
    "\n",
    "plt.show()\n",
    "#Disadvantages of 1-D scatter plot: Very hard to make sense as points \n",
    "#are overlapping a lot.\n",
    "#Are there better ways of visualizing 1-D scatter plots?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "gjZTt3WS7eUD",
    "outputId": "f71f78ef-2d20-408e-f187-707e5b31b3e1",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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FCt577z3atm1LdnY2n376Kbm5ubz//vt069YNgJycHN566y1WrlyJxWJh6tSpbNy4kf/9\n73/079+fcePGsX79evLy8iS8Aq2gXJ2X7KYshFC/oUOHsnDhQh566CGio6O57bbb6NixI2azGYDm\nzZtz8uRJjh49yosvvgiA0+mkcePGREZGcuONNwIQGxvLlClTSElJAeDkyZNcvHiRP/zhDwDYbDZO\nnjzJo48+yvz58xk3bhwJCQm0b9++yj9zyE3YsJbjmleEyShT5YUQqvf999/TuXNnPvzwQ/r168fC\nhQtJTU3F7XZTWFjIkSNHaNSoEU2aNGHOnDksXryYqVOncuedd9K0aVP27NkDQH5+PhMnTiw+b4MG\nDUhMTGTRokUsXryY0aNHc+ONN7JmzRruu+8+Fi9eTPPmzVm+fHmVf+bQ67zKONsQvMOGF22OQJck\nhBCV0rZtW5599lneeecdPB4PY8aMYfXq1Tz88MPk5OQwadIk4uPjeeGFF3j22WdxuVzodDr++te/\n0rhxYzZv3szIkSNxu91Mnjy5+Lzx8fGMHz+eMWPG4Ha7qV+/Pv3798fhcDBjxgwsFgt6vZ6ZM2dW\n+WcOufDydV5lHTZMz5ZhQyGEuiUlJbFkyZLir1NSUti9ezdz58694ri2bduyePHiq17/5z//+arH\nunbtCsCgQYMYNGjQFc916NAhKN3Wr4XcsGGBw4XFZECv05V6rMVklKnyQgihQiHYebmJDCt9piF4\nOy+ZbSiE0JquXbsWd07VVUh2Xr5dkksTEWaQ+7yEEEKFQi68bHZ38V5dpYkwGXG4PLjcngBXJYQQ\nojxCMLxcRJVj2BCQoUMhhFCZkLvmVeBwERdhLtOxv15ZPibcFMiyhBAh5pOUk34936iuSX49n9qF\nXOdltbuIKuM1L9/EDrnuJYQIJQcPHmTr1q3BLqNEIRdeBQ538XBgaSyyIaUQIgR99913HDlyJNhl\nlCjkhg2t9nLMNvQNG8o1LyFENfDb1ef/8Y9/8Mknn7Bt2zY8Hg/jx4+nU6dOrF69GpPJRJs2bcjP\nz+f1118nLCyseCV6l8vFlClTUBQFu93Oiy++SHJyMv/4xz/Yu3cvOTk5tGrVilmzZgXss4RUeCmK\nQoGj7Pd5ybChEKI6+e3q8+vWrSM9PZ0lS5Zgt9sZPnw4ixcv5r777qNWrVq0a9eO3r17s2TJEhIS\nEvjwww9555136Nq1K3Fxcfztb3/jyJEjFBQUYLVaiYmJ4f3338fj8TBgwADOnTtHQkJCQD5LSIWX\n3eXB7VHK3Hn5hg0LHTJsKITQvt+uPt+qVSv27dvHmDFjAHC5XJw+fbr4+OzsbKKioooD6KabbuK1\n115j6tSpnDhxgsceewyj0cikSZMICwvj4sWLPP3000RERFBQUIDT6QzYZwmpa14fbU4DIPVMXqnH\nfpJyku9TzwGwLvV8QOsSQoiq8NvV51etWkXXrl1ZvHgxH374If3796dhw4bodDo8Hg81atTAarVy\n/rz3Z+CWLVto3LgxKSkp1KlTh0WLFjFp0iRee+01NmzYQEZGBq+99hpPP/00RUVFKIoSsM8SUp2X\nw+W92bgsG1ECmC4t3ut7nRBC+Eswprb/dvX5N998ky+++IJRo0ZRUFBAnz59iIqKom3btvztb3+j\nWbNmvPzyyzzxxBPodDpiY2OZNWsWOp2Op59+miVLluByuZg8eTItW7Zk3rx5PPjgg+h0Oho2bMj5\n8+dp2LBhQD5LSIWX3VX27VAAwgze45yywoYQohr47erz4A2037rzzju58847i7++9dZbrzrm/fff\nv+qxlStXVr7IMirTT/Fdu3YVj4n+2gcffMCAAQMYM2YMY8aM4dixY34v0J8ud15lCy9f52WXzksI\nIVSl1M5r4cKFrFmzBovFctVze/fuZc6cOddMbjXyhVBZOy+9TodRr8Mp4SWEEKpS6k/xpKQk3nrr\nrWs+t2/fPt59911GjhzJggUL/F6cv9nLec0LvEHnkGFDIYRQlVI7r759+5Kenn7N5wYMGMCoUaOI\niori8ccf54cffqBnz57XPDY1NbVylV5DUVFRuc6beeEiADnZWSiFZQswg04h12qrdP3lrTVYpE7/\n00qtUqf/+WpNTk4OdinVToUnbCiKwrhx44iOjgagR48e7N+//7rhFYi/vPJ+U1h2FAJWGibWLfu9\nXuZ8DKawStevlW9gqdP/tFKr1Ol/WqpVayocXlarlXvvvZevvvqKiIgIUlJSGDJkiD9r87vyXvPy\nHStT5YUQfrft6tl6ldLl9/49n8qVO7y++OILCgoKGDFiBE899RRjx47FbDbTrVs3evToEYga/cbh\n8qDXgVGvK/NrzAa55iWEEL/muyF5xIgRZX7NW2+9Ra1atRg5cqRfaihTeDVo0IDly5cDMHDgwOLH\nBw8ezODBg/1SSFWwuzyYjXp0unKEl1FPbmHgljgRQgitueOOO4JdQmjdpOxweco10xDAZJBhQyFE\n9fD4448zduxYbr75Zvbs2VPcDaWlpeHxeJgyZQpdu3bl3nvvpXHjxphMJkaPHs2cOXMwGo1YLBbe\neOMNvvvuO44dO8YzzzzDvHnzWLduHW63m5EjR/LAAw+waNEivvzyS4xGI126dGHq1KlX1DF79my2\nb98OwL333su4ceOYNm0aOTk55OTksGDBAmJjY0v8LCEVXnaXu1zXu8B7Q7OssCGEqA6GDRvG6tWr\nufnmm1m1ahXdu3fn7NmzvPLKK2RnZzN69Gi+/PJLCgoKeOyxx2jdujVz5syhf//+jBs3jvXr15OX\nd3lt2P3797NhwwZWrFiB2+3mtdde4+DBg3z99dcsXboUo9HIE088wQ8//FD8mh9++IH09HSWL1+O\ny+Vi1KhR3HLLLQDccsstjB8/vkyfJaTCy+H2lHl1DR+T3OclhKgmunfvzquvvkpOTk7xHl6//PIL\nu3fvBryryl+86L2lqEmTJgA8+uijzJ8/n3HjxpGQkED79u2Lz3f8+HHat2+PwWDAYDAwbdo0vv76\nazp06IDJZAKgS5cuHD58uPg1R48epUuXLuh0OkwmEx06dODo0aNXvGdZhNSq8nanp9ydl/nSsGEg\nV0cWQoiqoNfr6devHy+88AJ9+vShWbNmDBgwgMWLF7Nw4UL69etHXFxc8bEAa9as4b777mPx4sU0\nb968eP4DQNOmTdm/fz8ejwen08nvf/97mjRpwu7du3G5XCiKwtatW68IpWbNmhUPGTqdTnbs2EGj\nRo0AyjUfIeQ6rzizqVyvCTPq8SjeyR7hpvJdLxNCiOsK0tT2IUOG0KdPH7799lvq1KnDjBkzGD16\nNFarlVGjRhWHlk/79u2ZMWMGFosFvV7PzJkz2bp1K+C9f7d79+6MHDkSj8fDyJEjadWqFf379y9+\nrHPnzvTp04cDBw4A0LNnT7Zs2cKIESNwOp3069ePNm3alPtz6JQqaCm2b99O586d/X7e8t4A2Oml\ntSTFRzC8S9mX6N98NIsvdmewfUYfakaFVaRMQDs3K0qd/qeVWqVO/9NSrVoTWsOGLg9mQzknbFzq\ntqx22U1ZCCHUIqTCy+Fyl3vCRvil4/OLJLyEEEItQia83B4Fp1sp/1R56byEEEJ1Qia8bA5v+JS3\n8/Idb5PwEkII1QiZ8CqwuwEwl3OFDd+KHNJ5CSGEeoTMVPkKd14mueYlhPC/FYdW+PV8w1oM8+v5\n1C5kOi/fsF95r3mFS+clhKimNmzYwLJly8p0bGZmJi+88MJ1n09NTeXtt9/2U2WlC53O69KwYbmX\nhzLo0AFW6byEENVMeVaHr127donhlZycXKX3tIVQeFWs89LpdISZ9NJ5CSE077eryo8fP754JfhJ\nkyYRFxfHHXfcQdeuXXnxxReJjIykZs2ahIWF8fjjj/P000+zfPlyBg4cyM0338zBgwfR6XTMmzeP\n/fv3s3TpUubOncuKFStYsmQJHo+HXr168eSTT/Lxxx/z3XffUVhYSI0aNXj77bcxm80V/iyhM2xY\nfM2r/Es8hRsNcs1LCKF5vlXlAVatWsVTTz1V/FxmZib/+te/ePjhh/nLX/7C7Nmz+eijj0hKSrrq\nPDabjQEDBvDxxx9Tp04dNmzYUPzchQsXWLhwIZ988gmrV6/G4XBgtVrJycnhgw8+KF6Bfs+ePZX6\nLKETXsWzDcv/kcNMepkqL4TQvO7du7Nnz57iVeXDwi4vedegQYPiTuj8+fM0b94c4LpL+7Vu3RqA\nxMRE7HZ78eOnTp2iefPmhIeHo9PpeOaZZ4iKisJkMvH0008zffp0zp49i8tVuZ+pIRNeBRWcbeh9\njUGGDYUQmvfbVeUNBsMVz/nUrVuXI0eOALBr165rnut6K8AnJSVx7NgxHA4HAE8++SRbtmxh3bp1\nvP766/z5z3/G46n8Th0hc83LWsFrXgDhJj35El5CCD8K1tT2X68qv2XLlmse85e//IXp06cTERGB\nyWQiISGhzOePj4/n4YcfZvTo0eh0Onr27Em7du2wWCw88MADgHfyx/nz5yv1OUImvAocbkwGHfpy\n7BfjYzYasBY5A1CVEEJUrcTERPbt2wd4hwp9fr1P1549e5g/fz7x8fHMnTsXk8lEgwYNio9Zv359\n8bHPPPNM8X937doVgPvvv5/777//ivf96KOP/Po5Qia8rHZXuVfX8Ak36snMl85LCBEaatasyYQJ\nE4iIiCA6OprZs2cHu6SrhEx4FdhdFbreBd7rZHKflxAiVPTr149+/foFu4wShcyEDau9/Nuh+ISZ\nDNgcbjyegO/bKYQQogxCJrwKHK5yb0Tp49vTy3evmBBCiOAKmfCy2V3Fi+yWl+zpJYQQ6hI64eVw\nV7jz8g03ynUvIYRQh9AJL7urQktDAYRf6rzkXi8hhFCHkAqvitygDJc7L1nfUAgh1CEkwktRFAoc\nFZ9t6Ou8ZNhQCCHUISTCy+7y4PIoFe68fOGVJ6tsCCGEKoREeBU4KrYRpY/FF16FEl5CCKEGIRFe\nlzeirNiEDZNBh1Gvk85LCCFUIjTCqxLboYB36f8Yi4m8QrnmJYQQahAa4VWJ7VB8YsKN0nkJIYRK\nhEh4Ve6aF3Cp85LwEkIINQiR8PJH52UiT6bKCyGEKoRGeBXPNqzYhA2AGItROi8hhFCJ0Agvv3Ve\nEl5CCKEGoRFelZxtCMhsQyGEUJHQCC+7C4Pee69WRUWHGSl0unG4PH6sTAghREWUKbx27drFmDFj\nrnp8/fr1DBkyhBEjRrB8+XK/F+cvNrubSLMBna7i4RVjMQGQL0OHQggRdMbSDli4cCFr1qzBYrFc\n8bjT6WTWrFl8+umnWCwWRo4cSa9evahVq1bAiq0om91FZFipH7VEMRbv6/OKXNSMCvNHWUIIISqo\n1M4rKSmJt95666rHjx49SlJSErGxsZjNZjp37szWrVsDUmRlFTjclQ+vcG/nJTMOhRAi+Er9id63\nb1/S09OvetxqtRIdHV38dWRkJFar9brnSU1NrWCJ11dUVFSm8567mIPe7SHjbEaF38tUaAZg76Gj\nmK0R5X59WWsNtlCvM+7oZ+U6PqfZ4FKPCfU/U3/TSp1wudbk5ORgl1LtVLgdiYqKwmazFX9ts9mu\nCLPfCsRfXlm/KXQ/ZlMrXE9i3cQKv1f7RjXgmzPE1a5HcnL5z6OVb+CQr9P2c7kOTyxDDSH/Z+pn\nWqkTtFWr1lR4tmGzZs1IS0sjJycHh8PBtm3b6Nixoz9r8xur3U2E2V/XvGTYUAghgq3cP9G/+OIL\nCgoKGDFiBNOmTWPixIkoisKQIUNISEgIRI2VZrO7iAqr+OoaINe8hBBCTcoUXg0aNCieCj9w4MDi\nx3v16kWvXr0CU5kf2ewuosIr13lFmA0YZE8vIYRQhZC4STnfD1PldTqdd1sUWWVDCCGCrtqHl8Pl\nweHyEF3J8IJLS0RJ5yWEEEFX7cPLtyhvZTsvuLQ4r1zzEkKIoKv24WW9FF5Rfum8jLKnlxBCqICE\nVzlI5yWEEOpQ7cPLN2xY2dmGn6ScJDPfzrm8Ij5JOemP0oQQQlRQtQ+vfD9e8wo3GSh0uit9HiGE\nEJVT7cPL13n5Y7ZhuMmA063g8sieXkIIEUzVPrysRf7rvCwm7x9XkVPCSwghgqn6h5efrnmBt/MC\nKJKhQyHLt8q0AAAgAElEQVSECKqQCa/ISi7MC2CR8BJCCFWo9uFls7uK1yWsLF/nJZM2hBAiuKp9\neFn9sK6hT7jZ13nJNS8hhAgm//xUVzGr3e2XmYZwediw0CGdV7WQcxL2rIAzO8HtgJo3gDEcYhsE\nuzIhRCmqf3gVOf3Weck1r2qiMBvW/gV2fAyK2xtaJgscXe8NsUa3Quv7wVDt/3kIoVnV/l+nze72\ny9JQACaDDoNOJ9e8tOz4T7D6EbCeg5v/AN0eg7gk73OF2bBiPBz7L9iy4KaHwGAOZrVCiOuo9te8\n/LGXl49OpyPcbJBhQy1SFNjwd/hwoLfLmrgW+s++HFwAlhrQejB0GAlZh2H3Mu/rhBCqEwKdl4to\nP9zj5WMxGSiQzktb3E74zxTvMGG7YTDwDTBHXv/4hl2hKBcOfgXxTaHRbVVXqxCiTKp9eHlnGxr8\ndj6LSU+RdF7aUZgDy8fC8R/hjv+DntNBV4bbJm64Cy4cgdQ1UKcNWOLK9HYrDq246rGz58+y17C3\nzCUPazGszMcKEaqq/bCh1e4iKszkt/NFmI1yzUsr8s/B+/dA2kYYNA96/b+yBRd4j2s3HDweSP08\nsHUKIcqtWndeDpcHh8tDlD87L7OBTKvdb+cTZbTt/Su+jDt7Fmw/X//4gouw82PIPwsPfgrNepb/\nPSNreV93+Dto2vPK62NCiKCq1p2XzY8bUfqEm2TChupZM2HTm2C7AGM+q1hw+TTrBeYoOPCl/+oT\nQlRatQ6vvCLvrsdR4f4bNrSYDBQ53Xg8MgtNlaznYPOb4HHB+P9AUtfKnc8Y7u26sg56b2oWQqhC\n9Q6vQm/nFWvxY3iZDShA/qWtVoSKFFyEn9/x/ne3xyGxvX/O2+g27/T6I9/753xCiEqr1uGVW+jt\nvPwZXhGXVtnwnVuohD0ffp4Hbjt0nQTRdf13blM4JN0KZ3d7Zy8KIYKuWoeXb9gwxuLH+7zMEl6q\n43bB1vfAnuddNSOmnv/fo9Gt3v8/udn/5xZClFu1nm0YiM7Lty1KTqHDb+cUlaAosPdTyEmDzr+H\nGk0uP/ebGYqVElET6iR7w6v53aD33wxWIUT5Ve/O61J4xfhzwoZ0XupychOc+hlu6AOJHQL7Xo1u\n83Z358p+w7EQIjCqfedl1OuIMPtzhQ0JL9WwZsK+z6B2S2h5T+Dfr06yd/3DtI0BDcrlB5djdVop\ncBbg8DhwuB043U48eDDqjZj0JuLC4ogLi0NX1puuy+C3K4HISh9Czap1eOUVOYmxmPz6DzxCOi91\nUDywa5l325IOo0BXBYMIOr134sbBL72rzkfWqvQpFUUhszCTE7knOJV/igxbBrn2XNxK6fcSGnVG\nakfUpmlcU9rVakediDqVrkcIrajW4ZVb6PLr9S4Ak0GPUa+T8AqyyHPbIPs43DgKwmOr7o0bdPEu\n2Ht6G7ToV+HTuD1udmftJiUjhfMF5wGINkfTIKoBLeNbEhcWR6QpErPBjFlvxmwwo0OH0+PE4XGQ\nU5RDVmEWZ6xn2HR6ExtPb6RxTGN6JvWkYXRDf31aIVSrWodXXqGTGD+uKO9jMRnILZDwCprCbKLP\nbPAumFv/pqp9b0sN7+aV6duged8KneJk3kn+c+w/ZBVmUTeyLvc0uYcbatxArDm27KMEv8rrAmcB\nOzN3svnMZt7f+z6d6nTirkZ3EWYMq1B9QmhBtQ6v3ELvsKG/hZsN0nkF04H/gAK0G1r2hXb9qX5n\n2L3UO8OxHBRFYXPGZr5P+54YcwwjWo6gRY0WlR7WjjBFcGu9W+mS0IUfT/3Izxk/k5aXxohWI6hl\nqfzQphBqVO1nGwYivCwmCa+gyU6D09uxJdzk7YKCIfFG0Ju83VcZKYrCtye+ZV3aOlrFt+LRGx+l\nZXxLv16PNRvM3NX4Lsa0HkOhq5BFexZxKv+U384vhJpU7/Aqcvr9mhd4J21IeAWBosD+zyAsGmvi\nLcGrwxQOddvCmR3gKtv9ft+lfceWs1u4JfEWhrYYSpghcEN6jWMb81C7h4gwRfDx/o9Jz08P2HsJ\nESzVNrwURSG3MDDhZTEZyJFrXlXv3F7vJI0W96AE8Id/mdTvAk4bHC19vcN9eftIyUjh5ro3c1ej\nu/zabV1PXHgc49uMJ9oczZIDS8gqzAr4ewpRlapteOUVuXC6FWpGmv1+7nCzofgGaFFFFAUOr/Wu\ndNHw5mBXA7VbgSkC9q4s8bBT+afYdHETzWs05+7Gd1dJcPlEmaMYlTwKvU7Pv1P/Tb4jv8reW4hA\nq7bhddHmHc6JD0B4WUwG8u0u3LItStXJOgS5J6FZb3UszaQ3eG9UPvAVOAqueUiRq4jVh1cTZYzi\nvhvuQ18V96L9Rnx4PCNbjaTAWcDSA0txeWQ3BFE9VOPw8u52HIjw8t2oLN1XFTqyFsJioYEKui6f\nep28Q4eHv73m02vT1pJrz6VXrV6EG8OruLjL6kXV4/7m95Nhy2DtibVBq0MIf6rG4eUNlkB1XiCr\nbFSZkylw4Yh3R2SDiu7uqNkMohJgz6dXPXUi9wQ7zu+gW71uJIQnBKG4K7WMb8ktibew9dxW9l/Y\nH+xyhKi0Un8SeDweXnjhBQ4ePIjZbObll1+mUaNGxc+//PLL/PLLL0RGRgIwb948oqOjA1dxGQWy\n87IUrywv4VUl/vcamCIhqVuwK7mSTg9t7vOuXl+UW7zSh0fx8PXxr4kLi6NHgx5cOH8hyIV69U7q\nzan8U6w5uoa6EXWJt8QHuyQhKqzUzmvdunU4HA6WLVvGn/70J2bPnn3F8/v27eO9995j8eLFLF68\nWBXBBXDh0jWvmpH+n5UmK8tXobN74NA30LQHqHHFiLZDvRtgHviq+KGd53eSWZjJ3Y3uxmTw/2zX\nijLoDQxtMRQ9ej478hkexRPskoSosFI7r+3bt9O9e3cAbrzxRvbuvbzqtMfjIS0tjeeff56srCyG\nDh3K0KFDr3me1NRUP5V8WVFR0XXPe+TkBcKMOk4cPVT8WMbZPL+8r7XAe9F7/5ET1HGXbQpySbWq\nidrqrLfpBaKMEWRaWqCcPVv8uNPpJONXXwdLTkRXmkXUxZHyIafCbuTU2VOsP72ehLAEYu2xnM04\ni9Pp5GxG8Gv16VajGz9k/cDag2vpEHt5dfzf1pnqVs/3wa+p7Xu0JL5ak5OTg11KtVNqeFmtVqKi\nooq/NhgMuFwujEYjBQUFjB49mt///ve43W7Gjh1L27ZtadWq1VXnCcRfXonfFHt2UivKccXzO/JO\n+uV9o4qcsCuHqPg6JCc3Kv0FlFKriqiqzqwjcOp7uO2P1K3R+IqnMs6eJbFu3eDU9SuJrVvD6Qcw\nb36b5KQ6zMs4ToG7gBHJI0iMTgTgbMZZ6iYGv1afBCWBDE8G27K30SmpE7UjagNX15ncQiXfB7+h\nqu/RUmipVq0pddgwKioKm81W/LXH48Fo9GaexWJh7NixWCwWoqKiuOWWWzhw4EDgqi2HizZHQK53\nweVrXjLbMMA2vu4dKuw2OdiVlKztEPC4uLBnKZtOb6JVfCtVr+yu0+kY0HQAYYYwPj/yuQwfCk0q\nNbw6derEhg0bANi5cyctWrQofu7EiROMHDkSt9uN0+nkl19+oU2bNoGrthwCGV5Gg55wk56cgrIt\nDSQqIDcddi2FTmMhSuX7VNVtBzWb8/6BT3B6nPRK6hXsikoVaYrknqb3cMZ2hk1nNgW7HCHKrdRh\nw7vuuouNGzfywAMPoCgKr7zyCu+//z5JSUn07t2bQYMGMXz4cEwmE4MGDaJ58+ZVUXepzuUV0apu\n4CaPxFpMMmEjkDa9BShw6xPBrqR0Oh25rQey4uRy2tdI1sxK7q1rtiY5PpkfT/1Iq/irh/qFULNS\nw0uv1zNz5swrHmvWrFnxfz/00EM89NBD/q+sElxuD5n5durGBO7GUAmvALJmwvYPof0IiEsKdjVl\nsjzCTIFezzB3OFrqx/s36c/x3OOsObqGfvEV31xTiKqmojs+/SfTasejQNrFAj5J8c8kjd+Ks5iL\nw+u37zGqqzZ+4KpWyjvgKoLbnwp2JWVS5Cri45PfcJvbyO2n97G+7aBgl1RmUeYo+jbpy+dHPme/\naT/1qBfskoQok2q5wsbZ3CKAgKwo7xNjMcnK8oFQmANbFkLrQVBLHUPQpVlzdA0Xiy4yseFd1Lxw\njAhrZrBLKpf2tdrTLK4ZW7K3kF2UHexyhCiTah1eMeGBC69Yi0lmGwZCynyw50H3PwW7kjJxe9x8\nsO8D2tVqR5fOjwHQMG1rkKsqH51Ox71N70WHjv8c+w+KIgtOC/WrnuGVdym8Ath5yTWvACjMgc3z\noNW9kNg+2NWUydqTazmVf4oJbSegi2/MhZpNSdJYeAHEhsXStUZXjuceZ8f5HcEuR4hSVctrXmfz\nijAb9ESaA7d1RnykCZvDTZHTHbD3CDkpC8CeCz3+L