Combining and Aggregating.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `Pandas.concat()`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `concat` function of `pandas` allows us to combine data by stacking one data on top of the other (by default)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When the table are of same shape and size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1 = {'col1': [1,2,3,4,5,6],\n",
    "        'col2': ['a','b','c','d','e','f']\n",
    "        }\n",
    "\n",
    "data2 = {'col1': [11,22,33,44,55],\n",
    "        'col2': ['aa','bb','cc','dd','ee']}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.DataFrame(data1)\n",
    "df2 = pd.DataFrame(data2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
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       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>d</td>\n",
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       "      <th>4</th>\n",
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       "      <th>5</th>\n",
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       "      <td>f</td>\n",
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       "  </tbody>\n",
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      "text/plain": [
       "   col1 col2\n",
       "0     1    a\n",
       "1     2    b\n",
       "2     3    c\n",
       "3     4    d\n",
       "4     5    e\n",
       "5     6    f"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
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       "<div>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11</td>\n",
       "      <td>aa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22</td>\n",
       "      <td>bb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33</td>\n",
       "      <td>cc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>44</td>\n",
       "      <td>dd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>55</td>\n",
       "      <td>ee</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   col1 col2\n",
       "0    11   aa\n",
       "1    22   bb\n",
       "2    33   cc\n",
       "3    44   dd\n",
       "4    55   ee"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
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       "      <th>col1</th>\n",
       "      <th>col2</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>a</td>\n",
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       "      <th>1</th>\n",
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       "      <td>4</td>\n",
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       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>e</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>f</td>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11</td>\n",
       "      <td>aa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22</td>\n",
       "      <td>bb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33</td>\n",
       "      <td>cc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>44</td>\n",
       "      <td>dd</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>55</td>\n",
       "      <td>ee</td>\n",
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       "  </tbody>\n",
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      "text/plain": [
       "   col1 col2\n",
       "0     1    a\n",
       "1     2    b\n",
       "2     3    c\n",
       "3     4    d\n",
       "4     5    e\n",
       "5     6    f\n",
       "0    11   aa\n",
       "1    22   bb\n",
       "2    33   cc\n",
       "3    44   dd\n",
       "4    55   ee"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#combining the two tables\n",
    "df3 = pd.concat([df1,df2])\n",
    "df3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When the tables are of different shape and size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col1</th>\n",
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       "      <th>col3</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>a</td>\n",
       "      <td>apple</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>b</td>\n",
       "      <td>ball</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>c</td>\n",
       "      <td>cat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>d</td>\n",
       "      <td>dog</td>\n",
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       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>e</td>\n",
       "      <td>egg</td>\n",
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       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>f</td>\n",
       "      <td>fish</td>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11</td>\n",
       "      <td>aa</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22</td>\n",
       "      <td>bb</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2</th>\n",
       "      <td>33</td>\n",
       "      <td>cc</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>3</th>\n",
       "      <td>44</td>\n",
       "      <td>dd</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>55</td>\n",
       "      <td>ee</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "   col1 col2   col3\n",
       "0     1    a  apple\n",
       "1     2    b   ball\n",
       "2     3    c    cat\n",
       "3     4    d    dog\n",
       "4     5    e    egg\n",
       "5     6    f   fish\n",
       "0    11   aa    NaN\n",
       "1    22   bb    NaN\n",
       "2    33   cc    NaN\n",
       "3    44   dd    NaN\n",
       "4    55   ee    NaN"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1 = {'col1': [1,2,3,4,5,6],\n",
    "        'col2': ['a','b','c','d','e','f'],\n",
    "        'col3': ['apple','ball','cat','dog','egg','fish']}\n",
    "\n",
    "data2 = {'col1': [11,22,33,44,55],\n",
    "        'col2': ['aa','bb','cc','dd','ee']}\n",
    "\n",
    "#defining the `DataFrame`\n",
    "df1 = pd.DataFrame(data1)\n",
    "df2 = pd.DataFrame(data2)\n",
    "\n",
