Plotting A CSV File

CSV_PLot.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Here I have a load a CSV file and By the Use of Pandas I have Plot it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              Name  Class            Dorm  Room   GPA\n",
      "0  Sally Whittaker   2018  McCarren House   312  3.75\n",
      "1  Belinda Jameson   2017   Cushing House   148  2.52\n",
      "2       Jeff Smith   2018  Prescott House  17-D  1.20\n",
      "3      Sandy Allen   2019    Oliver House   108  3.48\n",
      "4  Sally Whittaker   2018  McCarren House   312  4.50\n",
      "5  Belinda Jameson   2017   Cushing House   148  3.52\n",
      "6       Jeff Smith   2018  Prescott House  17-D  2.20\n",
      "7      Sandy Allen   2019    Oliver House   108  1.95\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.head of               Name  Class            Dorm  Room   GPA\n",
       "0  Sally Whittaker   2018  McCarren House   312  3.75\n",
       "1  Belinda Jameson   2017   Cushing House   148  2.52\n",
       "2       Jeff Smith   2018  Prescott House  17-D  1.20\n",
       "3      Sandy Allen   2019    Oliver House   108  3.48\n",
       "4  Sally Whittaker   2018  McCarren House   312  4.50\n",
       "5  Belinda Jameson   2017   Cushing House   148  3.52\n",
       "6       Jeff Smith   2018  Prescott House  17-D  2.20\n",
       "7      Sandy Allen   2019    Oliver House   108  1.95>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('C:\\Users\\pabla\\OneDrive\\Desktop\\Plot\\sample.csv')\n",
    "#print df\n",
    "                     \n",
    "                     \n",
    "filename = 'C:\\Users\\pabla\\OneDrive\\Desktop\\Plot\\Sample.h5'\n",
    "\n",
    "print(pd.read_hdf(filename, 'data'))\n",
    "\n",
    "df1= pd.read_csv('C:\\Users\\pabla\\OneDrive\\Desktop\\Plot\\sample.csv')\n",
    "#sa1= pd.read_csv('C:\\Users\\pabla\\OneDrive\\Desktop\\Plot\\cc.csv',index_col=1)\n",
    "df1.head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xb465240>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#df1[2:7]\n",
    "df1.groupby(\"Class\")['GPA'].mean().plot(kind='bar')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}