Pandas data importing test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# syntax: from (library) import (specific library function)\n",
    "from pandas import DataFrame, read_csv\n",
    "\n",
    "# syntax: import (library) as (give the library a nickname/alias)\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd \n",
    "\n",
    "# Enable inline plotting\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Flavor</th>\n",
       "      <th>Scoops</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Vanilla</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Strawberry</td>\n",
       "      <td>155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chocolate</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Oreo</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Coffee</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Flavor  Scoops\n",
       "0     Vanilla       9\n",
       "1  Strawberry     155\n",
       "2   Chocolate      77\n",
       "3        Oreo      58\n",
       "4      Coffee      73"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flavors = ['Vanilla','Strawberry','Chocolate','Oreo','Coffee']\n",
    "scoops = [9, 155, 77, 58, 73]\n",
    "IceCreamDataSet = list(zip(flavors,scoops))\n",
    "\n",
    "myDF = pd.DataFrame(data = IceCreamDataSet, columns=['Flavor', 'Scoops'])\n",
    "myDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Flavor</th>\n",
       "      <th>Scoops</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Vanilla</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Strawberry</td>\n",
       "      <td>155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chocolate</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Oreo</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Coffee</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Flavor  Scoops\n",
       "0     Vanilla       9\n",
       "1  Strawberry     155\n",
       "2   Chocolate      77\n",
       "3        Oreo      58\n",
       "4      Coffee      73"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myDF.to_csv('/Users/Annamira/Documents/Jupyter/pandasTest1.csv', index=False)\n",
    "Location = r'/Users/Annamira/Documents/Jupyter/pandasTest1.csv' \n",
    "readDF = pd.read_csv(Location)\n",
    "readDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x118819940>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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eaOMEvqS+qZndJVWXin1eYSW1Da23EUmLi3D16uOYMiiW5D7hn/tv785UTyv+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\nRvCenOmq2qmbonnEXT2Bg6q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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1189d7c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "readDF.plot()\n",
    "# readDF.plot.scatter(x='Flavor' , y='Scoops')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}