feature_engineering/combing_data_in_pandas.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "(244, 7)\n"
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "   total_bill   tip     sex smoker  day    time  size\n0       16.99  1.01  Female     No  Sun  Dinner     2\n1       10.34  1.66    Male     No  Sun  Dinner     3\n2       21.01  3.50    Male     No  Sun  Dinner     3\n3       23.68  3.31    Male     No  Sun  Dinner     2\n4       24.59  3.61  Female     No  Sun  Dinner     4",
      "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>total_bill</th>\n      <th>tip</th>\n      <th>sex</th>\n      <th>smoker</th>\n      <th>day</th>\n      <th>time</th>\n      <th>size</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>16.99</td>\n      <td>1.01</td>\n      <td>Female</td>\n      <td>No</td>\n      <td>Sun</td>\n      <td>Dinner</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>10.34</td>\n      <td>1.66</td>\n      <td>Male</td>\n      <td>No</td>\n      <td>Sun</td>\n      <td>Dinner</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>21.01</td>\n      <td>3.50</td>\n      <td>Male</td>\n      <td>No</td>\n      <td>Sun</td>\n      <td>Dinner</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>23.68</td>\n      <td>3.31</td>\n      <td>Male</td>\n      <td>No</td>\n      <td>Sun</td>\n      <td>Dinner</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>24.59</td>\n      <td>3.61</td>\n      <td>Female</td>\n      <td>No</td>\n      <td>Sun</td>\n      <td>Dinner</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 2
    }
   ],
   "source": [
    "# load tips data from seaborn \n",
    "data = sns.load_dataset(\"tips\")\n",
    "print(data.shape)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# splitting the data into two seperate df \n",
    "bill = data.groupby([\"sex\", \"smoker\"])[\"total_bill\", \"tip\"].sum()\n",
    "tip = bill.copy() \n",
    "# removing bill from tip and tip from bill \n",
    "del tip[\"total_bill\"]\n",
    "del bill[\"tip\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "(4, 3)\n"
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "      sex smoker  total_bill\n0    Male    Yes     1337.07\n1    Male     No     1919.75\n2  Female    Yes      593.27\n3  Female     No      977.68",
      "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>sex</th>\n      <th>smoker</th>\n      <th>total_bill</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Male</td>\n      <td>Yes</td>\n      <td>1337.07</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Male</td>\n      <td>No</td>\n      <td>1919.75</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Female</td>\n      <td>Yes</td>\n      <td>593.27</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Female</td>\n      <td>No</td>\n      <td>977.68</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "source": [
    "bill.reset_index(inplace= True)\n",
    "print(bill.shape)\n",
    "bill.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "(4, 3)\n"
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "      sex smoker     tip\n0    Male    Yes  183.07\n1    Male     No  302.00\n2  Female    Yes   96.74\n3  Female     No  149.77",
      "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>sex</th>\n      <th>smoker</th>\n      <th>tip</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Male</td>\n      <td>Yes</td>\n      <td>183.07</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Male</td>\n      <td>No</td>\n      <td>302.00</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Female</td>\n      <td>Yes</td>\n      <td>96.74</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Female</td>\n      <td>No</td>\n      <td>149.77</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "source": [
    "tip.reset_index(inplace= True) \n",
    "print(tip.shape)\n",
    "tip.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merging "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "text": "\u001b[1;31mSignature:\u001b[0m\n\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmerge\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n\u001b[0m    \u001b[0mleft\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m    \u001b[0mright\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m    \u001b[0mhow\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'inner'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m    \u001b[0mon\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m    \u001b[0mleft_on\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m    \u001b[0mright_on\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m    \u001b[0mleft_index\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m    \u001b[0mright_index\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m    \u001b[0msort\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m    \u001b[0msuffixes\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'_x'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'_y'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m    \u001b[0mcopy\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m    \u001b[0mindicator\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m    \u001b[0mvalidate\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[1;34m'DataFrame'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;31mDocstring:\u001b[0m\nMerge DataFrame or named Series objects with a database-style join.