Section_10_Reading_And_Writing_Data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Reading And Writing Data\n",
    "\n",
    "- [Overview](#Overview)\n",
    "- [Data From Excel](#Data-From-Excel)\n",
    "- [Data Into Python](#Data-Into-Python)\n",
    "- [Dictionaries Introduction](#Dictionaries-Introduction)\n",
    "- [Data From Python](#Data-From-Python)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Overview\n",
    "\n",
    "- In *Excel* $\\to$ save as `.csv` or `.xlsx` files\n",
    "- In *Python* $\\to$ use the `pandas` module\n",
    "- Further `pandas` documentation:\n",
    "    - [10 Minutes to Pandas](http://pandas.pydata.org/pandas-docs/stable/10min.html)\n",
    "    - [Pandas Tutorials](http://pandas.pydata.org/pandas-docs/stable/tutorials.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Getting Data In/Out\n",
    "\n",
    "### Data From *Excel*\n",
    "\n",
    "- Saving data $\\to$ Save As $\\to$ `xlsx Spreadsheet (.xlsx)`\n",
    "- Saving data $\\to$ Save As $\\to$ `Text CSV (.csv)`\n",
    "    - Export settings:\n",
    "        - Field delimiter $\\to$ separates data by this character\n",
    "- Saving data $\\to$ Save As $\\to$ `Microsoft Excel (.xlsx)`\n",
    "- `.csv` vs `.xlsx`\n",
    "    - `.csv` $\\to$ raw text data $\\to$ no formula are stored $\\to$ only values stored\n",
    "    - `.xlsx` $\\to$ store formula and values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Data Into *Python*\n",
    "\n",
    "- Load data with `pandas`:\n",
    "    - `pandas.read_csv(filename)`\n",
    "    - `pandas.read_excel(filename, sheet_name=sheet)`\n",
    "\n",
    "### Outcomes\n",
    "- load data into *Python*\n",
    "- `pandas` data frame object\n",
    "    - `index` $\\to$ row indexes\n",
    "    - `columns` $\\to$ column headings\n",
    "    - `values` $\\to$ `numpy` array of data\n",
    "- selecting data\n",
    "    - selecting one or more columns of data\n",
    "    - selecting one or more rows of data\n",
    "- plot darts (scatter plot)\n",
    "- plot histogram of darts distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    x coord  y coord   dist  in circle\n",
      "0     1.405   -1.217  1.859       True\n",
      "1     1.814   -0.096  1.817       True\n",
      "2    -0.857    1.391  1.633       True\n",
      "3    -0.125   -0.258  0.286       True\n",
      "4     1.714   -1.784  2.474      False\n",
      "5     0.535    0.609  0.811       True\n",
      "6     0.150    0.055  0.160       True\n",
      "7    -0.731    1.646  1.801       True\n",
      "8     0.205   -1.371  1.387       True\n",
      "9     1.096    0.639  1.269       True\n",
      "10   -1.733   -1.491  2.286      False\n",
      "11    1.287   -1.864  2.265      False\n",
      "12    0.548    0.233  0.595       True\n",
      "13   -1.293   -1.285  1.823       True\n",
      "14    0.335    0.204  0.392       True\n",
      "15   -0.574    0.257  0.629       True\n",
      "16   -1.296   -0.828  1.538       True\n",
      "17    0.560    1.776  1.862       True\n",
      "18    0.467    0.668  0.816       True\n",
      "19   -1.986   -0.816  2.147      False\n",
      "20    1.264    1.437  1.914       True\n",
      "21    0.825   -0.538  0.985       True\n",
      "22   -0.429    1.559  1.617       True\n",
      "23   -0.448   -0.358  0.574       True\n",
      "24    0.116    0.126  0.171       True\n",
      "25   -1.562    1.086  1.903       True\n",
      "26    1.677   -1.561  2.291      False\n",
      "27   -0.102    0.235  0.256       True\n",
      "28   -1.494    1.144  1.881       True\n",
      "29   -0.808   -0.845  1.169       True\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "#pdata = pd.read_csv('data_files/darts_data.csv')\n",
    "pdata = pd.read_csv('data_files\\darts_data.csv')\n",
    "print(pdata)\n",
    "# print(pdata.index)\n",
    "# print(pdata.columns)\n",
    "# print(pdata.values)\n",
    "# print(pdata[['x coord', 'y coord']])\n",
    "# print(pdata.loc[[0, 1]])\n",
    "\n",
    "plt.figure(1)\n",
    "plt.plot(pdata['x coord'], pdata['y coord'], 'r*')\n",
    "\n",
    "plt.figure(2)\n",
    "plt.hist(pdata['dist'])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "#pdata = pd.read_excel('data_files/darts_data.xlsx', sheet_name='Sheet1')\n",
    "pdata = pd.read_excel('data_files\\darts_data.xlsx', sheet_name='Sheet1')\n",
    "# print(pdata)\n",
    "# print(pdata.index)\n",
    "# print(pdata.columns)\n",
    "# print(pdata.values)\n",
    "# print(pdata[['x coord', 'y coord']])\n",
    "# print(pdata.loc[[0, 1]])\n",
    "\n",
    "plt.figure(1)\n",
    "plt.plot(pdata['x coord'], pdata['y coord'], 'r*')\n",
    "\n",
    "plt.figure(2)\n",
    "plt.hist(pdata['dist'])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dictionaries Introduction\n",
    "\n",
    "- Dictionaries allow us to group objects under 1 name using keywords\n",
    "- Created using curly brackets `{}`\n",
