7.Concat-merge-join.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Operation: ```concat(), merge(), join()```\n",
    "\n",
    "This notebook will provide a walkthrough for data ```concat, merge and join``` functionality for combining multiple DataFrame objects.\n",
    "\n",
    "Some part of this notebook are taken from Pydata tutorials. Read more about these functionality from [Pandas Pydata Document about concat, merge, join](https://pandas.pydata.org/docs/user_guide/merging.html)[1].\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": [
    "#### 1. Concatanation of data frames by index:\n",
    " Here we create three seperate Dataframe with same column names (e.g., ```col A, col B, col C, col D```) and create a final dataframe with concatenating these dataframes. Read more from [pydata](https://pandas.pydata.org/docs/user_guide/merging.html)\n",
    " \n",
    " The defult setting is: ```pd.concat(objs, axis=0, join='outer', ignore_index=False, keys=None,\n",
    "          levels=None, names=None, verify_integrity=False, copy=True)```\n",
    " \n",
    "\n",
    "![img](https://pandas.pydata.org/docs/_images/merging_concat_basic.png)\n",
    "\n",
    "<center> Image courtesy : Pydata </center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col A</th>\n",
       "      <th>col B</th>\n",
       "      <th>col C</th>\n",
       "      <th>col D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A0</td>\n",
       "      <td>B0</td>\n",
       "      <td>C0</td>\n",
       "      <td>D0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A1</td>\n",
       "      <td>B1</td>\n",
       "      <td>C1</td>\n",
       "      <td>D1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A2</td>\n",
       "      <td>B2</td>\n",
       "      <td>C2</td>\n",
       "      <td>D2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A3</td>\n",
       "      <td>B3</td>\n",
       "      <td>C3</td>\n",
       "      <td>D3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A4</td>\n",
       "      <td>B4</td>\n",
       "      <td>C4</td>\n",
       "      <td>D4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A5</td>\n",
       "      <td>B5</td>\n",
       "      <td>C5</td>\n",
       "      <td>D5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>A6</td>\n",
       "      <td>B6</td>\n",
       "      <td>C6</td>\n",
       "      <td>D6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>A7</td>\n",
       "      <td>B7</td>\n",
       "      <td>C7</td>\n",
       "      <td>D7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>A8</td>\n",
       "      <td>B8</td>\n",
       "      <td>C8</td>\n",
       "      <td>D8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>A9</td>\n",
       "      <td>B9</td>\n",
       "      <td>C9</td>\n",
       "      <td>D9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>A10</td>\n",
       "      <td>B10</td>\n",
       "      <td>C10</td>\n",
       "      <td>D10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>A11</td>\n",
       "      <td>B11</td>\n",
       "      <td>C11</td>\n",
       "      <td>D11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   col A col B col C col D\n",
       "0     A0    B0    C0    D0\n",
       "1     A1    B1    C1    D1\n",
       "2     A2    B2    C2    D2\n",
       "3     A3    B3    C3    D3\n",
       "4     A4    B4    C4    D4\n",
       "5     A5    B5    C5    D5\n",
       "6     A6    B6    C6    D6\n",
       "7     A7    B7    C7    D7\n",
       "8     A8    B8    C8    D8\n",
       "9     A9    B9    C9    D9\n",
       "10   A10   B10   C10   D10\n",
       "11   A11   B11   C11   D11"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.DataFrame({'col A': ['A0', 'A1', 'A2', 'A3'],\n",
    "                    'col B': ['B0', 'B1', 'B2', 'B3'],\n",
    "                    'col C': ['C0', 'C1', 'C2', 'C3'],\n",
    "                    'col D': ['D0', 'D1', 'D2', 'D3']},\n",
    "                    index=[0, 1, 2, 3])\n",
    " \n",
    "\n",
    "df2 = pd.DataFrame({'col A': ['A4', 'A5', 'A6', 'A7'],\n",
    "                    'col B': ['B4', 'B5', 'B6', 'B7'],\n",
    "                    'col C': ['C4', 'C5', 'C6', 'C7'],\n",
    "                    'col D': ['D4', 'D5', 'D6', 'D7']},\n",
    "                    index=[4, 5, 6, 7])\n",
    "     \n",
    "\n",
    "df3 = pd.DataFrame({'col A': ['A8', 'A9', 'A10', 'A11'],\n",
    "                    'col B': ['B8', 'B9', 'B10', 'B11'],\n",
    "                    'col C': ['C8', 'C9', 'C10', 'C11'],\n",
    "                    'col D': ['D8', 'D9', 'D10', 'D11']},\n",
    "                    index=[8, 9, 10, 11])\n",
    "     \n",
    "\n",
    "frames = [df1, df2, df3]\n",
    "result = pd.concat(frames, sort=False)\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Concatenating different dataframes with different column names with defult ```join=\"outer\"```."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
