hunterowens · February 10, 2015 21:29
diff --git a/pandas.ipynb b/pandas.ipynb
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  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Boilerplate\n",
      "* `import pandas as pd` is a convention.\n",
      "* matplotlib inside the ipython notebook\n",
      "* make it look less terrible. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "import numpy as np\n",
      "\n",
      "%matplotlib inline\n",
      "pd.set_option('display.mpl_style', 'default')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Series\n",
      "Two Parts: *Index* and *Value*"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd.Series(np.random.rand(10)).hist(bins=2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 84,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x10bd49390>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x10ba833d0>"
       ]
      }
     ],
     "prompt_number": 84
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "*NB* The index can be either just sequence of integers or something semantic. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s = pd.Series(np.random.rand(1000))\n",
      "s[:100].hist(bins=10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 86,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x10bf8df10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x10be78b10>"
       ]
      }
     ],
     "prompt_number": 86
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## TimeSeries\n",
      "Sometimes you want the index to be something that is not just 0,1,2,...\n",
      "\n",
      "For example, index might want to be times. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rng = pd.date_range('2/10/2015', periods=168, freq='H')\n",
      "ts = pd.Series(np.random.randn(len(rng)), index=rng)\n",
      "ts.plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 89,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x10c227450>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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wU4nIOKPNWbQlaaKiNY4Fdv7ezZVHRha0uwH2j9kx0w61VtF/Pm4biRVFP0Gb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\nkEwUUTONyHHFJYm8IMeT55nPh7LQIBAT23lTmiyJacuebp4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snAc4mFwjrB/UuMoL7pHC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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10aa1e990>"
       ]
      }
     ],
     "prompt_number": 89
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## DataFrames\n",
      "A datatype that is representative of a set of tabular data. Similar to R, STATA and Matlab types. Inspired directly from R. \n",
      "\n",
      "Has an Index, Headers, Rows, and Values. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "reg = {'name': 'regenstein',\n",
      "       'books': 200,\n",
      "       'rooms': 25}\n",
      "crerar = {'name': 'crerar',\n",
      "          'books': 250,\n",
      "          'rooms': 4}\n",
      "libraries = [reg, crerar]\n",
      "lib_short_df = pd.DataFrame(libraries)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 91
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lib_short_df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>books</th>\n",
        "      <th>name</th>\n",
        "      <th>rooms</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 200</td>\n",
        "      <td> regenstein</td>\n",
        "      <td> 25</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 250</td>\n",
        "      <td>     crerar</td>\n",
        "      <td>  4</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 105,
       "text": [
        "   books        name  rooms\n",
        "0    200  regenstein     25\n",
        "1    250      crerar      4"
       ]
      }
     ],
     "prompt_number": 105
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##I/O\n",
      "(input/output). Using Pandas to get data from elsewhere into a form that you can use. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "url = 'http://cfss.uchicago.edu/data/libraries.csv'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 97
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lib_df = pd.read_csv(url)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 98
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lib_df[' numBooks'].plot(kind='bar', x)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 104,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x10c7b7550>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x10c7bfa10>"
       ]
      }
     ],
     "prompt_number": 104
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lib_df.describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th> capacity</th>\n",
        "      <th> numBooks</th>\n",
        "      <th> floors</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>count</th>\n",
        "      <td>    5.000000</td>\n",
        "      <td>       5.000000</td>\n",
        "      <td> 5.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td>  650.000000</td>\n",
        "      <td>  344520.400000</td>\n",
        "      <td> 3.600000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td>  511.126208</td>\n",
        "      <td>  542448.921373</td>\n",
        "      <td> 2.302173</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td>  100.000000</td>\n",
        "      <td>   15032.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td>  150.000000</td>\n",
        "      <td>   75322.000000</td>\n",
        "      <td> 2.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td>  750.000000</td>\n",
        "      <td>  102496.000000</td>\n",
        "      <td> 4.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td> 1000.000000</td>\n",
        "      <td>  224506.000000</td>\n",
        "      <td> 4.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
        "      <td> 1250.000000</td>\n",
        "      <td> 1305246.000000</td>\n",
        "      <td> 7.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 108,
       "text": [
        "          capacity        numBooks    floors\n",
        "count     5.000000        5.000000  5.000000\n",
        "mean    650.000000   344520.400000  3.600000\n",
        "std     511.126208   542448.921373  2.302173\n",
        "min     100.000000    15032.000000  1.000000\n",
        "25%     150.000000    75322.000000  2.000000\n",
        "50%     750.000000   102496.000000  4.000000\n",
        "75%    1000.000000   224506.000000  4.000000\n",
        "max    1250.000000  1305246.000000  7.000000"
       ]
      }
     ],
     "prompt_number": 108
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
 }