    "#combining the tables\n",
    "df3 = pd.concat([df1,df2])\n",
    "df3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the empty cells are filled with NaN values and python does not show any errors."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `Pandas.merge()`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Defining two `DataFrame` tables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1 = {'col1': [1,2,3,4,5,6],\n",
    "        'col2': ['a','b','c','d','e','f'],\n",
    "        'col3': ['apple','ball','cat','dog','egg','fish']}\n",
    "\n",
    "data2 = {'col1': [11,22,33,44,55],\n",
    "        'col2': ['a','b','cc','dd','e']}\n",
    "\n",
    "df1 = pd.DataFrame(data1)\n",
    "df2 = pd.DataFrame(data2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Merging the two tables of above. By default, the join is `inner` i.e. only rows that have common values in the merging column are displayed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>a</td>\n",
       "      <td>apple</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>b</td>\n",
       "      <td>ball</td>\n",
       "      <td>22</td>\n",
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       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>e</td>\n",
       "      <td>egg</td>\n",
       "      <td>55</td>\n",
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      "text/plain": [
       "   col1_x col2   col3  col1_y\n",
       "0       1    a  apple      11\n",
       "1       2    b   ball      22\n",
       "2       5    e    egg      55"
      ]
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     "execution_count": 40,
     "metadata": {},
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   "source": [
    "df3 = pd.merge(left=df1, right=df2, on='col2')\n",
    "df3"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The join type can be specified while merging the tables, the available joins are:\n",
    "* __inner__: only includes elements that appear in both dataframes with a common key\n",
    "* __outer__: includes all data from both dataframes\n",
    "* __right__: includes all of the rows from the \"right\" dataframe along with any rows from the \"left\" dataframe with a common key; the result retains all columns from both of the original dataframes\n",
    "* __left__: includes all of the rows from the \"left\" dataframe along with any rows from the \"right\" dataframe with a common key; the result retains all columns from both of the original dataframes"
   ]
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  {
   "cell_type": "code",
   "execution_count": 42,
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       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>a</td>\n",
       "      <td>apple</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>b</td>\n",
       "      <td>ball</td>\n",
       "      <td>22.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>c</td>\n",
       "      <td>cat</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>d</td>\n",
       "      <td>dog</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>e</td>\n",
       "      <td>egg</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6.0</td>\n",
       "      <td>f</td>\n",
       "      <td>fish</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>cc</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>dd</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   col1_x col2   col3  col1_y\n",
       "0     1.0    a  apple    11.0\n",
       "1     2.0    b   ball    22.0\n",
       "2     3.0    c    cat     NaN\n",
       "3     4.0    d    dog     NaN\n",
       "4     5.0    e    egg    55.0\n",
       "5     6.0    f   fish     NaN\n",
       "6     NaN   cc    NaN    33.0\n",
       "7     NaN   dd    NaN    44.0"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = pd.merge(left=df1, right=df2, on='col2', how='outer')\n",
    "df3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In case of `outer` join, the missing values are filled with `NaN` like in `pd.concat()`. \n",
    "\n",
    "_This also suggests that by default, the `concat` function uses `outer` join._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `Pandas.DataFrame.GroupBy()`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The groupby function performs in three steps: split, apply and combine. First it groups and splits data based on defined column/information provided and then apply the aggregating task and finally combined the data to give the output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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",
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       "    }\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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>C</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key  data\n",
       "0   A     0\n",
       "1   B     1\n",
       "2   C     2\n",
       "3   A     3\n",
       "4   B     4\n",
       "5   C     5"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'key': ['A', 'B', 'C', 'A', 'B', 'C'],\n",
    "                   'data': range(6)}, columns=['key', 'data'])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We `GroupBy` the above table on the `key` column which groups the table based on the values on the `key` column.\n",
    "`GroupBy` creates a `DataFrameGroupBy` object as can be seen below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x00000113DEA59E80>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('key')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When supplying an aggregating function along with the `GroupBy` method will apply the provided function in all the values of the remaining column."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we apply the `sum()` function and we see that the values in `data` column in summed up based on the values of `key` column i.e. every values in `data` is added for every `A` value of the `key` column and so forth."