\n\nThe join is done on columns or indexes. If joining columns on\ncolumns, the DataFrame indexes *will be ignored*. Otherwise if joining indexes\non indexes or indexes on a column or columns, the index will be passed on.\n\nParameters\n----------\nleft : DataFrame\nright : DataFrame or named Series\n    Object to merge with.\nhow : {'left', 'right', 'outer', 'inner'}, default 'inner'\n    Type of merge to be performed.\n\n    * left: use only keys from left frame, similar to a SQL left outer join;\n      preserve key order.\n    * right: use only keys from right frame, similar to a SQL right outer join;\n      preserve key order.\n    * outer: use union of keys from both frames, similar to a SQL full outer\n      join; sort keys lexicographically.\n    * inner: use intersection of keys from both frames, similar to a SQL inner\n      join; preserve the order of the left keys.\non : label or list\n    Column or index level names to join on. These must be found in both\n    DataFrames. If `on` is None and not merging on indexes then this defaults\n    to the intersection of the columns in both DataFrames.\nleft_on : label or list, or array-like\n    Column or index level names to join on in the left DataFrame. Can also\n    be an array or list of arrays of the length of the left DataFrame.\n    These arrays are treated as if they are columns.\nright_on : label or list, or array-like\n    Column or index level names to join on in the right DataFrame. Can also\n    be an array or list of arrays of the length of the right DataFrame.\n    These arrays are treated as if they are columns.\nleft_index : bool, default False\n    Use the index from the left DataFrame as the join key(s). If it is a\n    MultiIndex, the number of keys in the other DataFrame (either the index\n    or a number of columns) must match the number of levels.\nright_index : bool, default False\n    Use the index from the right DataFrame as the join key. Same caveats as\n    left_index.\nsort : bool, default False\n    Sort the join keys lexicographically in the result DataFrame. If False,\n    the order of the join keys depends on the join type (how keyword).\nsuffixes : tuple of (str, str), default ('_x', '_y')\n    Suffix to apply to overlapping column names in the left and right\n    side, respectively. To raise an exception on overlapping columns use\n    (False, False).\ncopy : bool, default True\n    If False, avoid copy if possible.\nindicator : bool or str, default False\n    If True, adds a column to output DataFrame called \"_merge\" with\n    information on the source of each row.\n    If string, column with information on source of each row will be added to\n    output DataFrame, and column will be named value of string.\n    Information column is Categorical-type and takes on a value of \"left_only\"\n    for observations whose merge key only appears in 'left' DataFrame,\n    \"right_only\" for observations whose merge key only appears in 'right'\n    DataFrame, and \"both\" if the observation's merge key is found in both.\n\nvalidate : str, optional\n    If specified, checks if merge is of specified type.\n\n    * \"one_to_one\" or \"1:1\": check if merge keys are unique in both\n      left and right datasets.\n    * \"one_to_many\" or \"1:m\": check if merge keys are unique in left\n      dataset.\n    * \"many_to_one\" or \"m:1\": check if merge keys are unique in right\n      dataset.\n    * \"many_to_many\" or \"m:m\": allowed, but does not result in checks.\n\n    .. versionadded:: 0.21.0\n\nReturns\n-------\nDataFrame\n    A DataFrame of the two merged objects.\n\nSee Also\n--------\nmerge_ordered : Merge with optional filling/interpolation.\nmerge_asof : Merge on nearest keys.\nDataFrame.join : Similar method using indices.\n\nNotes\n-----\nSupport for specifying index levels as the `on`, `left_on`, and\n`right_on` parameters was added in version 0.23.0\nSupport for merging named Series objects was added in version 0.24.0\n\nExamples\n--------\n\n>>> df1 = pd.DataFrame({'lkey': ['foo', 'bar', 'baz', 'foo'],\n...                     