    "- Can store any object types or mix of object types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'x': [2, 5, 1, 0, 9], 'y': [1], 'z': [0, 1, 2], 'name': 'SomeName'}\n",
      "[2, 5, 1, 0, 9]\n",
      "SomeName\n",
      "2\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "var = {\n",
    "    'x': [2, 5, 1, 0, 9],\n",
    "    'y': [1],\n",
    "    'z': [0, 1, 2],\n",
    "    'name': 'SomeName'\n",
    "}\n",
    "\n",
    "print(var)\n",
    "print(var['x'])\n",
    "print(var['name'])\n",
    "\n",
    "print(var['z'][2])\n",
    "\n",
    "lst = var['z']\n",
    "print(lst[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'dict'>\n",
      "[4 2 5 1]\n",
      "[ 8  4 10  2]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "data = {\n",
    "    'x': np.array([4, 2,  5, 1]),\n",
    "    'y': np.array([8, 4, 10, 2])\n",
    "}\n",
    "\n",
    "print(type(data))\n",
    "print(data['x'])\n",
    "print(data['y'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- `pandas` data frame objects are just \"cleaver\" dictionary like objects"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example - Calculating molar mass\n",
    "\n",
    "- Calculate the molar mass of Glucose\n",
    "$$C_6 H_{12} O_6$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "180\n"
     ]
    }
   ],
   "source": [
    "atomic_weight = {\n",
    "    'H': 1,\n",
    "    'C': 12,\n",
    "    'N': 14,\n",
    "    'O': 16,\n",
    "    'S': 32\n",
    "}\n",
    "\n",
    "syms = ['C', 'H', 'O']\n",
    "mult = [ 6,   12,  6 ]\n",
    "\n",
    "mass = 0\n",
    "for i in range(len(syms)):\n",
    "    s = syms[i]\n",
    "    m = mult[i]\n",
    "    w = atomic_weight[s]\n",
    "    mass = mass + (w * m)\n",
    "print(mass)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "180\n"
     ]
    }
   ],
   "source": [
    "atomic_weight = {'H': 1, 'C': 12, 'N': 14, 'O': 16, 'S': 32}\n",
    "\n",
    "def molar_mass(formula):\n",
    "    total = 0\n",
    "    for character in formula:\n",
    "        if character in atomic_weight:\n",
    "            weight = atomic_weight[character]\n",
    "            multiplier = 1\n",
    "        else:\n",
    "            multiplier = int(character) - 1\n",
    "        total = total + weight * multiplier\n",
    "    return total\n",
    "\n",
    "\n",
    "formula = ['C', 6, 'H', 12, 'O', 6]\n",
    "mass = molar_mass(formula)\n",
    "print(mass)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Data From *Python*\n",
    "\n",
    "- Create a `pandas` data frame object\n",
    "- Write data:\n",
    "    - `pandas.to_csv(filename)`\n",
    "    - `pandas.to_excel(filename, sheet_name=sheet)`\n",
    "\n",
    "### Outcomes\n",
    "- Creating a `pandas` data frame object\n",
    "- Writing the data to `.csv` or `.xlsx` files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "x = np.linspace(0, 10, 100)\n",
    "y = x**2 + 10\n",
    "\n",
    "data = {\n",
    "    'x coords': x,\n",
    "    'y coords': y,\n",
    "}\n",
    "\n",
    "pdata = pd.DataFrame(data)\n",
    "\n",
    "pdata.to_csv('mydata.csv')\n",
    "pdata.to_excel('mydata.xlsx', sheet_name='Sheet2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example - Experimental Data\n",
    "\n",
    "- Consider an experimental setup whereby air is forced through a rectangular channel. Inside this channel there are several obstacles (cylindrical pins) that obstruct the flow.\n",
    "\n",
    "\n",
    "- The flow velocity and pressure is measured at 39 different points along the the channel with pressure taps\n",
    "\n",
    "\n",
    "- 50 data readings are taken for each of the pressure taps\n",
    "\n",
    "\n",
    "- Plot an error bar plot showing the statistical accuracy of the data readings for each probe\n",
    "\n",
    "\n",
    "###  Outcomes:\n",
    "\n",
    "- `array.reshape` function\n",
    "\n",
    "\n",
    "- Box plot (Error bar plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "#%matplotlib qt5\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "dataframe = pd.read_csv('data_files/experimental_data.csv', sep='\\t')\n",
    "\n",
    "renolds = dataframe['Re'].values\n",
    "renolds = renolds.reshape((50, 39))\n",
    "\n",
    "plt.boxplot(renolds)\n",
    "plt.title(\"Error Bar Plot of Reynolds Number\")\n",
    "plt.xlabel(\"Probe Number\")\n",
    "plt.ylabel(\"Reynolds Number\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Re-shape in Numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 6],\n",
       "       [2, 7],\n",
       "       [3, 8],\n",
       "       [4, 9],\n",
       "       [5, 0]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "aa = np.array([1,2,3,4,5,6,7,8,9,0])\n",
    "bb = np.copy(aa)\n",
    "aa = np.reshape(aa,(5,2),'F')\n",
    "aa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2],\n",
       "       [3, 4],\n",
       "       [5, 6],\n",
       "       [7, 8],\n",
       "       [9, 0]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bb = np.reshape(bb,(5,2))\n",
    "bb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}