[4]Merging, Joining & Concatenating.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "courses = pd.read_csv('courses.csv')\n",
    "students = pd.read_csv('students.csv')\n",
    "nov = pd.read_csv('reg-month1.csv')\n",
    "dec = pd.read_csv('reg-month2.csv')\n",
    "\n",
    "matches = pd.read_csv('matches.csv')\n",
    "delivery = pd.read_csv('deliveries.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28, 2)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dec.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Concatenating"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### pd.concat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(25, 2)\n",
      "(28, 2)\n"
     ]
    }
   ],
   "source": [
    "print(nov.shape)\n",
    "print(dec.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
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       "      <th></th>\n",
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      "text/plain": [
       "    student_id  course_id\n",
       "0           23          1\n",
       "1           15          5\n",
       "2           18          6\n",
       "3           23          4\n",
       "4           16          9\n",
       "5           18          1\n",
       "6            1          1\n",
       "7            7          8\n",
       "8           22          3\n",
       "9           15          1\n",
       "10          19          4\n",
       "11           1          6\n",
       "12           7         10\n",
       "13          11          7\n",
       "14          13          3\n",
       "15          24          4\n",
       "16          21          1\n",
       "17          16          5\n",
       "18          23          3\n",
       "19          17          7\n",
       "20          23          6\n",
       "21          25          1\n",
       "22          19          2\n",
       "23          25         10\n",
       "24           3          3\n",
       "0            3          5\n",
       "1           16          7\n",
       "2           12         10\n",
       "3           12          1\n",
       "4           14          9\n",
       "5            7          7\n",
       "6            7          2\n",
       "7           16          3\n",
       "8           17         10\n",
       "9           11          8\n",
       "10          14          6\n",
       "11          12          5\n",
       "12          12          7\n",
       "13          18          8\n",
       "14           1         10\n",
       "15           1          9\n",
       "16           2          5\n",
       "17           7          6\n",
       "18          22          5\n",
       "19          22          6\n",
       "20          23          9\n",
       "21          23          5\n",
       "22          14          4\n",
       "23          14          1\n",
       "24          11         10\n",
       "25          42          9\n",
       "26          50          8\n",
       "27          38          1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regs = pd.concat([nov,dec]) #merge vertically...\n",
    "regs                     #total reg in 2 months"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(53, 2)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regs.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>22</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>22</td>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>23</td>\n",
       "      <td>9</td>\n",
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       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>23</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
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       "      <th>50</th>\n",
       "      <td>42</td>\n",
       "      <td>9</td>\n",
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       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
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       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    student_id  course_id\n",
       "0           23          1\n",
       "1           15          5\n",
       "2           18          6\n",
       "3           23          4\n",
       "4           16          9\n",
       "5           18          1\n",
       "6            1          1\n",
       "7            7          8\n",
       "8           22          3\n",
       "9           15          1\n",
       "10          19          4\n",
       "11           1          6\n",
       "12           7         10\n",
       "13          11          7\n",
       "14          13          3\n",
       "15          24          4\n",
       "16          21          1\n",
       "17          16          5\n",
       "18          23          3\n",
       "19          17          7\n",
       "20          23          6\n",
       "21          25          1\n",
       "22          19          2\n",
       "23          25         10\n",
       "24           3          3\n",
       "25           3          5\n",
       "26          16          7\n",
       "27          12         10\n",
       "28          12          1\n",
       "29          14          9\n",
       "30           7          7\n",
       "31           7          2\n",
       "32          16          3\n",
       "33          17         10\n",
       "34          11          8\n",
       "35          14          6\n",
       "36          12          5\n",
       "37          12          7\n",
       "38          18          8\n",
       "39           1         10\n",
       "40           1          9\n",
       "41           2          5\n",
       "42           7          6\n",
       "43          22          5\n",
       "44          22          6\n",
       "45          23          9\n",
       "46          23          5\n",
       "47          14          4\n",
       "48          14          1\n",
       "49          11         10\n",
       "50          42          9\n",
       "51          50          8\n",
