Lec 43 - Groupby on Dict and Series.ipynb

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   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import pandas as pd\n",
      "from pandas import Series,DataFrame\n",
      "\n",
      "#let's learn how to use dict or series with groupby"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Let's make a Dframe\n",
      "\n",
      "animals = DataFrame(np.arange(16).reshape(4, 4),\n",
      "                   columns=['W', 'X', 'Y', 'Z'],\n",
      "                   index=['Dog', 'Cat', 'Bird', 'Mouse'])\n",
      "\n",
      "#Now lets add some NAN values\n",
      "animals.ix[1:2, ['W', 'Y']] = np.nan \n",
      "\n",
      "#Show\n",
      "animals"
     ],
     "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>W</th>\n",
        "      <th>X</th>\n",
        "      <th>Y</th>\n",
        "      <th>Z</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Dog</th>\n",
        "      <td>  0</td>\n",
        "      <td>  1</td>\n",
        "      <td>  2</td>\n",
        "      <td>  3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Cat</th>\n",
        "      <td>NaN</td>\n",
        "      <td>  5</td>\n",
        "      <td>NaN</td>\n",
        "      <td>  7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Bird</th>\n",
        "      <td>  8</td>\n",
        "      <td>  9</td>\n",
        "      <td> 10</td>\n",
        "      <td> 11</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Mouse</th>\n",
        "      <td> 12</td>\n",
        "      <td> 13</td>\n",
        "      <td> 14</td>\n",
        "      <td> 15</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "        W   X   Y   Z\n",
        "Dog     0   1   2   3\n",
        "Cat   NaN   5 NaN   7\n",
        "Bird    8   9  10  11\n",
        "Mouse  12  13  14  15"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Now let's say I had a dictionary with ebhavior values in it\n",
      "behavior_map = {'W': 'good', 'X': 'bad', 'Y': 'good','Z': 'bad'}"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Now we can groupby using that mapping\n",
      "animal_col = animals.groupby(behavior_map, axis=1)\n",
      "\n",
      "# Show the sum accroding to the groupby with the mapping\n",
      "animal_col.sum()\n",
      "\n",
      "# For example [dog][good] = [dog][Y]+[dog][W]"
     ],
     "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>bad</th>\n",
        "      <th>good</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Dog</th>\n",
        "      <td>  4</td>\n",
        "      <td>  2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Cat</th>\n",
        "      <td> 12</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Bird</th>\n",
        "      <td> 20</td>\n",
        "      <td> 18</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Mouse</th>\n",
        "      <td> 28</td>\n",
        "      <td> 26</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "       bad  good\n",
        "Dog      4     2\n",
        "Cat     12   NaN\n",
        "Bird    20    18\n",
        "Mouse   28    26"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Now let's try it with a Series\n",
      "behav_series = Series(behavior_map)\n",
      "\n",
      "#Show\n",
      "behav_series"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "W    good\n",
        "X     bad\n",
        "Y    good\n",
        "Z     bad\n",
        "dtype: object"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Now let's groupby the Series\n",
      "\n",
      "animals.groupby(behav_series, axis=1).count()"
     ],
     "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>bad</th>\n",
        "      <th>good</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Dog</th>\n",
        "      <td> 2</td>\n",
        "      <td> 2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Cat</th>\n",
        "      <td> 2</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Bird</th>\n",
        "      <td> 2</td>\n",
        "      <td> 2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Mouse</th>\n",
        "      <td> 2</td>\n",
        "      <td> 2</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "       bad  good\n",
        "Dog      2     2\n",
        "Cat      2     0\n",
        "Bird     2     2\n",
        "Mouse    2     2"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# We can also groupby with functions!\n",
      "\n",
      "#Show our dframe again\n",
      "animals"
     ],
     "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>W</th>\n",
        "      <th>X</th>\n",
        "      <th>Y</th>\n",
        "      <th>Z</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Dog</th>\n",
        "      <td>  0</td>\n",
        "      <td>  1</td>\n",
        "      <td>  2</td>\n",
        "      <td>  3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Cat</th>\n",
        "      <td>NaN</td>\n",
        "      <td>  5</td>\n",
        "      <td>NaN</td>\n",
        "      <td>  7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Bird</th>\n",
        "      <td>  8</td>\n",
        "      <td>  9</td>\n",
        "      <td> 10</td>\n",
        "      <td> 11</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Mouse</th>\n",
        "      <td> 12</td>\n",
        "      <td> 13</td>\n",
        "      <td> 14</td>\n",
        "      <td> 15</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 26,