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       "      <th>colA</th>\n",
       "      <th>colB</th>\n",
       "      <th>colC</th>\n",
       "      <th>colD</th>\n",
       "      <th>colE</th>\n",
       "      <th>colF</th>\n",
       "      <th>colG</th>\n",
       "      <th>colH</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A1</td>\n",
       "      <td>B1</td>\n",
       "      <td>C1</td>\n",
       "      <td>D1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A2</td>\n",
       "      <td>B2</td>\n",
       "      <td>C2</td>\n",
       "      <td>D2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A3</td>\n",
       "      <td>B3</td>\n",
       "      <td>C3</td>\n",
       "      <td>D3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E4</td>\n",
       "      <td>F4</td>\n",
       "      <td>G4</td>\n",
       "      <td>H4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E5</td>\n",
       "      <td>F5</td>\n",
       "      <td>G5</td>\n",
       "      <td>H5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E6</td>\n",
       "      <td>F6</td>\n",
       "      <td>G6</td>\n",
       "      <td>H6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E7</td>\n",
       "      <td>F7</td>\n",
       "      <td>G7</td>\n",
       "      <td>H7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>A8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>H8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>A9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>H9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>A10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>H10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>A11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>H11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   colA colB colC colD colE colF colG colH\n",
       "0    A0   B0   C0   D0  NaN  NaN  NaN  NaN\n",
       "1    A1   B1   C1   D1  NaN  NaN  NaN  NaN\n",
       "2    A2   B2   C2   D2  NaN  NaN  NaN  NaN\n",
       "3    A3   B3   C3   D3  NaN  NaN  NaN  NaN\n",
       "4   NaN  NaN  NaN  NaN   E4   F4   G4   H4\n",
       "5   NaN  NaN  NaN  NaN   E5   F5   G5   H5\n",
       "6   NaN  NaN  NaN  NaN   E6   F6   G6   H6\n",
       "7   NaN  NaN  NaN  NaN   E7   F7   G7   H7\n",
       "8    A8  NaN   C8  NaN   E8  NaN  NaN   H8\n",
       "9    A9  NaN   C9  NaN   E9  NaN  NaN   H9\n",
       "10  A10  NaN  C10  NaN  E10  NaN  NaN  H10\n",
       "11  A11  NaN  C11  NaN  E11  NaN  NaN  H11"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4 = pd.DataFrame({'colE': ['E4', 'E5', 'E6', 'E7'],\n",
    "                    'colF': ['F4', 'F5', 'F6', 'F7'],\n",
    "                    'colG': ['G4', 'G5', 'G6', 'G7'],\n",
    "                    'colH': ['H4', 'H5', 'H6', 'H7']},\n",
    "                    index=[4, 5, 6, 7])\n",
    "     \n",
    "\n",
    "df5 = pd.DataFrame({'colA': ['A8', 'A9', 'A10', 'A11'],\n",
    "                    'colE': ['E8', 'E9', 'E10', 'E11'],\n",
    "                    'colC': ['C8', 'C9', 'C10', 'C11'],\n",
    "                    'colH': ['H8', 'H9', 'H10', 'H11']},\n",
    "                    index=[8, 9, 10, 11])\n",
    "     \n",
    "\n",
    "frames = [df1, df2, df3]\n",
    "result = pd.concat(frames, sort=False)\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Concatenating different dataframes with different column names with defult ```join=\"inner\"``` and ```axis =0```."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>colA</th>\n",
       "      <th>colC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A0</td>\n",
       "      <td>C0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A1</td>\n",
       "      <td>C1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A2</td>\n",
       "      <td>C2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A3</td>\n",
       "      <td>C3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>A8</td>\n",
       "      <td>C8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>A9</td>\n",
       "      <td>C9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>A10</td>\n",
       "      <td>C10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>A11</td>\n",
       "      <td>C11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   colA colC\n",
       "0    A0   C0\n",
       "1    A1   C1\n",
       "2    A2   C2\n",
       "3    A3   C3\n",
       "8    A8   C8\n",
       "9    A9   C9\n",
       "10  A10  C10\n",
       "11  A11  C11"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frames = [df1, df5]\n",
    "result = pd.concat(frames, sort=False, join = \"inner\")\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<center> axis = 1</center>\n",
    "<img src=\"https://miro.medium.com/max/875/1*LoUq8uZrbg_tO3t4tqZfqg.png\" width=\"400\" height=\"400\"></img>\n",