9iVlImiKCxKeZXG5hr0PJ8Gme9zMro2HU+k\nEL3/K/Ij4gCw5OWBPebyCxvfGqSKS5Ycncwp1ynWpq3lhrgbgl2OECWqnp1XbhF1YsICuvFfzSjv\nOnu+fcNEJRXlws//hJYDvPtkacDPtpOkFp1nfK0uGC7t1ZVeqwkK0DDrWHCLqwCdTsfApgNxK26+\nPP6lDB8KVauW4ZV2oYBGNSMC+h61LoXXBauEl19sfMMbYBrpugAWZW2ltjGSgbGXl/8pMkeQGZNI\nwwvHvbs/a0y8JZ6eDXtyOPswXx3/qvQXCBEk1TK8Tlyw0ahmZEDfo2aUd/WOLKs9oO8TEnJOweZ/\nQrvhUO/GYFdTJvsLz/Gz7SSja3bCrL9y9P1krSbEFOYSZ7sYpOoqp2tiV+pH1Wf2ltlcKFTHdi5C\n/Fa1C6+cAgc5BU6aBDi8al/qvCS8/OD7SzfB934+uHWUw/tZ24jSmxlW4+qJJadrNsaj02ly6BBA\nr9MzsNlAbE4bs7fMLv0FQgRBtQuv41neRYQb16qqzkuGDSvl9HbYs9y7+G6cehez/bVTjhy+yzvE\n8PgORBuu3mPMYQrnXGx9b3hpcOgQoE5EHR5p/wjfnPiG709+H+xyhLhKtQuvExe84dWkVmCveUWY\njUSYDVyQzqvi3C74z9MQWVszq2kAfJi1HYNOz+j4jtc95lStJkQ6bNTMP1eFlfnXhHYTaFmjJS//\n/DK59txglyPEFapdeB3PtKHXQcP4wIYXeLsvGTashE1vQMZOuOfvEKaOHbhLc8FVwGc5e/ldbDK1\nTVHXPS69ZmOceiNNzh+uwur8y6Q3MfO2mWQXZTNz80yZfShUpdqF194zeTSrHUWYMXD3ePnUigrj\ngkyVr5jzB+C/s6H1YGgzONjVlNm/L+zAobgZX+umEo9zG0ycqtWUhlnHMbm1ezN765qtebzj43yX\n9h1LDy4NdjlCFKt24bXndC7tGsRWyXvVjAwjM186r3JzFsHqR7zd1j1/D3Y1ZWZzO1h6cSe9o2+g\ncViNUo8/ntACo8dF09z0KqgucCa0ncAdDe7gb1v/xt6svcEuRwigmoXXubwiMvPttKtfNeFVPy6c\n09mFMpxSHooCXz7tHS4c+CZE1Q52RWX2afZu8j12JpTSdflcjKpNriWOFtnHA1xZYOl1ev5621+p\nbanNn/77J1m8V6hCtQqv3enei8pVFV4N4yPIt7solCWiym7LQtj5b+jxLCTfG+xqyqzI4+T9C9vo\nGplEu4jEsr1Ip+N4QgsSCi8SU6DtH/hx4XH8o8c/uFB0gSfWP0GRqyjYJYkQV63Ca+ORLMJNetpW\nUXglXZoUIktEldGBL+GbadCiP/SYFuxqymVl9h4uuAqYVPuWcr0urXYz3DodTc8eDFBlVadd7XbM\n6j6L3Zm7ee6n53B75Jc2ETzVamHe/x48T7emNQk3BX6yBlye0XjR5qBBjQg8isLK7eks/jmNBaM7\nkxTgJapUY9v7pR9zbj9s+xfENoD73wW9dn5vsntcLMraSpeIBnSObFCu1zpMFo7FNKRx5mH2JnXC\nZTQHqMqqcVeju5h601T+tvVvzNk6h+dufi6ga4gKcT3a+QlSimOZVk5cKODOlnWq7D194ZV9qfPa\neSqHHadySM3IY863B6qsDtU7tw+2L4KYROj6CITHlP4aFVmds5fzLhuPlrPr8tlX8wZMbieNM7U7\nbf7XxrQew7jW41hyYAkzf54pHZgIimrTeS3begqDXke/tnWr7D2jwozUjDRzscAbXinHLlA7OowB\n7RL5JOUkVruLqLBq80dcMWkbYc+n3o6r66Ng0lY36vS4+VfWVjpG1OPmyIqtAJIVEU9WdB1uyEjl\nSN3Wfq4wOP7U5U+YDWYW7lmIzWnjr7f/FZM+cPvnCfFb1aLzyitysmzbKfq2SSAhJrxK37tZ7SjO\n5haRkVvIqexCbmocz91tEnC4PaQcC+FFTT1u2P857FkBdZKh2+NgDuySXYGwNHsXZ535PFq7W6WG\nxw4ntia6KI+62dqeNu+j0+l4stOT/LHTH/n6+Nc8svYRMgsyg12WCCGabgu+OpTHjryTrPolndwC\nJ41rRvJJyskqraFjozi2/5TNT4ezMBl0dEqKo2PDGhj1On45mU3v5IQqrUcV7Pnwy4dw4Qg0uh3a\n3Af6qrkO6U+57iLmZ27m1shG3BrVqFLnOh3fmAJzBC0y9nKWEX6qMPgeavcQCREJzNw8k2FfDGPO\nHXPomtg12GWJEKD5zuvwuXy2pWXTvXltGtSo+iGpHs1r4/Yo7DyVQ4cGcUSYjVjMBpITY/glLafK\n6wm6i8dgw6uQnQY3PgjthmoyuAAWZqaQ77bzdN07Kn0uRa/ncGIbEnIziM886ofq1GNgs4F8MuAT\nYsJiePi7h5m5eSY5RSH4vS+qlKY7L7vLw6q9p6kdFUbv5KqbqPFr3ZrVpHViDBm5hfT81WSRTklx\nrNiejsvtwWjQ/O8IpVMUOL4BUj8HS7z3+lZMvWsfW5bZiUGW7sjlk4s7GRzXhpbh/rmR+mjdVrQ6\nvYfWe7/gfz2n+OWcatG8RnOWDljKWzveYsmBJXyX9h2TOkxChw6ToXLXwoa1GOanKkV1oumfqj+f\nKiCv0MnQzg0wBSkgdDodo29pxNS+ragReXka9I1JcRQ43BzNtAWlrirlssOOj2D/aqjTGro/ff3g\n0ojXz/2EAR2T69zqt3O6DSYO1mtLYsZeamh0r6+SRJgiePbmZ1kxcAUta7Rk9pbZvLnjTTae3ig3\nNQu/02x4nciysfdcETc3ia+SFeTLq209743Se09X860kzu2Dn/4BZ3ZCqwHQZYLmZhT+1o/5x/g2\n7xATat1Egsm/q90fTUzGbo6kzd4v/HpeNWleoznv3f0ei/ouIiEige9Pfs/c7XP58tiXnLNpd4sY\noS6aHTZcsOEYOqBXq+AMF5amae0oLCYDe8/kMqRz+W5s1QRFgR0fw1dTwWCCWx6DWs2DXVWl5bvt\nzDyzjuZhtXio1s1+P7/LYOJQcl/a7VpFrXMHyUpo6ff3UAOdTsdNdW9idOvRZNgy2JqxlV3nd7H9\n3HaSopO4qe5NtIpvhUGj10NF8Gmy87LaXazZeZoWtcKIDlfnvSUGvY7W9WLYdzov2KX4n8MGn02C\nNY9Dw5vgjqnVIrgA/nFuA1kuGzPr340pQD9YD7fsjS0ino7bl4LHE5D3UJPEyER+d8PvmNJ5Cn0a\n9SHPkcfKwyt545c3+PHUj+Q78oNdotAgTXZeX+3JwOZw0ybh+psBqkHbejF8uj0dj6carTp//gCs\nGAeZB73rE/b4P/jlo2BX5RebrWmszN5Dt8hGpBaeJ7XwfEDex20MY3fHYXTbuICmR3/iWPMeAXkf\ntYkwRXBrvVu5JfEWjuQcYdvZbfyY/iM/nf6JVvGt6JrYlYbRFbsRXIQeTYbXt3vPUj/OQt0odZff\npn4sH25O48SFajJpY+cS73Ym5kgYsxqa9Qx2RX6T5bTx3OmvqWmI4M7opgF/v/SkLpw//ANtd6/m\nVKMuODV4A3dF6XV6WtRoQYsaLbhYeJFt57ax8/xO9l/Yzw1xN9ArqRd1I6tupRyhTZobNrTaXfx0\nOIt+beuqfkFQ36SNPVqftFFwET6dAJ89CvU6wSM/VavgcnrcTE3/EpvbwbAa7THpquA6jE7Hzs4j\nMTkK6Lj1k8C/n0rFW+K5u/HdTOk8hd5JvUnPT+fd3e+y8tBKLhZeDHZ5QsU0F14/HDiPw+2p0jUM\nK6p5QhRmg559ZzR83evoenjnVu9ST71mwNjPvQvsVhOKovByxvdsK0jnL/Xuoo6p6oaic2s0ZH/b\ngTRKS6HhiZQqe181MhvM3Fb/Np7s9CS317+dQ9mHmLdrHt+d+I48h4b//YiA0Vx4fbPvLLWiwuiU\nVPo27MFmMuhplRitzenyhdnwxRRYfB+ExcBD67wTMwzqHqotr7fOb2RVzl4eqd2Ve+OSq/z9D7S5\nh6xazei09WMsthBeC/OScGM4vZJ68UTHJ+hQuwM/Z/zMwNUDWX5wuaxeL66gqZ9ERU43/z1wnkEd\n62PQq3vI0KdNvVi+3H0GRdHINiCK4l0F/ptpUHDBu6BurxlgsgS7Mr9SFIV3MjezMGsLQ2u0Y3Jt\n/92MXK469Aa2dJvIXV+/yK0/zeO/vafiNlXt4tLXs+LQiqC9d5Q5ioHNBtKlbhe2n93OSz+/xLKD\ny3j2pmeJxr/33glt0lTn9d+D57E53PTXwJChT9v6MeQVuThrdQW7lNJl7Cbpxydg5USIbQh/+C/0\n/Wu1Cy6X4uGVs+t5J/NnBse1YUZi76BeP7VF1yHltj9QI/sk3TYuQCcdRrHEyEQ+6PcBf+/xd6wO\nKxO/m8jfD/+dU/mngl2aCDJNhdcXuzOoGWmmW9OawS6lzHzDm3vOqnh5nJxTsPpRWHAH4dmHoP+r\n3mHCxA7BrszvMp1WHk1bxdKLuxhfszMv1rsbgy74/wwy6nfgly6jSTyzh85bPgqJ+7/KSqfT0bdx\nXz4f/DlPdHyCXbm7GPTZIF7f/jo2ZzWZySvKTTPDhgUOF+tTzzOkc31NLXTbqm40daLD2H6mINil\nXC07DTa9Cb8s9n5925Nk2nTUNZiqzb1bPoqi8J/cVGZl/IBDcTGz3t3cV6NtsMu6wrHmPQgvzKHN\n3i8w222k3PYwbmNYsMtSjXBjOH9o/wfaKG34Kv8r/rX3X3x+9HOe7Pgkg24YhF4Fv4SIqqOZv+2v\n9pyl0Onm3vbaWvBVp9NxR4va/HKmEJdbJb9Nn90Dq/4Ab3aE7R9ChxHwxHa4ayaKUR3XW/xpT0EG\nj6atYvrpb7ghvCafNhuruuDy2d9+EL90GUW9M7u4c92rROXJWoC/FW+O56+3/5VP7vmE+lH1eX7T\n8wz+fDCrD6/G4XYEuzxRRTTReSmKwns/HaNlQjRdm8QHu5xyu7t1Ap9uT2dd6jn6tQ3SNHN7vnci\nxi8fwZlfwBQJt0yCbpM1vwL8tbgVDz/bTrL4wi9stJ4g1hDOs3XvZGT8jaoYJizJ0Ra9KIiI5+bN\n/+Lur19gb7tBHG7ZB6WazfSsrHa127G4/2K+TfuWf+35F89vep43d7zJ4BsG87tmv6NJbJNglygC\nSBP/GlbvOM2Bs/n8fVgH1d+YfC29WtWhTqSRRf87Qd82VXhzdf45OPSN939HfwBXoXfLkn5zoP1w\niNDeLwIlKfI42VFwhv9ZT/B17gEyXTbiDOH8sc7tjIy/kUiDufSTqERGgxv5dsBLdNr2MR12fkrz\nQ99zqOVdnGh2O06ztlft9yedTke/xv3o26gvmzM28+/Uf7No7yLe2/MebWu2pUfDHtzR4A5axbeS\nYcVqptTw8ng8vPDCCxw8eBCz2czLL79Mo0aXt0Rfvnw5S5cuxWg0MmnSJHr29O/KC9tOXOQva/bR\npVEN7u9Y36/nripGg55hbWP5Z8oF5v94jEd7NPVvgHk8kJ8BOWlwdi+c3u7trrIOeZ+PTYJOY6Dd\ncGjQBTT4C8CvOTwuzrjzSbc5OenI4WBRJgeLMtlbeBaH4sao03N7VBMGxibTI7opYXpN/I52laKI\nODZ1n0xCxj5a7f+aG3csp/3OlWQmtCAjsR3ZNRuTU6Mhrmo2G7QidDodt9a7lVvr3UpmQSZfHPuC\ndWnrmLdzHv/c+U+izdG0rdmWtrXa0ji2MQ2jG9IwuiE1w2tq8hdiUYbwWrduHQ6Hg2XLlrFz505m\nz57NO++8A0BmZiaLFy9m5cqV2O12Ro0axW233YbZXPnfcDcdyWLmf/Zz4Gw+jWpG8PoDN6LXyL1d\n1zKgZQxHrEbmfHOAzccu8OHvbyr9H43LAVsWQF4GeJzgdnr/31kIhTlQlONduik33fu4T2QdqN8Z\nOjwALfp5uy2V/gN1etwszd5FlsuGS/HgUty4FQWX4saJB4fHTb7HjtVtx+pxkO0q5KL70uSXSzvN\nR+hNtAirzQPxN9I1siFdIhoQoaEuq0Q6HefqteVcvbbEXTxBw5PbqJe+kxt3LC8+xGGyUBhRg0JL\nHE6TBY/BhNtgxm0wkRebyLHmdwav/iCoHVGbCW0nMKHtBC4UXmDTmU3sOL+DPVl7WLR3EW7l8q0I\n4YZwaoTXIMYcQ2xYLDHmGMwGs/d/eu//m/QmDHoDyfHJ3N347iB+MvFrOkVRSlzyfNasWbRv354B\nAwYA0L17d3766ScAvv/+e3788UdmzpwJwOTJk3nkkUdo3779FefYvn17IGoXQgjN6Ny5c7BLqFZK\n7bysVitRUZfXezMYDLhcLoxGI1arlejoy3e7R0ZGYrVarzqH/KUJIYTwp1KvYEZFRWGzXb4R0OPx\nYDQar/mczWa7IsyEEEKIQCg1vDp16sSGDRsA2LlzJy1atCh+rn379mzfvh273U5+fj5Hjx694nkh\nhBAiEEq95uWbbXjo0CEUReGVV15hw4YNJCUl0bt3b5YvX86yZctQFIVHHnmEvn37VlXtQgghQlSp\n4QX0xgMAAAezSURBVKVWu3bt4u9//zuLFy8OdinX5XQ6mT59OqdPn8bhcDBp0iR69+4d7LKuye12\nM2PGDI4fP45Op+PFF19UdRd94cIF7r//fhYtWkSzZs2CXc413XfffcXXixs0aMCsWbOCXNH1LViw\ngPXr1+N0Ohk5ciTDhg0LdklXWbVqFatXrwbAbreTmprKxo0biYlR144NTqeTadOmcfr0afR6PS+9\n9JJqv0e1TJM3wCxcuJA1a9Zgsaj7/pY1a9YQFxfHq6++Sk5ODoMHD1ZteP3www8ALF26lJSUFObO\nnVt8S4TaOJ1Onn/+ecLD1buUld1uR1EUVf9y5ZOSksKOHTtYsmQJhYWFLFq0KNglXdP999/P/fff\nD8CLL77IkCFDVBdcAD/++CMul4ulS5eyceNGXn/9dd56661gl1XtaPKW86SkJE18M/Tr148//vGP\ngHeJK4OhCraXr6A+ffrw0ksvAXDmzBlV/lDwmTNnDg888AB16tQJdinXdeDAAQoLC5kwYQJjx45l\n586dwS7puv73v//RokULJk+ezKOPPsqdd94Z7JJKtGfPHo4cOcKIESOCXco1NWnSBLfbjcfjwWq1\nFk9wE/6lyT/Vvn37kp6eHuwyShUZGQl4bzd48sknmTJlSpArKpnRaOTZZ59l7dq1vPnmm8Eu55pW\nrVpFfHw83bt359133w12OdcVHh7OxIkTGTZsGCdOnODhhx/mm2++UeUPsuzsbM6cOcP8+fNJT09n\n0qRJfPPNN6pdeWLBggVMnjw52GVcV0REBKdPn6Z///5kZ2czf/78YJdULWmy89KSjIwMxo4dy6BB\ngxg4cGCwyynVnDlz+Pbbb/nzn/9MQYH6tnFZuXIlmzZtYsyYMaSmpvLss8+SmZkZ7LKu0qRJE373\nu9+h0+lo0qQJcXFxqqwTIC4ujttvvx2z2UzTpk0JCwvj4sWLwS7rmvLy8jh+/Di33HJLsEu5rg8+\n+IDbb7+db7/9ls8//5xp06Zht9uDXVa1I+EVQFlZWUyYMIGpU6cydOjQYJdTos8++4wFCxYAYLFY\n0Ol06PXq+/b497//zccff8zixYtJTk5mzpw51K5dO9hlXeXTTz9l9uzZAJw7dw6r1arKOsG7iMBP\nP/2EoiicO3eOwsJC4uLigl3WNW3dupVu3boFu4wSxcTEFN/vGhsbi8vlwu2W3bH9TX1jGNXI/Pnz\nycvLY968ecybNw/wTjZR40SDu+++m+eee44HH3wQl8vF9OnTVVmnVgwdOpTnnnuOkSNHotPpeOWV\nV1Q5ZAjQs2dPtm7dytChQ1EUheeff16112ePHz9OgwYNgl1GicaPH8/06dMZNWoUTqeTp556iogI\n2QnA3zQ7VV4IIUToUt+4kBBCCFEKCS8hhBCaI+ElhBBCcyS8hBBCaI6ElxBCCM2R8BKqZrfbWbFi\nRYnH9OrVq8SbQG+77Ta/1bN27VrOnTtHeno6w4cP99t5hRDlI+ElVC0zM7PU8KpKH3300TV3CxdC\nVC113jUpqr1Vq1axbt06bDYb2dnZTJ48mRo1ajB37lwMBgMNGzZk5syZzJ8/nyNHjvD2228zdOhQ\nXnjhBex2O5mZmUyZMoU+ffqU+T0PHjzIyy+/DHiXRHrllVfYv38/CxcuxGQykZ6ezj333MOkSZNI\nS0tj2rRpGI1G6tevz+nTp5k4cWLxklSvvvoqFy9e5LHHHiMzM5OWLVsWn1sIUQUUIYJg5cqVyvjx\n4xW3261kZmYqd955p9KrVy8lKytLURRFmTt3rrJs2TLl1KlTyv9v7/5dUo3iMIA/74uLmTRUUEFl\ni2sYROAiDYH9Ae8m2dTQJkhBSzlYZBC0GDREaz9syFlwCVw0GprKINwkKAjR0HruULwQdeF66fJe\n730+4+Fwznf7cobzfC3LIkmen5+zUCiQJIvFIufn50mS09PTbDQaP70rGAySJC3L4vX1NUny6OiI\n29vbLBQKnJ2dZbPZZK1W48TEBElycXGR+XyeJHl4eMhIJEKSjEQivLm5YaVS4dTUFB8fH/ny8vKh\ndhH58/TyEsdMTk7CNE309fXB7Xbj7u7OTt5vNBoIBoMf9vf392N3dxcnJycwDAOtVqut+8rlMhKJ\nBIC3mWA+nw8A4Pf74XK54HK57EiscrmMQCAA4C37L5vNfjpveHgYPT09AIDe3l7U6/W26hGR36fm\nJY65uroC8BZg/Pz8jJGREaTTaXi9XuRyOXR1dcE0Tby+vgIAdnZ2YFkWQqEQMpmMPVX3V42NjWFz\ncxNDQ0MoFot2yvtXoz/8fj8uLi4QCoVweXlprxuGAb4nqv2tI0NE/gdqXuKY+/t7RKNRPD09YXV1\nFaZpYmFhASTh8XiQSqXQ3d2NZrOJra0thMNhpFIp7O3tYWBgAA8PD23dt7a2huXlZbRaLRiGgWQy\niWq1+uXeeDyOlZUV7O/vw+v12qG6gUAAS0tL9uBOEXGGgnnFEaenp7i9vUU8Hne6lC+dnZ1hfHwc\no6OjOD4+RqlUwsbGhtNlicg7vbzkn5DL5XBwcPBpfW5uDjMzM22fNzg4iFgsBrfbDdM0sb6+/g1V\nish30ctLREQ6jj4pi4hIx1HzEhGRjqPmJSIiHUfNS0REOo6al4iIdJwflK7twdmANTAAAAAASUVO\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b0cb390>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.FacetGrid(iris, hue=\"species\", size=5) \\\n",
    "   .map(sns.distplot, \"petal_length\") \\\n",
    "   .add_legend();\n",
    "plt.show();\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zHGF-B3h7eUK",
    "outputId": "9f613f86-35a8-47f9-94e3-3fd74a25588f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Rh3tgYbbD98A0iUNEJCSEbICVV/v2/arpgTlsFiyoByYiEipCNsBqVp6vmcRhsVgIs1t1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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10abf1a20>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.FacetGrid(iris, hue=\"species\", size=5) \\\n",
    "   .map(sns.distplot, \"petal_width\") \\\n",
    "   .add_legend();\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "eKMrbu917eUU",
    "outputId": "8d2132d0-66ae-484d-a70f-2ba2574a9989"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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d6prgJl8LF94L+/4FX/3VNdcEvqn8BmODkUvTLuWCMRecdXx9XOQ4lk9cTqu1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\ntW6Y3p84FgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b8e53c8>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.FacetGrid(iris, hue=\"species\", size=5) \\\n",
    "   .map(sns.distplot, \"sepal_length\") \\\n",
    "   .add_legend();\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "RyusT9e47eUb",
    "outputId": "0cf8dda7-ece7-4fcc-f9a6-7306c0525ffa"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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KnFBYsU5FSX8ddDgc+Hy+ob+rqjqUuLZt20ZHRwcvv/wyr732Glu3bmXXrl2Z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\ne++9F0VRuPPOO9myZQuRSIS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XdyvfCj4n5IR2f1eVNzjS6aWJC2m7XY7DFlws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\nnrq4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b86fdd8>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.FacetGrid(iris, hue=\"species\", size=5) \\\n",
