P4 - Merging the data .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5328d837",
   "metadata": {},
   "source": [
    "Data we use for your projects won't always be confined to a single table in a CSV or excel file. Data is often split across several tables that we need to combine in some way."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5f9d823",
   "metadata": {},
   "source": [
    "we can join data frames in Python using functions included with pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0e766c33",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd \n",
    "import os "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ad6ce287",
   "metadata": {},
   "outputs": [
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       "      <th>P_ID</th>\n",
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      "text/plain": [
       "   P_ID  gender  height  weight\n",
       "0     1    male      71     175\n",
       "1     2    male      73     225\n",
       "2     3  female      64     130\n",
       "3     4  female      64     125\n",
       "4     5  female      66     165\n",
       "5     6    male      69     160\n",
       "6     7  female      62     115\n",
       "7     8    male      72     250"
      ]
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     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table1 = pd.DataFrame({\"P_ID\" : (1,2,3,4,5,6,7,8),\n",
    "                     \"gender\" : (\"male\", \"male\", \"female\",\"female\",\n",
    "                                \"female\", \"male\", \"female\", \"male\"),\n",
    "                     \"height\" : (71,73,64,64,66,69,62,72),\n",
    "                     \"weight\" : (175,225,130,125,165,160,115,250)})\n",
    "\n",
    "table1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "69e5e05f",
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   "outputs": [
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       "      <td>0</td>\n",
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       "      <th>6</th>\n",
       "      <td>9</td>\n",
       "      <td>male</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "   P_ID     sex  visits  checkup  follow_up  illness  surgery  ER\n",
       "0     1    male       1        1          0        0        0   0\n",
       "1     2    male       2        1          0        0        0   1\n",
       "2     4  female       4        1          1        2        0   0\n",
       "3     5  female      12        1          2        7        2   0\n",
       "4     7  female       2        1          0        1        0   0\n",
       "5     8    male       2        1          0        1        0   0\n",
       "6     9    male       1        0          0        0        0   1\n",
       "7    10  female       1        0          0        0        0   1"
      ]
     },
     "execution_count": 3,
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     "output_type": "execute_result"
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   ],
   "source": [
    "table2 = pd.DataFrame({\"P_ID\" : (1, 2, 4, 5, 7, 8, 9, 10),\n",
    "                     \"sex\" : (\"male\", \"male\", \"female\",\"female\",\n",
    "                            \"female\", \"male\", \"male\", \"female\"),\n",
    "                     \"visits\" : (1,2,4,12,2,2,1,1),\n",
    "                     \"checkup\" : (1,1,1,1,1,1,0,0),\n",
    "                     \"follow_up\" : (0,0,1,2,0,0,0,0),\n",
    "                     \"illness\" : (0,0,2,7,1,1,0,0),\n",
    "                     \"surgery\" : (0,0,0,2,0,0,0,0),\n",
    "                     \"ER\" : ( 0,1,0,0,0,0,1,1) } ) \n",
    "\n",
    "table2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d63d4787",
   "metadata": {},
   "source": [
    "Both data frames contain the column \"P_ID\" but the other columns are different.\n",
    "we can combine two data frames by a common columns  with merge():"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "789c822f",
   "metadata": {},
   "outputs": [],
   "source": [
    "c1 = pd.merge(table1,\n",
    "             table2,\n",
    "             how = 'inner',\n",
    "             on = 'P_ID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7ae32ac6",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   P_ID  gender  height  weight     sex  visits  checkup  follow_up  illness  \\\n",
       "0     1    male      71     175    male       1        1          0        0   \n",
       "1     2    male      73     225    male       2        1          0        0   \n",
       "2     4  female      64     125  female       4        1          1        2   \n",
       "3     5  female      66     165  female      12        1          2        7   \n",
       "4     7  female      62     115  female       2        1          0        1   \n",
       "5     8    male      72     250    male       2        1          0        1   \n",
       "\n",
       "   surgery  ER  \n",
       "0        0   0  \n",
       "1        0   1  \n",
       "2        0   0  \n",
       "3        2   0  \n",
       "4        0   0  \n",
       "5        0   0  "
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     "execution_count": 5,
     "metadata": {},
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   "source": [
    "c1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59979a78",
   "metadata": {},
   "source": [
    "* Inspecting the new combined data frame, we can see that the number of records dropped from 8 in the original tables to 6 in the combined table. If we inspect the P_ID column closely, we see that the original data tables contain some different values for P_ID. Note that inside the merge function we set the argument \"how\" to \"inner\". An inner join only merges records that appear in both columns used for the join. Since patients 3 and 6 only appear in table1 and patients 9 and 10 only appear in table2, those four patients were dropped when we merged the tables together.\n",