       "52          38          1"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#here index is retained. i.e index is repeating.. to avoid it,\n",
    "pd.concat([nov,dec],ignore_index=True)               #new index is formed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nithin\\AppData\\Local\\Temp\\ipykernel_11488\\1613214451.py:2: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  nov.append(dec)\n",
      "C:\\Users\\Nithin\\AppData\\Local\\Temp\\ipykernel_11488\\1613214451.py:3: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  nov.append(dec,ignore_index=True)\n"
     ]
    },
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      "text/plain": [
       "    student_id  course_id\n",
       "0           23          1\n",
       "1           15          5\n",
       "2           18          6\n",
       "3           23          4\n",
       "4           16          9\n",
       "5           18          1\n",
       "6            1          1\n",
       "7            7          8\n",
       "8           22          3\n",
       "9           15          1\n",
       "10          19          4\n",
       "11           1          6\n",
       "12           7         10\n",
       "13          11          7\n",
       "14          13          3\n",
       "15          24          4\n",
       "16          21          1\n",
       "17          16          5\n",
       "18          23          3\n",
       "19          17          7\n",
       "20          23          6\n",
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       "23          25         10\n",
       "24           3          3\n",
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       "50          42          9\n",
       "51          50          8\n",
       "52          38          1"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#append                                             #concat is prefered\n",
    "nov.append(dec)\n",
    "nov.append(dec,ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### multi_index dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>17</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>18</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>22</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>22</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>23</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>23</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>42</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        student_id  course_id\n",
       "Nov 0           23          1\n",
       "    1           15          5\n",
       "    2           18          6\n",
       "    3           23          4\n",
       "    4           16          9\n",
       "    5           18          1\n",
       "    6            1          1\n",
       "    7            7          8\n",
       "    8           22          3\n",
       "    9           15          1\n",
       "    10          19          4\n",
       "    11           1          6\n",
       "    12           7         10\n",
       "    13          11          7\n",
       "    14          13          3\n",
       "    15          24          4\n",
       "    16          21          1\n",
       "    17          16          5\n",
       "    18          23          3\n",
       "    19          17          7\n",
       "    20          23          6\n",
       "    21          25          1\n",
       "    22          19          2\n",
       "    23          25         10\n",
       "    24           3          3\n",
       "Dec 0            3          5\n",
       "    1           16          7\n",
       "    2           12         10\n",
       "    3           12          1\n",
       "    4           14          9\n",
       "    5            7          7\n",
       "    6            7          2\n",
       "    7           16          3\n",
       "    8           17         10\n",
       "    9           11          8\n",
       "    10          14          6\n",
       "    11          12          5\n",
       "    12          12          7\n",
       "    13          18          8\n",
       "    14           1         10\n",
       "    15           1          9\n",
       "    16           2          5\n",
       "    17           7          6\n",
       "    18          22          5\n",
       "    19          22          6\n",
       "    20          23          9\n",
       "    21          23          5\n",
       "    22          14          4\n",
       "    23          14          1\n",
       "    24          11         10\n",
       "    25          42          9\n",
       "    26          50          8\n",
       "    27          38          1"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#if u want to retain old index, and still want to differentiate between repeated index,use parameter called 'keys' and give a key value for each dataframe.\n",
    "multi = pd.concat([nov,dec],keys=['Nov','Dec'])\n",
    "multi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "student_id    23\n",
       "course_id      1\n",
       "Name: (Nov, 0), dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multi.loc['Nov']\n",
    "multi.loc['Dec']\n",
    "multi.loc['Nov',0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "student_id    7\n",
       "course_id     2\n",
       "Name: (Dec, 6), dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multi.loc['Dec',6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>student_id</th>\n",
       "      <th>course_id</th>\n",
       "      <th>student_id</th>\n",
       "      <th>course_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23.0</td>\n",
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       "      <td>3</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
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       "      <td>15.0</td>\n",
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       "      <td>16</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16.0</td>\n",
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       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>18.0</td>\n",