    "   .map(sns.distplot, \"sepal_width\") \\\n",
    "   .add_legend();\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "sUTbOpd67eUg"
   },
   "outputs": [],
   "source": [
    "# Histograms and Probability Density Functions (PDF) using KDE\n",
    "# How to compute PDFs using counts/frequencies of data points in each window.\n",
    "# How window width effects the PDF plot.\n",
    "\n",
    "\n",
    "# Interpreting a PDF:\n",
    "## why is it called a density plot?\n",
    "## Why is it called a probability plot?\n",
    "## for each value of petal_length, what does the value on y-axis mean?\n",
    "# Notice that we can write a simple if..else condition as if(petal_length) < 2.5 then flower type is setosa.\n",
    "# Using just one feature, we can build a simple \"model\" suing if..else... statements.\n",
    "\n",
    "# Disadv of PDF: Can we say what percentage of versicolor points have a petal_length of less than 5?\n",
    "\n",
    "# Do some of these plots look like a bell-curve you studied in under-grad?\n",
    "# Gaussian/Normal distribution.\n",
    "# What is \"normal\" about normal distribution?\n",
    "# e.g: Hieghts of male students in a class.\n",
    "# One of the most frequent distributions in nature.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "5w3mEOUR7eUk",
    "outputId": "bc3aeb10-a564-449e-8ca7-9b60d268d01b",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.02  0.02  0.04  0.14  0.24  0.28  0.14  0.08  0.    0.04]\n",
      "[ 1.    1.09  1.18  1.27  1.36  1.45  1.54  1.63  1.72  1.81  1.9 ]\n"
     ]
    },
    {
     "data": {
      "image/png": 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NT4CLnya07byr364KkVfTeXhZKP+39Sx93G3o1TkWbytvfO0rn2U5wHkAVsZW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\nEM3M/wN3CgDoVMol1AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bb359b0>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Need for Cumulative Distribution Function (CDF)\n",
    "# We can visually see what percentage of versicolor flowers have a \n",
    "# petal_length of less than 5?\n",
    "# How to construct a CDF?\n",
    "# How to read a CDF?\n",
    "\n",
    "#Plot CDF of petal_length\n",
    "\n",
    "counts, bin_edges = np.histogram(iris_setosa['petal_length'], bins=10, \n",
    "                                 density = True)\n",
    "pdf = counts/(sum(counts))\n",
    "print(pdf);\n",
    "print(bin_edges);\n",
    "cdf = np.cumsum(pdf)\n",
    "plt.plot(bin_edges[1:],pdf);\n",
    "plt.plot(bin_edges[1:], cdf)\n",
    "\n",
    "\n",
    "counts, bin_edges = np.histogram(iris_setosa['petal_length'], bins=20, \n",
    "                                 density = True)\n",
    "pdf = counts/(sum(counts))\n",
    "plt.plot(bin_edges[1:],pdf);\n",
    "\n",
    "plt.show();\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "KDX4yFj17eUq",
    "outputId": "5926d33d-e5fd-48eb-be13-43b5166ab05e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.02  0.02  0.04  0.14  0.24  0.28  0.14  0.08  0.    0.04]\n",
      "[ 1.    1.09  1.18  1.27  1.36  1.45  1.54  1.63  1.72  1.81  1.9 ]\n"
     ]
    },
    {
     "data": {
      "image/png": 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MfjhwlueWH+Cl7y25N9ybod386dveG3sb+c+2KZDibmqum1RTGHA3zuO/qtOk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\nddUsU05ODnPnzmXFihW4uLj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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bd2dcc0>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Need for Cumulative Distribution Function (CDF)\n",
    "# We can visually see what percentage of versicolor flowers have a \n",
    "# petal_length of less than 1.6?\n",
    "# How to construct a CDF?\n",
    "# How to read a CDF?\n",
    "\n",
    "#Plot CDF of petal_length\n",
    "\n",
    "counts, bin_edges = np.histogram(iris_setosa['petal_length'], bins=10, \n",
    "                                 density = True)\n",
    "pdf = counts/(sum(counts))\n",
    "print(pdf);\n",
    "print(bin_edges)\n",
    "\n",
    "#compute CDF\n",
    "cdf = np.cumsum(pdf)\n",
    "plt.plot(bin_edges[1:],pdf)\n",
    "plt.plot(bin_edges[1:], cdf)\n",
    "\n",
    "\n",
    "\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "TjHpJqSz7eUw",
    "outputId": "fd91266a-0ad3-4ef2-8877-0797528ba053"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.02  0.02  0.04  0.14  0.24  0.28  0.14  0.08  0.    0.04]\n",
      "[ 1.    1.09  1.18  1.27  1.36  1.45  1.54  1.63  1.72  1.81  1.9 ]\n",
      "[ 0.02  0.1   0.24  0.08  0.18  0.16  0.1   0.04  0.02  0.06]\n",
      "[ 4.5   4.74  4.98  5.22  5.46  5.7   5.94  6.18  6.42  6.66  6.9 ]\n",
      "[ 0.02  0.04  0.06  0.04  0.16  0.14  0.12  0.2   0.14  0.08]\n",
      "[ 3.    3.21  3.42  3.63  3.84  4.05  4.26  4.47  4.68  4.89  5.1 ]\n"
     ]
    },
    {
     "data": {
      "image/png": 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w8/ahd8zVckfpUnWHMjDqdLiNHCV3FKEDbLxw/75PidjSVWiD6tJiTqUm28Wg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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x100721278>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plots of CDF of petal_length for various types of flowers.\n",
    "\n",
    "# Misclassification error if you use petal_length only.\n",
    "\n",
    "counts, bin_edges = np.histogram(iris_setosa['petal_length'], bins=10, \n",
    "                                 density = True)\n",
    "pdf = counts/(sum(counts))\n",
    "print(pdf);\n",