'value': [1, 2, 3, 5]})\n>>> df2 = pd.DataFrame({'rkey': ['foo', 'bar', 'baz', 'foo'],\n...                     'value': [5, 6, 7, 8]})\n>>> df1\n    lkey value\n0   foo      1\n1   bar      2\n2   baz      3\n3   foo      5\n>>> df2\n    rkey value\n0   foo      5\n1   bar      6\n2   baz      7\n3   foo      8\n\nMerge df1 and df2 on the lkey and rkey columns. The value columns have\nthe default suffixes, _x and _y, appended.\n\n>>> df1.merge(df2, left_on='lkey', right_on='rkey')\n  lkey  value_x rkey  value_y\n0  foo        1  foo        5\n1  foo        1  foo        8\n2  foo        5  foo        5\n3  foo        5  foo        8\n4  bar        2  bar        6\n5  baz        3  baz        7\n\nMerge DataFrames df1 and df2 with specified left and right suffixes\nappended to any overlapping columns.\n\n>>> df1.merge(df2, left_on='lkey', right_on='rkey',\n...           suffixes=('_left', '_right'))\n  lkey  value_left rkey  value_right\n0  foo           1  foo            5\n1  foo           1  foo            8\n2  foo           5  foo            5\n3  foo           5  foo            8\n4  bar           2  bar            6\n5  baz           3  baz            7\n\nMerge DataFrames df1 and df2, but raise an exception if the DataFrames have\nany overlapping columns.\n\n>>> df1.merge(df2, left_on='lkey', right_on='rkey', suffixes=(False, False))\nTraceback (most recent call last):\n...\nValueError: columns overlap but no suffix specified:\n    Index(['value'], dtype='object')\n\u001b[1;31mFile:\u001b[0m      c:\\users\\kpy\\documents\\demo\\envs\\lib\\site-packages\\pandas\\core\\reshape\\merge.py\n\u001b[1;31mType:\u001b[0m      function\n"
    }
   ],
   "source": [
    "pd.merge?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "(4, 4)\n"
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "      sex smoker  total_bill     tip\n0    Male    Yes     1337.07  183.07\n1    Male     No     1919.75  302.00\n2  Female    Yes      593.27   96.74\n3  Female     No      977.68  149.77",
      "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>sex</th>\n      <th>smoker</th>\n      <th>total_bill</th>\n      <th>tip</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Male</td>\n      <td>Yes</td>\n      <td>1337.07</td>\n      <td>183.07</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Male</td>\n      <td>No</td>\n      <td>1919.75</td>\n      <td>302.00</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Female</td>\n      <td>Yes</td>\n      <td>593.27</td>\n      <td>96.74</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Female</td>\n      <td>No</td>\n      <td>977.68</td>\n      <td>149.77</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "source": [
    "# Inner join \n",
    "inner_join = pd.merge(bill, tip, how=\"inner\", on=[\"sex\", \"smoker\"])\n",
    "print(inner_join.shape)\n",
    "inner_join.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "(4, 5)\n"
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "      sex smoker  total_bill     tip     _merge\n0    Male    Yes     1337.07  183.07       both\n1    Male     No     1919.75  302.00       both\n2  Female    Yes      593.27     NaN  left_only\n3  Female     No      977.68     NaN  left_only",
      "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>sex</th>\n      <th>smoker</th>\n      <th>total_bill</th>\n      <th>tip</th>\n      <th>_merge</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Male</td>\n      <td>Yes</td>\n      <td>1337.07</td>\n      <td>183.07</td>\n      <td>both</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Male</td>\n      <td>No</td>\n      <td>1919.75</td>\n      <td>302.00</td>\n      <td>both</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Female</td>\n      <td>Yes</td>\n      <td>593.27</td>\n      <td>NaN</td>\n      <td>left_only</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Female</td>\n      <td>No</td>\n      <td>977.68</td>\n      <td>NaN</td>\n      <td>left_only</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 8
    }
   ],
   "source": [
    "# Left join\n",
    "left_join = pd.merge(bill, tip.head(2), how= \"left\", on=[\"sex\", \"smoker\"], indicator=True)\n",
    "print(left_join.shape)\n",
    "left_join.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "(4, 5)\n"
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "      sex smoker  total_bill     tip      _merge\n0    Male    Yes     1337.07  183.07        both\n1    Male     No     1919.75  302.00        both\n2  Female    Yes         NaN   96.74  right_only\n3  Female     No         NaN  149.77  right_only",