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       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>22.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>17</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>19.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>11.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>18</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>24.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>21.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>16.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>23.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>22</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>17.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>22</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>23.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>23</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>25.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>23</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>19.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>25.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    student_id  course_id  student_id  course_id\n",
       "0         23.0        1.0           3          5\n",
       "1         15.0        5.0          16          7\n",
       "2         18.0        6.0          12         10\n",
       "3         23.0        4.0          12          1\n",
       "4         16.0        9.0          14          9\n",
       "5         18.0        1.0           7          7\n",
       "6          1.0        1.0           7          2\n",
       "7          7.0        8.0          16          3\n",
       "8         22.0        3.0          17         10\n",
       "9         15.0        1.0          11          8\n",
       "10        19.0        4.0          14          6\n",
       "11         1.0        6.0          12          5\n",
       "12         7.0       10.0          12          7\n",
       "13        11.0        7.0          18          8\n",
       "14        13.0        3.0           1         10\n",
       "15        24.0        4.0           1          9\n",
       "16        21.0        1.0           2          5\n",
       "17        16.0        5.0           7          6\n",
       "18        23.0        3.0          22          5\n",
       "19        17.0        7.0          22          6\n",
       "20        23.0        6.0          23          9\n",
       "21        25.0        1.0          23          5\n",
       "22        19.0        2.0          14          4\n",
       "23        25.0       10.0          14          1\n",
       "24         3.0        3.0          11         10\n",
       "25         NaN        NaN          42          9\n",
       "26         NaN        NaN          50          8\n",
       "27         NaN        NaN          38          1"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#merging horizontally                          .............less use cases\n",
    "pd.concat([nov,dec],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28, 4)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([nov,dec],axis=1).shape        #not necessary that no.of columns should be equal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Merging"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# 'Concat' function concatenates dataframes along rows or columns. We can think of it as stacking up multiple dataframes. \n",
    "# 'Merge' combines dataframes based on values in shared columns. Merge function offers more flexibility compared to concat   function because it allows combinations based on a condition.\n",
    "\n",
    "# concat() simply stacks multiple DataFrame together either vertically, or stitches horizontally after aligning on index.\n",
    "# merge() first aligns two DataFrame ' selected common column(s) or index, and then pick up the remaining columns from the aligned rows of each DataFrame ."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "4 types of Join:-\n",
    "    Inner,Left,Right,Outer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'reg' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[19], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m regs\n\u001b[1;32m----> 2\u001b[0m reg\u001b[38;5;241m.\u001b[39mshape\n\u001b[0;32m      3\u001b[0m students\u001b[38;5;241m.\u001b[39mshape\n",
      "\u001b[1;31mNameError\u001b[0m: name 'reg' is not defined"
     ]
    }
   ],
   "source": [
    "regs\n",
    "reg.shape\n",
    "students.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'reg' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[20], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m reg\n",
      "\u001b[1;31mNameError\u001b[0m: name 'reg' is not defined"
     ]
    }
   ],
   "source": [
    "reg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### inner join"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>student_id</th>\n",
       "      <th>name</th>\n",
       "      <th>partner</th>\n",
       "      <th>course_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Kailash Harjo</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Kailash Harjo</td>\n",
       "      <td>23</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Kailash Harjo</td>\n",
       "      <td>23</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Kailash Harjo</td>\n",
       "      <td>23</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>Esha Butala</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>Parveen Bhalla</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>Parveen Bhalla</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>Tarun Thaker</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7</td>\n",
       "      <td>Tarun Thaker</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7</td>\n",
       "      <td>Tarun Thaker</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>7</td>\n",
       "      <td>Tarun Thaker</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>7</td>\n",