    "print(bin_edges)\n",
    "cdf = np.cumsum(pdf)\n",
    "plt.plot(bin_edges[1:],pdf)\n",
    "plt.plot(bin_edges[1:], cdf)\n",
    "\n",
    "\n",
    "# virginica\n",
    "counts, bin_edges = np.histogram(iris_virginica['petal_length'], bins=10, \n",
    "                                 density = True)\n",
    "pdf = counts/(sum(counts))\n",
    "print(pdf);\n",
    "print(bin_edges)\n",
    "cdf = np.cumsum(pdf)\n",
    "plt.plot(bin_edges[1:],pdf)\n",
    "plt.plot(bin_edges[1:], cdf)\n",
    "\n",
    "\n",
    "#versicolor\n",
    "counts, bin_edges = np.histogram(iris_versicolor['petal_length'], bins=10, \n",
    "                                 density = True)\n",
    "pdf = counts/(sum(counts))\n",
    "print(pdf);\n",
    "print(bin_edges)\n",
    "cdf = np.cumsum(pdf)\n",
    "plt.plot(bin_edges[1:],pdf)\n",
    "plt.plot(bin_edges[1:], cdf)\n",
    "\n",
    "\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "1JykhrwO7eUz"
   },
   "source": [
    "# (3.5) Mean, Variance and Std-dev"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "7rhG9mB17eU0",
    "outputId": "d6383c6f-1007-4876-9907-ef4dc0a58982"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Means:\n",
      "1.464\n",
      "2.41568627451\n",
      "5.552\n",
      "4.26\n",
      "\n",
      "Std-dev:\n",
      "0.171767284429\n",
      "0.546347874527\n",
      "0.465188133985\n"
     ]
    }
   ],
   "source": [
    "#Mean, Variance, Std-deviation,  \n",
    "print(\"Means:\")\n",
    "print(np.mean(iris_setosa[\"petal_length\"]))\n",
    "#Mean with an outlier.\n",
    "print(np.mean(np.append(iris_setosa[\"petal_length\"],50)));\n",
    "print(np.mean(iris_virginica[\"petal_length\"]))\n",
    "print(np.mean(iris_versicolor[\"petal_length\"]))\n",
    "\n",
    "print(\"\\nStd-dev:\");\n",
    "print(np.std(iris_setosa[\"petal_length\"]))\n",
    "print(np.std(iris_virginica[\"petal_length\"]))\n",
    "print(np.std(iris_versicolor[\"petal_length\"]))\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "abmP92Sn7eU4"
   },
   "source": [
    "# (3.6) Median, Percentile, Quantile, IQR, MAD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "cICgORTF7eU5",
    "outputId": "bf2a36d9-e954-4ce1-ce5e-69c8999a743b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Medians:\n",
      "1.5\n",
      "1.5\n",
      "5.55\n",
      "4.35\n",
      "\n",
      "Quantiles:\n",
      "[ 1.     1.4    1.5    1.575]\n",
      "[ 4.5    5.1    5.55   5.875]\n",
      "[ 3.    4.    4.35  4.6 ]\n",
      "\n",
      "90th Percentiles:\n",
      "1.7\n",
      "6.31\n",
      "4.8\n",
      "\n",
      "Median Absolute Deviation\n",
      "0.148260221851\n",
      "0.667170998328\n",
      "0.518910776477\n"
     ]
    }
   ],
   "source": [
    "#Median, Quantiles, Percentiles, IQR.\n",
    "print(\"\\nMedians:\")\n",
    "print(np.median(iris_setosa[\"petal_length\"]))\n",
    "#Median with an outlier\n",
    "print(np.median(np.append(iris_setosa[\"petal_length\"],50)));\n",
    "print(np.median(iris_virginica[\"petal_length\"]))\n",
    "print(np.median(iris_versicolor[\"petal_length\"]))\n",
    "\n",
    "\n",
    "print(\"\\nQuantiles:\")\n",
    "print(np.percentile(iris_setosa[\"petal_length\"],np.arange(0, 100, 25)))\n",
    "print(np.percentile(iris_virginica[\"petal_length\"],np.arange(0, 100, 25)))\n",
    "print(np.percentile(iris_versicolor[\"petal_length\"], np.arange(0, 100, 25)))\n",
    "\n",
    "print(\"\\n90th Percentiles:\")\n",
    "print(np.percentile(iris_setosa[\"petal_length\"],90))\n",
    "print(np.percentile(iris_virginica[\"petal_length\"],90))\n",
    "print(np.percentile(iris_versicolor[\"petal_length\"], 90))\n",
    "\n",
    "from statsmodels import robust\n",
    "print (\"\\nMedian Absolute Deviation\")\n",
    "print(robust.mad(iris_setosa[\"petal_length\"]))\n",
    "print(robust.mad(iris_virginica[\"petal_length\"]))\n",
    "print(robust.mad(iris_versicolor[\"petal_length\"]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6OHiqoR-7eU9"
   },
   "source": [
    "# (3.7) Box plot and Whiskers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "s4ZG6dZw7eU_",
    "outputId": "e71ac9d1-fb27-4825-e75e-21d8fbc3f072",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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cLiZPnsz333/PyZMnycjI4Oabb/Z3NyIi8it+D/M33niD6Ohonn76aSorK7njjjsU5iIi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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bf186a0>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#Box-plot with whiskers: another method of visualizing the  1-D scatter plot more intuitivey.\n",
    "# The Concept of median, percentile, quantile.\n",
    "# How to draw the box in the box-plot?\n",
    "# How to draw whiskers: [no standard way] Could use min and max or use other complex statistical techniques.\n",
    "# IQR like idea.\n",
    "\n",
    "#NOTE: IN the plot below, a technique call inter-quartile range is used in plotting the whiskers. \n",
    "#Whiskers in the plot below donot correposnd to the min and max values.\n",
    "\n",
    "#Box-plot can be visualized as a PDF on the side-ways.\n",
    "\n",
    "sns.boxplot(x='species',y='petal_length', data=iris)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3S8dI16V7eVC"
   },
   "source": [
    "# (3.8) Violin plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ha1SwMC47eVE",
