groupby_pivot.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using Group By and Pivot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Group By\n",
    "Can be used to create a plot for time series data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>M</th>\n",
       "      <th>P</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>jan</td>\n",
       "      <td>eva</td>\n",
       "      <td>78</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>feb</td>\n",
       "      <td>eva</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mar</td>\n",
       "      <td>eva</td>\n",
       "      <td>38</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>jan</td>\n",
       "      <td>ed</td>\n",
       "      <td>50</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>feb</td>\n",
       "      <td>ed</td>\n",
       "      <td>49</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>mar</td>\n",
       "      <td>ed</td>\n",
       "      <td>28</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>jan</td>\n",
       "      <td>mike</td>\n",
       "      <td>92</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>feb</td>\n",
       "      <td>mike</td>\n",
       "      <td>73</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     M     P   C   D\n",
       "0  jan   eva  78  50\n",
       "1  feb   eva   2   5\n",
       "2  mar   eva  38  80\n",
       "3  jan    ed  50  55\n",
       "4  feb    ed  49  73\n",
       "5  mar    ed  28  29\n",
       "6  jan  mike  92  80\n",
       "7  feb  mike  73  98"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a DataFrame containing monthly Data for three Persons, where data is incomplete for one person \n",
    "from pandas import DataFrame\n",
    "\n",
    "df: DataFrame = pd.DataFrame({'M': ['jan', 'feb', 'mar', 'jan', 'feb', 'mar', 'jan', 'feb', ],\n",
    "                              'P': ['eva'] * 3 + ['ed'] * 3 + ['mike'] * 2,\n",
    "                              'C': np.random.randint(0, 101, 8),\n",
    "                              'D': np.random.randint(0, 101, 8)})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8 entries, 0 to 7\n",
      "Data columns (total 4 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   M       8 non-null      object\n",
      " 1   P       8 non-null      object\n",
      " 2   C       8 non-null      int64 \n",
      " 3   D       8 non-null      int64 \n",
      "dtypes: int64(2), object(2)\n",
      "memory usage: 384.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "# get info about the dataframe\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>51.250000</td>\n",
       "      <td>58.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>29.270902</td>\n",
       "      <td>30.499415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>35.500000</td>\n",
       "      <td>44.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>49.500000</td>\n",
       "      <td>64.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>74.250000</td>\n",
       "      <td>80.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>92.000000</td>\n",
       "      <td>98.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               C          D\n",
       "count   8.000000   8.000000\n",
       "mean   51.250000  58.750000\n",
       "std    29.270902  30.499415\n",
       "min     2.000000   5.000000\n",
       "25%    35.500000  44.750000\n",
       "50%    49.500000  64.000000\n",
       "75%    74.250000  80.000000\n",
       "max    92.000000  98.000000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get statistics about number columns\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>P</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>jan</th>\n",
       "      <td>eva</td>\n",
       "      <td>78</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feb</th>\n",
       "      <td>eva</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mar</th>\n",
       "      <td>eva</td>\n",
       "      <td>38</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jan</th>\n",
       "      <td>ed</td>\n",
       "      <td>50</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feb</th>\n",
       "      <td>ed</td>\n",
       "      <td>49</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       P   C   D\n",
       "M               \n",
       "jan  eva  78  50\n",
       "feb  eva   2   5\n",
       "mar  eva  38  80\n",
       "jan   ed  50  55\n",
       "feb   ed  49  73"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a df indexed by Month - this will be the x-axis in a plot\n",
    "dfm = df.set_index('M')\n",
    "dfm[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['jan', 'feb', 'mar', 'jan', 'feb', 'mar', 'jan', 'feb'], dtype='object', name='M')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show info on index\n",
    "dfm.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "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": [
    "### Plot trend for all P with one Metric C\n",
    "\n",
    "# groupby yields a DataSeries, plotted as f(y=C,x=M) for all P, with plot arguments:\n",
    "#  xticks provides labels for xaxis, todo: why is the 'mar' tick missing -incomplete data for mike?\n",
    "gc = dfm.groupby('P', sort=False)['C']\n",
    "gc.plot(title='Trend for C', legend=True, x='M', xticks=range(len(gc)));  # subplots=False, use_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'eva': Index(['jan', 'feb', 'mar'], dtype='object', name='M'),\n",
       " 'ed': Index(['jan', 'feb', 'mar'], dtype='object', name='M'),\n",
       " 'mike': Index(['jan', 'feb'], dtype='object', name='M')}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Grouping info is held in a dict exposed via .groups\n",
    "gc.groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "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": [
    "### Plot trend for one P with all Metrics\n",
    "\n",
    "# The index returned by groupby can be used with the iloc function:\n",
    "dfm.iloc[gc.indices['ed']].plot(kind='bar', title='ed',xticks=range(len(gc)));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pivot Table\n",
    "Although the former plot can be achieved by groupby there is the more specialized pivot_table which allows for reshaping data and easy plotting "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "p1=df.pivot_table(index='M',columns=['P'],values='C')\n",
    "p1.plot(kind='bar',xticks=range(len(p1)));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More complex Dataframes can be reshaped and plotted:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>P</th>\n",
       "      <th>N</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>feb</th>\n",
       "      <td>mike</td>\n",
       "      <td>lunch</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mar</th>\n",
       "      <td>mike</td>\n",
       "      <td>lunch</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jan</th>\n",
       "      <td>eva</td>\n",
       "      <td>dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feb</th>\n",
       "      <td>eva</td>\n",
       "      <td>dinner</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mar</th>\n",
       "      <td>eva</td>\n",
       "      <td>dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        P       N   D\n",
       "M                    \n",
       "feb  mike   lunch   6\n",
       "mar  mike   lunch  10\n",
       "jan   eva  dinner   3\n",
       "feb   eva  dinner   5\n",
       "mar   eva  dinner   3"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a Dataframe containing monthly Data for three Persons with metricName N and metricData D\n",
    "months = ['jan', 'feb', 'mar']\n",
    "persons = ['eva'] * 3 + ['ed'] * 3 + ['mike'] * 3\n",
    "df2 = pd.DataFrame({'M': months * 3 * 2,\n",
    "                    'P': persons * 2,\n",
    "                    'N': ['lunch'] * 3 * 3 + ['dinner'] * 3 * 3,\n",
    "                    'D': np.random.randint(1, 11, 9 * 2)})\n",
    "df2 = df2.set_index('M')\n",
    "df2[7:12]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reshape the Dataframe into 2-dim columns (Person, metricName) with values=metricData and Months as index\n",
    "p2 = df2.pivot_table(index='M', columns=['P', 'N'], values='D')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "p2.plot(kind='bar', legend=True, xticks=range(len(p2)));"
   ]
  }
 ],
 "metadata": {
  "jupytext": {
   "formats": "ipynb,py:light"
  },
  "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",
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