       "text": [
        "        W   X   Y   Z\n",
        "Dog     0   1   2   3\n",
        "Cat   NaN   5 NaN   7\n",
        "Bird    8   9  10  11\n",
        "Mouse  12  13  14  15"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Lets assume we wanted to group by the length of the animal names, we can pass the len function into groupby!\n",
      "\n",
      "# Show\n",
      "animals.groupby(len).sum()\n",
      "\n",
      "#Note the index is now number of letters in the animal name"
     ],
     "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>W</th>\n",
        "      <th>X</th>\n",
        "      <th>Y</th>\n",
        "      <th>Z</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>  0</td>\n",
        "      <td>  6</td>\n",
        "      <td>  2</td>\n",
        "      <td> 10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>  8</td>\n",
        "      <td>  9</td>\n",
        "      <td> 10</td>\n",
        "      <td> 11</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td> 12</td>\n",
        "      <td> 13</td>\n",
        "      <td> 14</td>\n",
        "      <td> 15</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "    W   X   Y   Z\n",
        "3   0   6   2  10\n",
        "4   8   9  10  11\n",
        "5  12  13  14  15"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# We can also mix functions with arrays,dicts, and Series for groupby methods\n",
      "\n",
      "# Set a list for keys\n",
      "keys = ['A', 'B', 'A', 'B']\n",
      "\n",
      "# Now groupby length of name and the keys to show max values\n",
      "animals.groupby([len, keys]).max()"
     ],
     "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></th>\n",
        "      <th>W</th>\n",
        "      <th>X</th>\n",
        "      <th>Y</th>\n",
        "      <th>Z</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">3</th>\n",
        "      <th>A</th>\n",
        "      <td>  0</td>\n",
        "      <td>  1</td>\n",
        "      <td>  2</td>\n",
        "      <td>  3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>B</th>\n",
        "      <td>NaN</td>\n",
        "      <td>  5</td>\n",
        "      <td>NaN</td>\n",
        "      <td>  7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <th>A</th>\n",
        "      <td>  8</td>\n",
        "      <td>  9</td>\n",
        "      <td> 10</td>\n",
        "      <td> 11</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <th>B</th>\n",
        "      <td> 12</td>\n",
        "      <td> 13</td>\n",
        "      <td> 14</td>\n",
        "      <td> 15</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 29,
       "text": [
        "      W   X   Y   Z\n",
        "3 A   0   1   2   3\n",
        "  B NaN   5 NaN   7\n",
        "4 A   8   9  10  11\n",
        "5 B  12  13  14  15"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# We can also use groupby with hierarchaly index levels\n",
      "\n",
      "#Create a hierarchal column index\n",
      "hier_col = pd.MultiIndex.from_arrays([['NY','NY','NY','SF','SF'],[1,2,3,1,2]],names=['City','sub_value'])\n",
      "\n",
      "# Create a dframe with hierarchal index\n",
      "dframe_hr = DataFrame(np.arange(25).reshape(5,5),columns=hier_col)\n",
      "\n",
      "#Multiply values by 100 for clarity\n",
      "dframe_hr = dframe_hr*100\n",
      "\n",
      "#Show\n",
      "dframe_hr"
     ],
     "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>\n",
        "      <th>City</th>\n",
        "      <th colspan=\"3\" halign=\"left\">NY</th>\n",
        "      <th colspan=\"2\" halign=\"left\">SF</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>sub_value</th>\n",
        "      <th>1</th>\n",
        "      <th>2</th>\n",
        "      <th>3</th>\n",
        "      <th>1</th>\n",
        "      <th>2</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>    0</td>\n",
        "      <td>  100</td>\n",
        "      <td>  200</td>\n",
        "      <td>  300</td>\n",
        "      <td>  400</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>  500</td>\n",
        "      <td>  600</td>\n",
        "      <td>  700</td>\n",
        "      <td>  800</td>\n",
        "      <td>  900</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 1000</td>\n",
        "      <td> 1100</td>\n",
        "      <td> 1200</td>\n",
        "      <td> 1300</td>\n",
        "      <td> 1400</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 1500</td>\n",
        "      <td> 1600</td>\n",
        "      <td> 1700</td>\n",
        "      <td> 1800</td>\n",
        "      <td> 1900</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 2000</td>\n",
        "      <td> 2100</td>\n",
        "      <td> 2200</td>\n",
        "      <td> 2300</td>\n",
        "      <td> 2400</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 36,
       "text": [
        "City         NY                SF      \n",
        "sub_value     1     2     3     1     2\n",
        "0             0   100   200   300   400\n",
        "1           500   600   700   800   900\n",
        "2          1000  1100  1200  1300  1400\n",
        "3          1500  1600  1700  1800  1900\n",
        "4          2000  2100  2200  2300  2400"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Up next: Data Aggregation!!"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}