    "* By changing the how we can introduced various types of joins."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6e68de77",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Performing the left joins on the tables \n",
    "\n",
    "lj = pd.merge(table1,\n",
    "        table2,\n",
    "        how= 'left',\n",
    "        on = 'P_ID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ff50c307",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>female</td>\n",
       "      <td>62</td>\n",
       "      <td>115</td>\n",
       "      <td>female</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>male</td>\n",
       "      <td>72</td>\n",
       "      <td>250</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   P_ID  gender  height  weight     sex  visits  checkup  follow_up  illness  \\\n",
       "0     1    male      71     175    male     1.0      1.0        0.0      0.0   \n",
       "1     2    male      73     225    male     2.0      1.0        0.0      0.0   \n",
       "2     3  female      64     130     NaN     NaN      NaN        NaN      NaN   \n",
       "3     4  female      64     125  female     4.0      1.0        1.0      2.0   \n",
       "4     5  female      66     165  female    12.0      1.0        2.0      7.0   \n",
       "5     6    male      69     160     NaN     NaN      NaN        NaN      NaN   \n",
       "6     7  female      62     115  female     2.0      1.0        0.0      1.0   \n",
       "7     8    male      72     250    male     2.0      1.0        0.0      1.0   \n",
       "\n",
       "   surgery   ER  \n",
       "0      0.0  0.0  \n",
       "1      0.0  1.0  \n",
       "2      NaN  NaN  \n",
       "3      0.0  0.0  \n",
       "4      2.0  0.0  \n",
       "5      NaN  NaN  \n",
       "6      0.0  0.0  \n",
       "7      0.0  0.0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5b6d98b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Performing the right join on the tables \n",
    "\n",
    "rj = pd.merge(table1,\n",
    "        table2,\n",
    "        how = 'right',\n",
    "        on = 'P_ID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "233746f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>P_ID</th>\n",
       "      <th>gender</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>sex</th>\n",
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       "      <th>checkup</th>\n",
       "      <th>follow_up</th>\n",
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       "  </thead>\n",
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       "      <td>225.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>female</td>\n",
       "      <td>64.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>female</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>female</td>\n",
       "      <td>66.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>female</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>female</td>\n",
       "      <td>62.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>female</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8</td>\n",
       "      <td>male</td>\n",
       "      <td>72.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>male</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   P_ID  gender  height  weight     sex  visits  checkup  follow_up  illness  \\\n",
       "0     1    male    71.0   175.0    male       1        1          0        0   \n",
       "1     2    male    73.0   225.0    male       2        1          0        0   \n",
       "2     4  female    64.0   125.0  female       4        1          1        2   \n",
       "3     5  female    66.0   165.0  female      12        1          2        7   \n",
       "4     7  female    62.0   115.0  female       2        1          0        1   \n",
       "5     8    male    72.0   250.0    male       2        1          0        1   \n",
       "6     9     NaN     NaN     NaN    male       1        0          0        0   \n",
       "7    10     NaN     NaN     NaN  female       1        0          0        0   \n",
       "\n",
       "   surgery  ER  \n",
       "0        0   0  \n",
       "1        0   1  \n",
       "2        0   0  \n",
       "3        2   0  \n",
       "4        0   0  \n",
       "5        0   0  \n",
       "6        0   1  \n",
       "7        0   1  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8b29d15c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>NaN</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>4</td>\n",
       "      <td>female</td>\n",
       "      <td>64.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>female</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>2.0</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>female</td>\n",
       "      <td>66.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>female</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>male</td>\n",
       "      <td>69.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>female</td>\n",
       "      <td>62.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>female</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>male</td>\n",
       "      <td>72.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>female</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   P_ID  gender  height  weight     sex  visits  checkup  follow_up  illness  \\\n",
       "0     1    male    71.0   175.0    male     1.0      1.0        0.0      0.0   \n",
       "1     2    male    73.0   225.0    male     2.0      1.0        0.0      0.0   \n",