      "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>sex</th>\n      <th>smoker</th>\n      <th>total_bill</th>\n      <th>tip</th>\n      <th>_merge</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Male</td>\n      <td>Yes</td>\n      <td>1337.07</td>\n      <td>183.07</td>\n      <td>both</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Male</td>\n      <td>No</td>\n      <td>1919.75</td>\n      <td>302.00</td>\n      <td>both</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Female</td>\n      <td>Yes</td>\n      <td>NaN</td>\n      <td>96.74</td>\n      <td>right_only</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Female</td>\n      <td>No</td>\n      <td>NaN</td>\n      <td>149.77</td>\n      <td>right_only</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "source": [
    "# Right join\n",
    "right_join = pd.merge(bill.head(2), tip, how= \"right\", on=[\"sex\", \"smoker\"], indicator= True)\n",
    "print(right_join.shape)\n",
    "right_join.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "(4, 5)\n"
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "      sex smoker  total_bill     tip      _merge\n0  Female    Yes      593.27     NaN   left_only\n1  Female     No      977.68     NaN   left_only\n2    Male    Yes         NaN  183.07  right_only\n3    Male     No         NaN  302.00  right_only",
      "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>sex</th>\n      <th>smoker</th>\n      <th>total_bill</th>\n      <th>tip</th>\n      <th>_merge</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Female</td>\n      <td>Yes</td>\n      <td>593.27</td>\n      <td>NaN</td>\n      <td>left_only</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Female</td>\n      <td>No</td>\n      <td>977.68</td>\n      <td>NaN</td>\n      <td>left_only</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Male</td>\n      <td>Yes</td>\n      <td>NaN</td>\n      <td>183.07</td>\n      <td>right_only</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Male</td>\n      <td>No</td>\n      <td>NaN</td>\n      <td>302.00</td>\n      <td>right_only</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "source": [
    "# Outer join \n",
    "outer_join = pd.merge(bill.tail(2), tip.head(2), how= \"outer\", on=[\"sex\", \"smoker\"], indicator=True)\n",
    "print(outer_join.shape)\n",
    "outer_join.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting indexes \n",
    "bill.set_index(keys= [\"sex\", \"smoker\"], inplace= True)\n",
    "tip.set_index(keys= [\"sex\", \"smoker\"], inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "               total_bill\nsex    smoker            \nMale   Yes        1337.07\n       No         1919.75\nFemale Yes         593.27\n       No          977.68",
      "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></th>\n      <th>total_bill</th>\n    </tr>\n    <tr>\n      <th>sex</th>\n      <th>smoker</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">Male</th>\n      <th>Yes</th>\n      <td>1337.07</td>\n    </tr>\n    <tr>\n      <th>No</th>\n      <td>1919.75</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">Female</th>\n      <th>Yes</th>\n      <td>593.27</td>\n    </tr>\n    <tr>\n      <th>No</th>\n      <td>977.68</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 12
    }
   ],
   "source": [
    "bill.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "                  tip\nsex    smoker        \nMale   Yes     183.07\n       No      302.00\nFemale Yes      96.74\n       No      149.77",
      "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></th>\n      <th>tip</th>\n    </tr>\n    <tr>\n      <th>sex</th>\n      <th>smoker</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">Male</th>\n      <th>Yes</th>\n      <td>183.07</td>\n    </tr>\n    <tr>\n      <th>No</th>\n      <td>302.00</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">Female</th>\n      <th>Yes</th>\n      <td>96.74</td>\n    </tr>\n    <tr>\n      <th>No</th>\n      <td>149.77</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 13
    }
   ],
   "source": [
    "tip.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "               total_bill     tip\nsex    smoker                    \nMale   Yes        1337.07  183.07\n       No         1919.75  302.00\nFemale Yes         593.27   96.74\n       No          977.68  149.77",
      "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></th>\n      <th>total_bill</th>\n      <th>tip</th>\n    </tr>\n    <tr>\n      <th>sex</th>\n      <th>smoker</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">Male</th>\n      <th>Yes</th>\n      <td>1337.07</td>\n      <td>183.07</td>\n    </tr>\n    <tr>\n      <th>No</th>\n      <td>1919.75</td>\n      <td>302.00</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">Female</th>\n      <th>Yes</th>\n      <td>593.27</td>\n      <td>96.74</td>\n    </tr>\n    <tr>\n      <th>No</th>\n      <td>977.68</td>\n      <td>149.77</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 14