   ]
  },
  {
   "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>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>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     data\n",
       "key      \n",
       "A       3\n",
       "B       5\n",
       "C       7"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('key').sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Multiple aggregating functions can also be applied in a single go by providing a list of aggregating function within the `agg` method. _Notice how the function is not followed by parenthesis. We don't use the parenthesis when supplying the function in the `agg` method._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">data</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>3</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>7</td>\n",
       "      <td>3.5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    data         \n",
       "     sum mean max\n",
       "key              \n",
       "A      3  1.5   3\n",
       "B      5  2.5   4\n",
       "C      7  3.5   5"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "df.groupby('key').agg([sum, np.mean, max])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `Pandas.cut`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `cut()` method is used to group data into bins that means it heps to convert individual data into categorical data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating a sample dataframe\n",
    "df = pd.DataFrame({'name': ['A', 'B', 'C', 'D', 'E'],\n",
    "                  'val1': [10,20,30,40,50],\n",
    "                  'val2': [40, 60, 75, 24, 13]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>name</th>\n",
       "      <th>val1</th>\n",
       "      <th>val2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>10</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>20</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>30</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D</td>\n",
       "      <td>40</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>E</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  name  val1  val2\n",
       "0    A    10    40\n",
       "1    B    20    60\n",
       "2    C    30    75\n",
       "3    D    40    24\n",
       "4    E    50    13"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#defining the bins to categorize the values in the above table\n",
    "bins = [0, 20, 40, 60, 80, 100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>name</th>\n",
       "      <th>val1</th>\n",
       "      <th>val2</th>\n",
       "      <th>group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>10</td>\n",
       "      <td>40</td>\n",
       "      <td>(20, 40]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>20</td>\n",
       "      <td>60</td>\n",
       "      <td>(40, 60]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>30</td>\n",
       "      <td>75</td>\n",
       "      <td>(60, 80]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D</td>\n",
       "      <td>40</td>\n",
       "      <td>24</td>\n",
       "      <td>(20, 40]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>E</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>(0, 20]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  name  val1  val2     group\n",
       "0    A    10    40  (20, 40]\n",
       "1    B    20    60  (40, 60]\n",
       "2    C    30    75  (60, 80]\n",
       "3    D    40    24  (20, 40]\n",
       "4    E    50    13   (0, 20]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#grouping the data of val2 column into categories defined above\n",
    "df['group'] = pd.cut(df['val2'], bins=bins)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice how the `group` column has values starting with round parenthesis and ending with a square parenthesis. This signifies that the starting value is not included in the group while the end value is included i.e. for (20, 40] number 20 will not be included in this group but 40 will"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also supply name to group name for each category, this is done by providing the `label` parameter in the `cut` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>name</th>\n",
       "      <th>val1</th>\n",
       "      <th>val2</th>\n",
       "      <th>group</th>\n",
       "      <th>group_named</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>10</td>\n",
       "      <td>40</td>\n",
       "      <td>(20, 40]</td>\n",
       "      <td>poor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>20</td>\n",
       "      <td>60</td>\n",
       "      <td>(40, 60]</td>\n",
       "      <td>average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>30</td>\n",
       "      <td>75</td>\n",
       "      <td>(60, 80]</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D</td>\n",
       "      <td>40</td>\n",
       "      <td>24</td>\n",
       "      <td>(20, 40]</td>\n",
       "      <td>poor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>E</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>(0, 20]</td>\n",
       "      <td>very poor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  name  val1  val2     group group_named\n",
       "0    A    10    40  (20, 40]        poor\n",
       "1    B    20    60  (40, 60]     average\n",
       "2    C    30    75  (60, 80]        good\n",
       "3    D    40    24  (20, 40]        poor\n",
       "4    E    50    13   (0, 20]   very poor"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#defining the labels to be included for the groups\n",
    "label = ['very poor', 'poor', 'average', 'good', 'very good']\n",
    "\n",
    "df['group_named'] = pd.cut(df['val2'], bins=bins, labels = label)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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