3_d_group_by_and_interate.ipynb

{
 "cells": [
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "perform group by on dataframe and iterate on the grouped result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "classes = [\"class 1\"] * 5 + [\"class 2\"] * 5\n",
    "sub_class = ['c1','c2','c2','c1','c3'] + ['c1','c2','c3','c2','c3']\n",
    "vals = [1,3,5,1,3] + [2,6,7,5,2]\n",
    "p_df = pd.DataFrame({\"class\": classes, \"sub_class\": sub_class, \"vals\": vals})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>class</th>\n",
       "      <th>sub_class</th>\n",
       "      <th>vals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>class 1</td>\n",
       "      <td>c1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>class 1</td>\n",
       "      <td>c2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>class 1</td>\n",
       "      <td>c2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>class 1</td>\n",
       "      <td>c1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>class 1</td>\n",
       "      <td>c3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>class 2</td>\n",
       "      <td>c1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>class 2</td>\n",
       "      <td>c2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>class 2</td>\n",
       "      <td>c3</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>class 2</td>\n",
       "      <td>c2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>class 2</td>\n",
       "      <td>c3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     class sub_class  vals\n",
       "0  class 1        c1     1\n",
       "1  class 1        c2     3\n",
       "2  class 1        c2     5\n",
       "3  class 1        c1     1\n",
       "4  class 1        c3     3\n",
       "5  class 2        c1     2\n",
       "6  class 2        c2     6\n",
       "7  class 2        c3     7\n",
       "8  class 2        c2     5\n",
       "9  class 2        c3     2"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "grouped = p_df.groupby(['class', 'sub_class'])['vals'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "class    sub_class\n",
       "class 1  c1           1.0\n",
       "         c2           4.0\n",
       "         c3           3.0\n",
       "class 2  c1           2.0\n",
       "         c2           5.5\n",
       "         c3           4.5\n",
       "Name: vals, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "class 1 : c1 : 1.0\n",
      "class 1 : c2 : 4.0\n",
      "class 1 : c3 : 3.0\n",
      "class 2 : c1 : 2.0\n",
      "class 2 : c2 : 5.5\n",
      "class 2 : c3 : 4.5\n"
     ]
    }
   ],
   "source": [
    "for index_val, value in grouped.iteritems():\n",
    "    class_name, sub_class_name = index_val\n",
    "    print(class_name, \":\", sub_class_name, \":\", value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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