    "\n",
    "----------\n",
    "\n",
    "<center> axis = 0</center>\n",
    "<img src=\"https://miro.medium.com/max/875/1*bQ3Bl6_N_V4er6XZxVxIZA.png\" width=\"400\" height=\"400\"></img>\n",
    "\n",
    "------\n",
    "<center> Image Courtesy www.towardsdatascience.com </center> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Merge dataframe\n",
    "\n",
    "Two ```DataFrame``` object can be merged on specific ```key```.\n",
    "\n",
    "Defult setting : ```pd.merge(left, right, how='inner', on=None, left_on=None, right_on=None,\n",
    "         left_index=False, right_index=False, sort=True,\n",
    "         suffixes=('_x', '_y'), copy=True, indicator=False,\n",
    "         validate=None)```\n",
    "\n",
    "![img](https://pandas.pydata.org/docs/_images/merging_merge_on_key.png)\n",
    "<center> Image courtesy : Pydata </center>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
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       "      <th>0</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>K1</td>\n",
       "      <td>A1</td>\n",
       "      <td>B1</td>\n",
       "      <td>C1</td>\n",
       "      <td>D1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>K2</td>\n",
       "      <td>A2</td>\n",
       "      <td>B2</td>\n",
       "      <td>C2</td>\n",
       "      <td>D2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>K3</td>\n",
       "      <td>A3</td>\n",
       "      <td>B3</td>\n",
       "      <td>C3</td>\n",
       "      <td>D3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key   A   B   C   D\n",
       "0  K0  A0  B0  C0  D0\n",
       "1  K1  A1  B1  C1  D1\n",
       "2  K2  A2  B2  C2  D2\n",
       "3  K3  A3  B3  C3  D3"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "left = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3'],\n",
    "                    'A': ['A0', 'A1', 'A2', 'A3'],\n",
    "                          'B': ['B0', 'B1', 'B2', 'B3']})\n",
    "     \n",
    "\n",
    "right = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3'],\n",
    "                    'C': ['C0', 'C1', 'C2', 'C3'],\n",
    "                    'D': ['D0', 'D1', 'D2', 'D3']})\n",
    "     \n",
    "\n",
    "result = pd.merge(left, right, on='key')\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "    \n",
    "<img src=\"https://datascience.quantecon.org/assets/_static/merge_files/merge_venns.png\" width=\"400\" height=\"300\"></img>\n",
    "\n",
    "\n",
    "<center> Image Courtesy www.datascience.quantecon.org </center> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. Join DataFrame"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Joining two ```DataFrame``` objects with ```join``` operation. Defult setting is ```join='inner'```.\n",
    "![img](![image.png](attachment:image.png)\n",
    "<center> Image courtesy : Pydata </center>"
   ]
  },
  {
   "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",
       "    }\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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>K0</th>\n",
       "      <td>A0</td>\n",
       "      <td>B0</td>\n",
       "      <td>C0</td>\n",
       "      <td>D0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K1</th>\n",
       "      <td>A1</td>\n",
       "      <td>B1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K2</th>\n",
       "      <td>A2</td>\n",
       "      <td>B2</td>\n",
       "      <td>C2</td>\n",
       "      <td>D2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A   B    C    D\n",
       "K0  A0  B0   C0   D0\n",
       "K1  A1  B1  NaN  NaN\n",
       "K2  A2  B2   C2   D2"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "left = pd.DataFrame({'A': ['A0', 'A1', 'A2'],\n",
    "                          'B': ['B0', 'B1', 'B2']},\n",
    "                         index=['K0', 'K1', 'K2'])\n",
    "     \n",
    "\n",
    "right = pd.DataFrame({'C': ['C0', 'C2', 'C3'],\n",
    "                           'D': ['D0', 'D2', 'D3']},\n",
    "                          index=['K0', 'K2', 'K3'])\n",
    "     \n",
    "\n",
    "result = left.join(right)\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://miro.medium.com/max/7360/1*-I_1qa5TIiB5eNYxnodfAA.png\" width=\"600\" height=\"500\"></img>\n",
    "\n",
    "\n",
    "<center> Image Courtesy www.towardsdatascience.com </center> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### References:\n",
    "1. [Pandas Pydata Document](https://pandas.pydata.org/docs/user_guide/merging.html)\n",
    "2. [Python Pandas DataFrame Join, Merge, and Concatenate](https://towardsdatascience.com/python-pandas-dataframe-join-merge-and-concatenate-84985c29ef78)\n",
    "3. [Merge, concat, Join in pandas](https://datascience.quantecon.org/pandas/merge.html)"
   ]
  }
 ],
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