    "outputId": "629aa3d3-15b9-473b-bdd8-95d115bb0634"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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hatCiRQtXlyBEvUiYC1EDCXPR1EiYC1GDy2crC9EUSJgLUQOj0ejqEoSoFwlz\nIS5zaXiihLloahy60JYQTc28efNITk4mMDDQ1aUIUS8S5kJcpkuXLnTp0sXVZQhRb9LNIoQQbkDC\nXAgh3ICEuRBCuAEJcyGEcAMS5kII4QYkzIUQwg1ImAshhBuQMBdCCDcgYS6EEG5AwlwIIdyAhLkQ\nQrgBu6/NYjKZeOmllzhz5gxVVVVMnjyZQYMG2fsyQgghLmP3MF+7di2BgYH8/e9/p7CwkHvuuadZ\nhfnRo0fZunUrHTt2pH///q4uRwjRTNg9zIcNG8bQoUMB0DQNvV5v70s0WpWVlbz8ylzOZGag0+tZ\n8p//EBkZ6eqyhBDNgN37zH19ffHz86OkpIRp06Yxffp0e1+iUVJVlX/84x+cycygMvo3aDoP/jx7\nNsXFxa4uTQjRDDhkPfOzZ88yZcoUHnjgAUaOHFnjMampqY64tEtomsayZcv48ccfqYpIxNyyE2We\nvpxM+5YpU6fyzPTpeHt7u7pMIYQbs3uY5+Xl8eijj/Lyyy/Tr1+/Wo+Lj4+396VdZvny5fz444+Y\nWnfDFN4TALVFWyo6DuTksQ189tlyXnvtVRRFcXGlQoimLDk5udbX7N7N8sEHH1BUVMR7773HxIkT\nmThxIhUVFfa+TKNx4cIFFv3rX5iDoqhq1xsuC2xLcHsqI3uzffs2du7c6cIqhRDuzu4t89mzZzN7\n9mx7n7bR2r17N6aqKkyxPa8I8kvMYV3wztrL1q1b6du3rwsqFEI0BzJpqIF++uknFL0B1Sek5gN0\nOsy+oezavQeTyeTc4oQQzYaE+Q26cOECb7/9Nt999x1VLTuDrvYvpSksgeyzWTz77ExOnTrlxCqF\nEM2FQ0azuCuz2czevXv5/vvv2bBxI1WVlZjCulAVeUud77MERVEZ/Rv2HfyJiQ89RO/evRk2dCi3\n3norPj4+TqpeCOHOJMyvIycnh+TkZHbt2sVPO3dSVlqK4mGgKigaU+uuaD5BNp3H3LITJYGRGHJS\n2L33ELt27kTv4UHPnj3p07s3vXr1Ijo6Gl0dLXwhhKiNhPllVFUlIyODAwcOsH//fn7+ZS+5OdkA\nKJ4+VAW0xRIehSUwAnQ1f+k8zh0FwNwy9toXDUZMEYmY2t6MrjgHj4KTJKccI3nPHgB8/fy5qWcP\nevToQbdu3YiNjcVgMDjmkxVCuJVmHeYFBQUcPnyY1NRUUlJSOHQohdLSEgAUTyMm31ZYovpiCQhH\nMwbVOFqikiXJAAARqElEQVTlah7n0oBawvwSRUENaE1VQGuqAKWyBH1RFqais2xN3s/WrVurz2Uw\nENepE126dCE+Pp7OnTsTHh4u49WFENdoFmGuaRq5ubkcO3aMo0ePcuTIEVIPp3E+71z1AYoCPkGY\nfNqitmqFxT8MzbuFTeF91YVQqsrAUoVHTirmVp1tOofm5Ye5ZSdo2ak63KtK0ZXkoi/O4cDpc6Sk\nrkZTzQD4+voRF9eJTp06ERsbS2xsLJGRkc1qDRwhxLXcLswrKys5efIk6enpHDt2jGPp6Rw9eozS\nksvWSPEJxGwMwdIuGtU3FNU3FPQN787wyE1FV1kEgNfJbYCGOSyh3ufRPH2xBHfAEtyh+glVRVee\nj640D1NpHslHMvh57z5QLQAYPD3p0KEDnWJjiYmJsf7x9/dv8OckhGgamnSYFxcXc+TIEY4cOcLR\no0dJO3KUM5kZqKoKgKI3YDEGYTGGo4aGoPqEoPoE2yW4a6IvOH3NxzcS5tfQ6X79RweoAlBVlIoC\n9KX5mMrOk3o2n6PH16OZvrK+LbRlK+I6xVpb8J07d6Zly5YNr0cI0eg0qTDPy8tjz5497N27l/0H\nDpCZkWF9TfH2w+QdhNq6uzW0NW9/UJw3OkS52BVS28d2pdOh+YRg9gkBLvbPaxqKqQxdWT66snyy\ny86T98shtm3bZn1bUHAI3bt1pXv37vTq1Yv27dtLH7wQbqDRh7mmaWzcuJEVK1eScugQAIrBG5Nv\nK9SIRFTfllh8Q8HQCFYltFRhNBoZMWIESUlJlFiqnHt9RanuovH0xRJYvY56JYDFVB3wpXmcK8ll\n886f2bRpEwBtwsP5/ciRjBkzBi8vL+fWK4Swm0Yf5h9//DGLFy8Gn0CqIhKxBLar7ipphK1JxVzF\niN+PYNq0aQAsX/uNiyu6SG9A9Q9D9Q/DTBcqAaWyGH1hJpn5x1m0aBHbd+zg/737rqsrFULcoEYf\n5unp6QBUhnauHu7n0Xhbj5qHJ0lJSQAkJSWheRhdXFHtNC9/zC07oRmM6EvzSD+WjqZp0uUiRBPV\n6MP86aef5kJREQf2/4R35m7M/m0wt2iLGtDmYgu9Ec2Y1HtSXpzPypUrqz/2b+Haeq6madVj2ovP\nor9wBkNRJpqpkrCw1rzwwiwJciGasEYf5m3atOH/vfsuhw8fZv369Wzbtp0zp6vXBlc8PDH7hGLx\nbYnqVz3aQ/P0c1kXjHbVrNCrP3Y6UwX60jx0pXnoSs9hKDuHVlkGQECLQPoNHMAdd9xB79698fBo\n9D8KQog6NJnf4M6dO9O5c2emTp1KTk4O+/bt4+DBgxw4eJATJw6inq0ec60YvDEbg6wjWlSfEFRj\nIOgcP6nGEtQOjwuZV3zsFJqGUlmErjQfXdl5dGX5GCry0SpKrIe0CW9L11630bVrV3r06EH79u1l\nHRgh3EiTCfPLhYWFMWTIEIYMGQJUTxRKT0+/bLz5EU4cP4Ip++JoEkUHxhaYjcEXA776j2bwsWsr\n3twqHsPZg2CpwhSRWD0D1N7MlejKCqyhrS8vQF9egGapXitdURQiIiKJ69WXjh07EhcXR2xsLAEB\nAfavRQjRaDTJML+al5cXCQkJJCT8OkHHYrGQmZlJenq6dTbokaPHOJ+Rbj1G8TRi9g7C4ntxQpFv\n6I1N47eeUEHz9AF8MIc1cI/TS2PGS/PQlZ5HV3YeQ3k+WsWvM1l9/fyJje1ITHR/OnbsSExMDB06\ndJAhhkI0Q24R5jXR6/VERUURFRXFwIEDrc8XFxdfEfBHjx7j+InDmC/uAlTdDx+Cxa8Vql8YFv8w\n54ygUc3oSs6hL8lFV5yDoTzP2r+tKArhbdsSd9MtdOzY0fonJCREbloKIQA3DvPa+Pv707NnT3r2\n7Gl9zmw2c+rUKdLS0jh8+DApKakcSz+ImrUPAM03BFNAePUYd/8wu42gUcoK8Cg8jf5CJh4luWgX\n11oJbxtBtz796dy5M506dSImJkY2sRBC1KnZhXlNPDw8rItT3XnnnQBUVFRw+PBh9u3bx57kZA4e\nOIDl7IHqdc2DozGFJaB5X9sPbW4ZV/fFzJV4nEvDK+8olBUA0CE6mluG3UvPnj3p2rUrgYGBdv8c\nhRDuTdE0TXP2RZOTk0lMTHT2ZRuktLSUnTt3smHDBrZt346qqphCY6mK7G3bUgKaiuHsAbzO7kMz\nV5GQkMCQIUPo37+/LH4lhLBJXdnpsJb5vn37+Mc//sGSJUscdQmn8vX1ZeDAgQwcOJC8vDyWLVvG\nylWrMBRlURY3tHrzitpYqjAeWY+uKIt+t93GpEcfJTa2js0rhBCinhwS5h9++CFr167FaGy809kb\nIjQ0lKlTp/K73/2O555/HuXIt5Qk3FNzC13T8D72Ax4l2cx68UWGDx/u/IKFEG7PIbNG2rVrx8KF\nCx1x6kYlLi6Ov7/5JoqpHM+Ls1Kv5pF3FH1hBk8//bQEuRDCYRwS5kOHDm0208Pj4uIYN3Yshryj\n6Iqzr3zRXIl35h7i4xMYNWqUawoUQjQLLkvc1NRUV13a7vr06cPX69ahndhKWdd74OKaLJ6nfgJT\nBaNHjyItLc3FVQoh3JnLwjw+voEzJBuZObNn8+yzz+KZsYeqqL7oC05hyDvKhIkTGTp0qKvLE0K4\ngeTk5Fpfk5WW7OSWW27h7rvvxpB9EF1RNsZT24mOjuGRRx5xdWlCiGbAYWEeERHB8uXLHXX6RumJ\nJ57Azz8AY+pXaJWlPPvsDAwGx2weLYQQl5OWuR35+flx14jqGaQdO3akW7duLq5ICNFcNI8hJ050\n9913k5eXJ/3kQginkjC3s7Zt2/Lyyy+7ugwhRDMj3SxCCOEGJMyFEMINSJgLIYQbkDAXQgg3IGEu\nhBBuQMJcCCHcgIS5EEK4AZeNM69rwRghhBD145I9QIUQQtiXdLMIIYQbkDAXQgg3IGFuB2lpaeze\nvdvVZYgbtHnzZj777LN6vWfhwoUsXbrUQRWJ+nxPzp07x9y5c2t9PTU1lXfffddOlTVe0mduBwsX\nLiQ0NJTx48e7uhThJPI9F42NrJpYhxMnTvDiiy/i4eGBqqrMnz+fTz/9lD179qCqKo888gg333wz\na9aswWAw0KVLF4qLi3n77bfx8vIiMDCQV199FbPZzPTp09E0jcrKSubNm0d8fDzz58/n4MGDFBYW\n0rlzZ1577TVXf8pNytSpU3nooYfo3bs3Bw4csAbsqVOnUFWV6dOn06dPH+666y7at2+PwWBgwoQJ\nvPHGG3h4eGA0GvnnP//Jd999x/Hjx5k5cybvvfce69evx2KxMH78eMaNG8fHH39MUlISHh4e9OrV\ni+eee+6KOl5//XXr6Ky77rqLhx9+mBdeeIHCwkIKCwtZtGgRLVq0cMWXqMm4+nv5yCOPWL/+kydP\nJjAwkN/85jf06dOHefPm4evrS0hICF5eXkydOpUZM2awfPlyRo4cSe/evUlLS0NRFN577z1SUlJY\ntmwZb731FitWrGDp0qWoqsrAgQOZNm0a//d//8d3331HeXk5QUFBvPvuu3h6err6S1JvEuZ12L59\nO927d+e5555jz549rF+/nszMTJYuXUplZSX3338/S5YsYdSoUYSGhtKtWzcGDRrE0qVLCQsLY/Hi\nxbz//vv06dOHwMBA3nzzTY4dO0ZZWRklJSUEBATwySefoKoqI0aMICcnh7CwMFd/2k3Gfffdx5o1\na+jduzerV6+mf//+ZGdn8+qrr1JQUMCECRNISkqirKyMp556ioSEBN544w2GDx/Oww8/zMaNGykq\nKrKeLyUlhc2bN7NixQosFgsLFiwgLS2NdevWsWzZMjw8PHj66af54YcfrO/54YcfyMzMZPny5ZjN\nZh544AH69u0LQN++fWXbQBtd/b185plnyM7OBqq7UVatWoWnpyejRo3izTffJDY2lrfeeoucnJwr\nzlNaWsqIESOYM2cOzz77LJs3byY0NBSA8+fP8+GHH7J27Vq8vLyYP38+JSUlFBYW8r//+7/odDom\nTZrEgQMHSExMdPrXoKEkzOswZswYPvzwQx577DH8/f3p3Lkzhw4dYuLEiQCYzWbOnDljPb6goAA/\nPz9rIN9yyy0sWLCA5557jpMnT/LUU0/h4eHB5MmT8fLyIj8/nxkzZuDj40NZWRkmk8kln2dT1b9/\nf/7+979TWFho/d/Szz//zP79+4Hq709+fj4AHTp0AODJJ5/kgw8+4OGHHyYsLIzu3btbz3fixAm6\nd++OXq9Hr9fzwgsvsG7dOnr06GHd/q9Xr14cPXrU+p709HR69