    }
   ],
   "source": [
    "# Merging on index\n",
    "index_merge= pd.merge(bill, tip, how=\"inner\", left_index=True, right_index=True)\n",
    "index_merge.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "               total_bill     tip\nsex    smoker                    \nMale   Yes        1337.07  183.07\n       No         1919.75  302.00\nFemale Yes         593.27   96.74\n       No          977.68  149.77",
      "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></th>\n      <th>total_bill</th>\n      <th>tip</th>\n    </tr>\n    <tr>\n      <th>sex</th>\n      <th>smoker</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">Male</th>\n      <th>Yes</th>\n      <td>1337.07</td>\n      <td>183.07</td>\n    </tr>\n    <tr>\n      <th>No</th>\n      <td>1919.75</td>\n      <td>302.00</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">Female</th>\n      <th>Yes</th>\n      <td>593.27</td>\n      <td>96.74</td>\n    </tr>\n    <tr>\n      <th>No</th>\n      <td>977.68</td>\n      <td>149.77</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 15
    }
   ],
   "source": [
    "# Merging partial index and column \n",
    "partial_merge= pd.merge(bill, tip, how=\"inner\", left_index=True, right_on=[\"sex\", \"smoker\"])\n",
    "partial_merge.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Concat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "text": "\u001b[1;31mSignature:\u001b[0m\n\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n\u001b[0m    \u001b[0mobjs\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mIterable\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mUnion\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mForwardRef\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'DataFrame'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mForwardRef\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Series'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mMapping\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mUnion\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mHashable\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mNoneType\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mUnion\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mForwardRef\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'DataFrame'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mForwardRef\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Series'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m    \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m    \u001b[0mjoin\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'outer'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m    \u001b[0mignore_index\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m    \u001b[0mkeys\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m    \u001b[0mlevels\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m    \u001b[0mnames\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m    \u001b[0mverify_integrity\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m    \u001b[0msort\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m    \u001b[0mcopy\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[0mUnion\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mForwardRef\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'DataFrame'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mForwardRef\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Series'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;31mDocstring:\u001b[0m\nConcatenate pandas objects along a particular axis with optional set logic\nalong the other axes.\n\nCan also add a layer of hierarchical indexing on the concatenation axis,\nwhich may be useful if the labels are the same (or overlapping) on\nthe passed axis number.\n\nParameters\n----------\nobjs : a sequence or mapping of Series or DataFrame objects\n    If a dict is passed, the sorted keys will be used as the `keys`\n    argument, unless it is passed, in which case the values will be\n    selected (see below). Any None objects will be dropped silently unless\n    they are all None in which case a ValueError will be raised.\naxis : {0/'index', 1/'columns'}, default 0\n    The axis to concatenate along.\njoin : {'inner', 'outer'}, default 'outer'\n    How to handle indexes on other axis (or axes).\nignore_index : bool, default False\n    If True, do not use the index values along the concatenation axis. The\n    resulting axis will be labeled 0, ..., n - 1. This is useful if you are\n    concatenating objects where the concatenation axis does not have\n    meaningful indexing information. Note the index values on the other\n    axes are still respected in the join.