       "2     3  female    64.0   130.0     NaN     NaN      NaN        NaN      NaN   \n",
       "3     4  female    64.0   125.0  female     4.0      1.0        1.0      2.0   \n",
       "4     5  female    66.0   165.0  female    12.0      1.0        2.0      7.0   \n",
       "5     6    male    69.0   160.0     NaN     NaN      NaN        NaN      NaN   \n",
       "6     7  female    62.0   115.0  female     2.0      1.0        0.0      1.0   \n",
       "7     8    male    72.0   250.0    male     2.0      1.0        0.0      1.0   \n",
       "8     9     NaN     NaN     NaN    male     1.0      0.0        0.0      0.0   \n",
       "9    10     NaN     NaN     NaN  female     1.0      0.0        0.0      0.0   \n",
       "\n",
       "   surgery   ER  \n",
       "0      0.0  0.0  \n",
       "1      0.0  1.0  \n",
       "2      NaN  NaN  \n",
       "3      0.0  0.0  \n",
       "4      2.0  0.0  \n",
       "5      NaN  NaN  \n",
       "6      0.0  0.0  \n",
       "7      0.0  0.0  \n",
       "8      0.0  1.0  \n",
       "9      0.0  1.0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# performing the outer join on the tables \n",
    "\n",
    "oj = pd.merge(table1,\n",
    "        table2,\n",
    "        how = 'outer',\n",
    "        on = 'P_ID')\n",
    "oj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "243cafe6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe thead th {\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <th>1</th>\n",
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       "      <td>male</td>\n",
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       "      <td>225</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   P_ID gender  height  weight\n",
       "0     1   male      71     175\n",
       "1     2   male      73     225"
      ]
     },
     "execution_count": 13,
     "metadata": {},
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   ],
   "source": [
    "table1.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "254561d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>male</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   P_ID   sex  visits  checkup  follow_up  illness  surgery  ER\n",
       "0     1  male       1        1          0        0        0   0\n",
       "1     2  male       2        1          0        0        0   1"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table2.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac307f6a",
   "metadata": {},
   "source": [
    "By looking at the table  we can say that gender in firts table has gender column and 2nd table has sex column, and both the column represent the same information means they are also common"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9c465081",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>P_ID</th>\n",
       "      <th>sex</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>visits</th>\n",
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       "      <th>follow_up</th>\n",
       "      <th>illness</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>71.0</td>\n",
       "      <td>175.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>225.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>64.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>female</td>\n",
       "      <td>64.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>female</td>\n",
       "      <td>66.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>12.0</td>\n",
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       "      <td>7.0</td>\n",
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       "      <td>69.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>female</td>\n",
       "      <td>62.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>male</td>\n",
       "      <td>72.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   P_ID     sex  height  weight  visits  checkup  follow_up  illness  surgery  \\\n",
       "0     1    male    71.0   175.0     1.0      1.0        0.0      0.0      0.0   \n",
       "1     2    male    73.0   225.0     2.0      1.0        0.0      0.0      0.0   \n",
       "2     3  female    64.0   130.0     NaN      NaN        NaN      NaN      NaN   \n",
       "3     4  female    64.0   125.0     4.0      1.0        1.0      2.0      0.0   \n",
       "4     5  female    66.0   165.0    12.0      1.0        2.0      7.0      2.0   \n",
       "5     6    male    69.0   160.0     NaN      NaN        NaN      NaN      NaN   \n",
       "6     7  female    62.0   115.0     2.0      1.0        0.0      1.0      0.0   \n",
       "7     8    male    72.0   250.0     2.0      1.0        0.0      1.0      0.0   \n",
       "8     9    male     NaN     NaN     1.0      0.0        0.0      0.0      0.0   \n",
       "9    10  female     NaN     NaN     1.0      0.0        0.0      0.0      0.0   \n",
       "\n",
       "    ER  \n",
       "0  0.0  \n",
       "1  1.0  \n",
       "2  NaN  \n",
       "3  0.0  \n",
       "4  0.0  \n",
       "5  NaN  \n",
       "6  0.0  \n",
       "7  0.0  \n",
       "8  1.0  \n",
       "9  1.0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Renaming the gender column of first table as sex \n",
    "\n",
    "table1.rename(columns = {'gender' : 'sex'},inplace = True)\n",
    "pd.merge(table1,\n",
    "        table2,\n",
    "        how = 'outer',\n",
    "        on = ['P_ID','sex'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "657344b1",
   "metadata": {},
   "source": [
    "* The pandas function merge() can perform common joins to combine data frames with matching columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "06121ee0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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