eqFoigYDAZ69OhBenr6FdcU13f1\n9zIhIcH6WkREhLWlnJubS2xsLACJiYl8/fXX15zr0nvbtGlDZWWl9fmMjAxiY2Px9vYGYObMmQAY\nDAbr72F2djZms9kxn6SDyQ3QOmzYsIHExEQWL17MsGHDWL16NX369GHJkiUsXryY4cOHExkZiaIo\nqKpKUFAQJSUl5ObmArBr1y7at2/Pzp07adWqFR9//DGTJ09mwYIFbN68mbNnz7JgwQJmzJhBRUUF\ncvuifnQ6HcOGDWPu3LkMHjyYmJgYRowYwZIlS/jwww8ZNmwYgYGB1mMB1q5dy6hRo1iyZAmxsbFX\n7FMbHR1NSkoKqqpiMpn4wx/+QIcOHdi/fz9msxlN09i9e/cVIR0TE2PtYjGZTPzyyy9ERUUBoCiK\ns74UTd7V30u9Xn/Fa5e0bt2aY8eOAbBv374az1Xb171du3YcP36cqqoqAKZNm8auXbtYv349b7/9\nNnPmzEFV1Sb7eygt8zp07dqVWbNm8f7776OqKu+88w5ffvklDzzwAGVlZQwePBg/Pz+6du3Km2++\nSUxMDH/96195+umnURSFFi1a8Nprr6EoCjNmzGDp0qWYzWamTJlCXFwc7733Hg8++CCKohAZGUlu\nbi6RkZGu/rSblHvvvZfBgwfz7bff0qpVK2bPns2ECRMoKSnhgQceuCIIALp3787s2bMxGo3odDr+\n53/+xzoSKT4+nv79+zN+/HhUVWX8+PF07tyZ4cOHW59LTExk8ODBHD58GIA77riDXbt2MXbsWEwm\nE8OGDaNLly5O/zq4g8u/l7t27arxmFdeeYWXXnoJHx8fDAZDvbolg4ODefzxx5kwYQKKonDHHXfQ\nrVs3jEYj48aNA6Bly5bWxlhTI6NZhBBNxn//+1+GDx9OcHAwb731FgaDgalTp7q6rEZBWuZCiCYj\nJCSERx99FB8fH/z9/Xn99dddXVKjIS1zIYRwA3IDVAgh3ICEuRBCuAEJcyGEcAMS5kLUobks0iSa\nPrkBKoQQbkCGJgq3cvXiaPfffz9ffPEFOp2Oc+fOMXbsWB588EHS0tL461//CmBdEM3Pz4+//OUv\n7N+/H5PJxNNPP42/v791kaZ169ZZ1/BITExk5syZJCcnX7Nwl5+fn4u/CqI5kjAXbuXqxdHS09PJ\nycnh888/R1VVRo4cybBhw5gzZw6vvvoqHTt2ZMWKFXz00Ud07dqVgoICVq5cyYULF/jkk0/o168f\nAIWFhSxcuJBVq1ZhNBp57rnn2LZtG1u3br1m4S4Jc+EKEubCrVy9ONptt93GTTfdZF2oKTY2ltOn\nT5Oens68efOA6jVV2rdvj6+vLz179gSgRYsWTJ8+nZ07dwJw+vRp8vPz+eMf/whUr853+vTpOhfu\nEsKZ5AaocCtXL4724YcfkpqaisVioby8nGPHjhEVFUWHDh144403WLJkCc899xwDBgwgOjqaAwcO\nAFBcXMykSZOs542IiKBNmzZ8/PHHLFmyhAkTJtCzZ886F+4SwpmkZS7cytWLo02cOJE1a9bw+OOP\nU1hYyOTJkwkODmbu3LnMmjULs9mMoij87W9/o3379uzYsYPx48djsViYMmWK9bzBwcE88sgjTJw4\nEYvFQtu2bRk+fDhVVVXXLNwlhCvIaBbh1nbu3Gm9gSmEO5NuFiGEcAPSMhdCCDcgLXMhhHADEuZC\nCOEGJMyFEMINSJgLIYQbkDAXQgg3IGEuhBBu4P8Dg1HEVVRI/AkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a3660f0>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# A violin plot combines the benefits of the previous two plots \n",
    "#and simplifies them\n",
    "\n",
    "# Denser regions of the data are fatter, and sparser ones thinner \n",
    "#in a violin plot\n",
    "\n",
    "sns.violinplot(x=\"species\", y=\"petal_length\", data=iris, size=8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "axQROeiL7eVK"
   },
   "source": [
    "# (3.9) Summarizing plots in english\n",
    "* Exaplain your findings/conclusions in plain english\n",
    "* Never forget your objective (the probelm you are solving) . Perform all of your EDA aligned with your objectives.\n",
    "\n",
    "# (3.10) Univariate, bivariate and multivariate analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "M0TFvvkr7eVL",
    "outputId": "3c4efcfb-03b1-4b9d-e72b-c58de1b6ca9b"
   },
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-20-f25211abae88>, line 3)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-20-f25211abae88>\"\u001b[0;36m, line \u001b[0;32m3\u001b[0m\n\u001b[0;31m    Def: Univariate, Bivariate and Multivariate analysis.\u001b[0m\n\u001b[0m                   ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "Def: Univariate, Bivariate and Multivariate analysis.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "k1TGpk-77eVP"
   },
   "source": [
    "# (3.11) Multivariate probability density, contour plot.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "C9HUB0R37eVQ"
   },
   "outputs": [],
   "source": [
    "#2D Density plot, contors-plot\n",
    "sns.jointplot(x=\"petal_length\", y=\"petal_width\", data=iris_setosa, kind=\"kde\");\n",
    "plt.show();\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "64iyVP9u7eVU"
   },
   "source": [
    "# (3.12) Exercise:\n",
    "\n",
    "1. Download Haberman Cancer Survival dataset from Kaggle. You may have to create a Kaggle account to donwload data. (https://www.kaggle.com/gilsousa/habermans-survival-data-set)\n",
    "2. Perform a similar alanlaysis as above on this dataset with the following sections:\n",
    "* High level statistics of the dataset: number of points, numer of   features, number of classes, data-points per class.\n",
    "* Explain our objective. \n",
    "* Perform Univaraite analysis(PDF, CDF, Boxplot, Voilin plots) to understand which features are useful towards classification.\n",
    "* Perform Bi-variate analysis (scatter plots, pair-plots) to see if combinations of features are useful in classfication.\n",
    "* Write your observations in english as crisply and unambigously as possible. Always quantify your results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "B1ttMbq67eVU"
   },
   "outputs": [],
   "source": [
    "iris_virginica_SW = iris_virginica.iloc[:,1]\n",
    "iris_versicolor_SW = iris_versicolor.iloc[:,1]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "bRfRxR0X7eVX"
   },
   "outputs": [],
   "source": [
    "from scipy import stats\n",
    "stats.ks_2samp(iris_virginica_SW, iris_versicolor_SW)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "g0mtgBgD7eVa"
   },
   "outputs": [],
   "source": [
    "x = stats.norm.rvs(loc=0.2, size=10)\n",
    "stats.kstest(x,'norm')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "KYFI7qk-7eVd"
   },
   "outputs": [],
   "source": [
    "x = stats.norm.rvs(loc=0.2, size=100)\n",
    "stats.kstest(x,'norm')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "CVMtqP_h7eVh"
   },
   "outputs": [],
   "source": [
    "x = stats.norm.rvs(loc=0.2, size=1000)\n",
    "stats.kstest(x,'norm')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Sy-AO9SZ7eVj"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "name": "Exploratory Data Analysis..ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}