\nkeys : sequence, default None\n    If multiple levels passed, should contain tuples. Construct\n    hierarchical index using the passed keys as the outermost level.\nlevels : list of sequences, default None\n    Specific levels (unique values) to use for constructing a\n    MultiIndex. Otherwise they will be inferred from the keys.\nnames : list, default None\n    Names for the levels in the resulting hierarchical index.\nverify_integrity : bool, default False\n    Check whether the new concatenated axis contains duplicates. This can\n    be very expensive relative to the actual data concatenation.\nsort : bool, default False\n    Sort non-concatenation axis if it is not already aligned when `join`\n    is 'outer'.\n    This has no effect when ``join='inner'``, which already preserves\n    the order of the non-concatenation axis.\n\n    .. versionadded:: 0.23.0\n    .. versionchanged:: 1.0.0\n\n       Changed to not sort by default.\n\ncopy : bool, default True\n    If False, do not copy data unnecessarily.\n\nReturns\n-------\nobject, type of objs\n    When concatenating all ``Series`` along the index (axis=0), a\n    ``Series`` is returned. When ``objs`` contains at least one\n    ``DataFrame``, a ``DataFrame`` is returned. When concatenating along\n    the columns (axis=1), a ``DataFrame`` is returned.\n\nSee Also\n--------\nSeries.append : Concatenate Series.\nDataFrame.append : Concatenate DataFrames.\nDataFrame.join : Join DataFrames using indexes.\nDataFrame.merge : Merge DataFrames by indexes or columns.\n\nNotes\n-----\nThe keys, levels, and names arguments are all optional.\n\nA walkthrough of how this method fits in with other tools for combining\npandas objects can be found `here\n<https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html>`__.\n\nExamples\n--------\nCombine two ``Series``.\n\n>>> s1 = pd.Series(['a', 'b'])\n>>> s2 = pd.Series(['c', 'd'])\n>>> pd.concat([s1, s2])\n0    a\n1    b\n0    c\n1    d\ndtype: object\n\nClear the existing index and reset it in the result\nby setting the ``ignore_index`` option to ``True``.\n\n>>> pd.concat([s1, s2], ignore_index=True)\n0    a\n1    b\n2    c\n3    d\ndtype: object\n\nAdd a hierarchical index at the outermost level of\nthe data with the ``keys`` option.\n\n>>> pd.concat([s1, s2], keys=['s1', 's2'])\ns1  0    a\n    1    b\ns2  0    c\n    1    d\ndtype: object\n\nLabel the index keys you create with the ``names`` option.\n\n>>> pd.concat([s1, s2], keys=['s1', 's2'],\n...           names=['Series name', 'Row ID'])\nSeries name  Row ID\ns1           0         a\n             1         b\ns2           0         c\n             1         d\ndtype: object\n\nCombine two ``DataFrame`` objects with identical columns.\n\n>>> df1 = pd.DataFrame([['a', 1], ['b', 2]],\n...                    columns=['letter', 'number'])\n>>> df1\n  letter  number\n0      a       1\n1      b       2\n>>> df2 = pd.DataFrame([['c', 3], ['d', 4]],\n...                    columns=['letter', 'number'])\n>>> df2\n  letter  number\n0      c       3\n1      d       4\n>>> pd.concat([df1, df2])\n  letter  number\n0      a       1\n1      b       2\n0      c       3\n1      d       4\n\nCombine ``DataFrame`` objects with overlapping columns\nand return everything. Columns outside the intersection will\nbe filled with ``NaN`` values.\n\n>>> df3 = pd.DataFrame([['c', 3, 'cat'], ['d', 4, 'dog']],\n...                    columns=['letter', 'number', 'animal'])\n>>> df3\n  letter  number animal\n0      c       3    cat\n1      d       4    dog\n>>> pd.concat([df1, df3], sort=False)\n  letter  number animal\n0      a       1    NaN\n1      b       2    NaN\n0      c       3    cat\n1      d       4    dog\n\nCombine ``DataFrame`` objects with overlapping columns\nand return only those that are shared by passing ``inner`` to\nthe ``join`` keyword argument.\n\n>>> pd.concat([df1, df3], join=\"inner\")\n  letter  number\n0      a       1\n1      b       2\n0      c       3\n1      d       4\n\nCombine ``DataFrame`` objects horizontally along the x axis by\npassing in ``axis=1``.\n\n>>> df4 = pd.DataFrame([['bird', 'polly'], ['monkey', 'george']],\n...                    columns=['animal', 'name'])\n>>> pd.concat([df1, df4], axis=1)\n  letter  number  animal    name\n0      a       1    bird   polly\n1      b       2  monkey  george\n\nPrevent the result from including duplicate index values with the\n``verify_integrity`` option.\n\n>>> df5 = pd.DataFrame([1], index=['a'])\n>>> df5\n   0\na  1\n>>> df6 = pd.DataFrame([2], index=['a'])\n>>> df6\n   0\na  2\n>>> pd.concat([df5, df6], verify_integrity=True)\nTraceback (most recent call last):\n    ...\nValueError: Indexes have overlapping values: ['a']\n\u001b[1;31mFile:\u001b[0m      c:\\users\\kpy\\documents\\demo\\envs\\lib\\site-packages\\pandas\\core\\reshape\\concat.py\n\u001b[1;31mType:\u001b[0m      function\n"
