6.Groupby-and-aggregate.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Groupby and Aggregation (Split-Apply-Combine):\n",
    "\n",
    "This notebook will provide a walkthrough for data **splitting** (mapping) with ```groupby()```, **apply** some action (e.g.,```count(), sum(), mean(), std()```) and finally **combine** through ```aggregation(), transform()``` action (reduction). \n",
    "\n",
    "Read more about these functionality from [Pydata documentation for Group by (split-apply-combine)](https://pandas.pydata.org/docs/user_guide/groupby.html)[1].\n",
    "Some parts of this notebook are taken from [EuroScipy 2016 Pandas Tutorial by Joris Van den Bossche and Nicholas Devenish](https://github.com/jorisvandenbossche/pandas-tutorial)[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Braund, Mr. Owen Harris</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cumings, Mrs. John Bradley (Florence Briggs Thayer)</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    PassengerId  Survived  \\\n",
       "Name                                                                        \n",
       "Braund, Mr. Owen Harris                                       1         0   \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...            2         1   \n",
       "\n",
       "                                                    Pclass     Sex   Age  \\\n",
       "Name                                                                       \n",
       "Braund, Mr. Owen Harris                                  3    male  22.0   \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...       1  female  38.0   \n",
       "\n",
       "                                                    SibSp  Parch     Ticket  \\\n",
       "Name                                                                          \n",
       "Braund, Mr. Owen Harris                                 1      0  A/5 21171   \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...      1      0   PC 17599   \n",
       "\n",
       "                                                       Fare Cabin Embarked  \n",
       "Name                                                                        \n",
       "Braund, Mr. Owen Harris                              7.2500   NaN        S  \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...  71.2833   C85        C  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic = pd.read_csv('data/titanic.csv')\n",
    "titanic = titanic.set_index('Name')\n",
    "titanic.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The groupby operation (split-apply-combine)\n",
    "is followed by multiple functionality e.g., ```groupby.aggregate()```,```groupby.count()``` ```groupby.size()```,```groupby.mean()```. \n",
    "\n",
    "The \"group by\" concept: we want to **apply the same function on subsets of your dataframe, based on some key to split the dataframe in subsets**\n",
    "\n",
    "This operation is also referred to as the \"split-apply-combine\" operation, involving the following steps:\n",
    "\n",
    "* **Splitting** the data into groups based on some criteria\n",
    "* **Applying** a function to each group independently\n",
    "* **Combining** the results into a data structure\n",
    "\n",
    "<img src=\"plots/splitApplyCombine.png\">\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Simple Groupby and aggregate example:\n",
    "Lets create a sample dataframe to operate ```groupby()``` followed by ```size()``` and ```aggregate()``` with ```np.sum()``` seperately."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>data</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key  data\n",
       "0   A     0\n",
       "1   B     5"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'key':['A','B','C','A','B','C','A','B','C'],\n",
    "                   'data': [0, 5, 10, 5, 10, 15, 10, 15, 20]})\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ```groupby()``` operation followed by ```size()``` does not return a ```DataFrame``` object, it becomes a pandas ```Series``` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Object type: <class 'pandas.core.series.Series'>\n",
      "key\n",
      "A    3\n",
      "B    3\n",
      "C    3\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "result = df.groupby('key').size()\n",
    "print(\"Object type:\", type(result))\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ```groupby()``` operation followed by ```aggregate()``` returns a ```DataFrame``` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "object type: <class 'pandas.core.frame.DataFrame'>\n"
     ]
    },
    {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     data\n",
       "key      \n",
       "A      15\n",
       "B      30\n",
       "C      45"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = df.groupby('key').aggregate(np.sum)\n",
    "print(\"object type:\", type(result))\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Exercise with Titanic Dataset\n",
    "\n",
    "<div class=\"alert alert-success\">\n",
    "    <b>EXERCISE</b>: Using groupby(), calculate the total number for each sex catagory.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex\n",
       "female    314\n",
       "male      577\n",
       "dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.groupby('Sex').size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "    <b>EXERCISE</b>: Using groupby(), calculate the average age for each sex.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex\n",
       "female    27.915709\n",
       "male      30.726645\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.groupby('Sex')['Age'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "    <b>EXERCISE</b>: Calculate the average survival ratio for all passengers.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3838383838383838"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic['Survived'].sum() / len(titanic['Survived'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "    <b>EXERCISE</b>: Calculate this survival ratio for all passengers younger that 25 (remember: filtering/boolean indexing).\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4119601328903654"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df25 = titanic[titanic_df1['Age'] <= 25]\n",
    "df25['Survived'].sum() / len(df25['Survived'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "    <b>EXERCISE</b>: Is there a difference in this survival ratio between the sexes? (tip: write the above calculation of the survival ratio as a function)\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def survival_ratio(survived):\n",
    "    return survived.sum() / len(survived)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex\n",
       "female    0.742038\n",
       "male      0.188908\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.groupby('Sex')['Survived'].aggregate(survival_ratio)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "    <b>EXERCISE</b>: Make a bar plot of the survival ratio for the different classes ('Pclass' column).\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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": [
    "titanic.groupby('Pclass')['Survived'].aggregate(survival_ratio).plot(kind='bar')\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. Some advanced groupby operations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "    <b>EXERCISE</b>: Find data for age distribution.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = titanic.copy(deep =True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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": [
    "df.groupby(df.Age//10 * 10).size().plot(kind='bar',figsize = [6,4])\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "    <b>EXERCISE</b>: Find data for male age distribution.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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": [
    "Male = df[df['Sex'] == 'male']\n",
    "Male.groupby(Male.Age // 10 * 10).size().plot(kind='bar',figsize = [6,4])\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "    <b>EXERCISE</b>: List data with Fare size greater then 50.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex\n",
       "female    87\n",
       "male      73\n",
       "dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Fare50 = df[df.Fare>50]\n",
    "Fare50.groupby([ 'Sex']).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age    Sex     Survived\n",
       "0.92   male    1           1\n",
       "2.00   female  0           1\n",
       "4.00   male    1           1\n",
       "11.00  male    1           1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Fare50.groupby(['Age', 'Sex','Survived']).size().head(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4. Groupby followed by transformation: ```groupby.transform()```. \n",
    "The transform operation accepts builting functions e.g., ```sum,  mean, std``` through keyword. One can define a new function called user defined function to supply inside ```transform(new_function)```."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>key</th>\n",
       "      <th>data</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key  data\n",
       "0   A     0\n",
       "1   B     5"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'key':['A','B','C','A','B','C','A','B','C'],\n",
    "                   'data': [0, 5, 10, 5, 10, 15, 10, 15, 20]})\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>data</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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   ]
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   "metadata": {},
   "outputs": [],
   "source": [
    "def normalize(group):\n",
    "    return (group - group.mean()) / group.std()"
   ]
  },
  {
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   "execution_count": 39,
   "metadata": {},
   "outputs": [
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   "source": [
    "### References:\n",
    "1. [Pydata documentation: Group by: split-apply-combine](https://pandas.pydata.org/docs/user_guide/groupby.html)\n",
    "2. [EuroScipy 2016 Pandas Tutorial by Joris Van den Bossche and Nicholas Devenish](https://github.com/jorisvandenbossche/pandas-tutorial)"
   ]
  }
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