    }
   ],
   "source": [
    "pd.concat?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "               total_bill     tip\nsex    smoker                    \nMale   Yes        1337.07     NaN\n       No         1919.75     NaN\nFemale Yes         593.27     NaN\n       No          977.68     NaN\nMale   Yes            NaN  183.07\n       No             NaN  302.00\nFemale Yes            NaN   96.74\n       No             NaN  149.77",
      "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></th>\n      <th>total_bill</th>\n      <th>tip</th>\n    </tr>\n    <tr>\n      <th>sex</th>\n      <th>smoker</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">Male</th>\n      <th>Yes</th>\n      <td>1337.07</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>No</th>\n      <td>1919.75</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">Female</th>\n      <th>Yes</th>\n      <td>593.27</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>No</th>\n      <td>977.68</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">Male</th>\n      <th>Yes</th>\n      <td>NaN</td>\n      <td>183.07</td>\n    </tr>\n    <tr>\n      <th>No</th>\n      <td>NaN</td>\n      <td>302.00</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">Female</th>\n      <th>Yes</th>\n      <td>NaN</td>\n      <td>96.74</td>\n    </tr>\n    <tr>\n      <th>No</th>\n      <td>NaN</td>\n      <td>149.77</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 17
    }
   ],
   "source": [
    "# Gluing dataframes row-wise \n",
    "row_glue= pd.concat([bill, tip], axis=0, sort=False)\n",
    "row_glue.head(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "               total_bill     tip\nsex    smoker                    \nMale   Yes        1337.07  183.07\n       No         1919.75  302.00\nFemale Yes         593.27   96.74\n       No          977.68  149.77",
      "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></th>\n      <th>total_bill</th>\n      <th>tip</th>\n    </tr>\n    <tr>\n      <th>sex</th>\n      <th>smoker</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">Male</th>\n      <th>Yes</th>\n      <td>1337.07</td>\n      <td>183.07</td>\n    </tr>\n    <tr>\n      <th>No</th>\n      <td>1919.75</td>\n      <td>302.00</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">Female</th>\n      <th>Yes</th>\n      <td>593.27</td>\n      <td>96.74</td>\n    </tr>\n    <tr>\n      <th>No</th>\n      <td>977.68</td>\n      <td>149.77</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 18
    }
   ],
   "source": [
    "# Gluing dataframes column-wise\n",
    "col_glue= pd.concat([bill, tip], axis=1, sort=False)\n",
    "col_glue.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "                   total_bill     tip\n    sex    smoker                    \ndf1 Male   Yes        1337.07     NaN\n           No         1919.75     NaN\n    Female Yes         593.27     NaN\n           No          977.68     NaN\ndf2 Male   Yes            NaN  183.07\n           No             NaN  302.00\n    Female Yes            NaN   96.74\n           No             NaN  149.77",
      "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></th>\n      <th></th>\n      <th>total_bill</th>\n      <th>tip</th>\n    </tr>\n    <tr>\n      <th></th>\n      <th>sex</th>\n      <th>smoker</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"4\" valign=\"top\">df1</th>\n      <th rowspan=\"2\" valign=\"top\">Male</th>\n      <th>Yes</th>\n      <td>1337.07</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>No</th>\n      <td>1919.75</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">Female</th>\n      <th>Yes</th>\n      <td>593.27</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>No</th>\n      <td>977.68</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th rowspan=\"4\" valign=\"top\">df2</th>\n      <th rowspan=\"2\" valign=\"top\">Male</th>\n      <th>Yes</th>\n      <td>NaN</td>\n      <td>183.07</td>\n    </tr>\n    <tr>\n      <th>No</th>\n      <td>NaN</td>\n      <td>302.00</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">Female</th>\n      <th>Yes</th>\n      <td>NaN</td>\n      <td>96.74</td>\n    </tr>\n    <tr>\n      <th>No</th>\n      <td>NaN</td>\n      <td>149.77</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 19
    }
   ],
   "source": [
    "# Adding an indicator \n",
    "indicator_glue = pd.concat([bill, tip], axis=0, sort=False, keys=[\"df1\", \"df2\"])\n",
    "indicator_glue "
   ]
  }
 ],
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