       "      <td>Tarun Thaker</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>11</td>\n",
       "      <td>David Mukhopadhyay</td>\n",
       "      <td>20</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>11</td>\n",
       "      <td>David Mukhopadhyay</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>11</td>\n",
       "      <td>David Mukhopadhyay</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>12</td>\n",
       "      <td>Radha Dutt</td>\n",
       "      <td>19</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>12</td>\n",
       "      <td>Radha Dutt</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>12</td>\n",
       "      <td>Radha Dutt</td>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>12</td>\n",
       "      <td>Radha Dutt</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>13</td>\n",
       "      <td>Munni Varghese</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>14</td>\n",
       "      <td>Pranab Natarajan</td>\n",
       "      <td>22</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>14</td>\n",
       "      <td>Pranab Natarajan</td>\n",
       "      <td>22</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>14</td>\n",
       "      <td>Pranab Natarajan</td>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>14</td>\n",
       "      <td>Pranab Natarajan</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>15</td>\n",
       "      <td>Preet Sha</td>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>15</td>\n",
       "      <td>Preet Sha</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>16</td>\n",
       "      <td>Elias Dodiya</td>\n",
       "      <td>25</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>16</td>\n",
       "      <td>Elias Dodiya</td>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>16</td>\n",
       "      <td>Elias Dodiya</td>\n",
       "      <td>25</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>16</td>\n",
       "      <td>Elias Dodiya</td>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>17</td>\n",
       "      <td>Yasmin Palan</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>17</td>\n",
       "      <td>Yasmin Palan</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>18</td>\n",
       "      <td>Fardeen Mahabir</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>18</td>\n",
       "      <td>Fardeen Mahabir</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>18</td>\n",
       "      <td>Fardeen Mahabir</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>19</td>\n",
       "      <td>Qabeel Raman</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>19</td>\n",
       "      <td>Qabeel Raman</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>21</td>\n",
       "      <td>Seema Kota</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>22</td>\n",
       "      <td>Yash Sethi</td>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>22</td>\n",
       "      <td>Yash Sethi</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>22</td>\n",
       "      <td>Yash Sethi</td>\n",
       "      <td>21</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>23</td>\n",
       "      <td>Chhavi Lachman</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>23</td>\n",
       "      <td>Chhavi Lachman</td>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>23</td>\n",
       "      <td>Chhavi Lachman</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>23</td>\n",
       "      <td>Chhavi Lachman</td>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>23</td>\n",
       "      <td>Chhavi Lachman</td>\n",
       "      <td>18</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>23</td>\n",
       "      <td>Chhavi Lachman</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>24</td>\n",
       "      <td>Radhika Suri</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>25</td>\n",
       "      <td>Shashank D’Alia</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>25</td>\n",
       "      <td>Shashank D’Alia</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    student_id                name  partner  course_id\n",
       "0            1       Kailash Harjo       23          1\n",
       "1            1       Kailash Harjo       23          6\n",
       "2            1       Kailash Harjo       23         10\n",
       "3            1       Kailash Harjo       23          9\n",
       "4            2         Esha Butala        1          5\n",
       "5            3      Parveen Bhalla        3          3\n",
       "6            3      Parveen Bhalla        3          5\n",
       "7            7        Tarun Thaker        9          8\n",
       "8            7        Tarun Thaker        9         10\n",
       "9            7        Tarun Thaker        9          7\n",
       "10           7        Tarun Thaker        9          2\n",
       "11           7        Tarun Thaker        9          6\n",
       "12          11  David Mukhopadhyay       20          7\n",
       "13          11  David Mukhopadhyay       20          8\n",
       "14          11  David Mukhopadhyay       20         10\n",
       "15          12          Radha Dutt       19         10\n",
       "16          12          Radha Dutt       19          1\n",
       "17          12          Radha Dutt       19          5\n",
       "18          12          Radha Dutt       19          7\n",
       "19          13      Munni Varghese       24          3\n",
       "20          14    Pranab Natarajan       22          9\n",
       "21          14    Pranab Natarajan       22          6\n",
       "22          14    Pranab Natarajan       22          4\n",
       "23          14    Pranab Natarajan       22          1\n",
       "24          15           Preet Sha       16          5\n",
       "25          15           Preet Sha       16          1\n",
       "26          16        Elias Dodiya       25          9\n",
       "27          16        Elias Dodiya       25          5\n",
       "28          16        Elias Dodiya       25          7\n",
       "29          16        Elias Dodiya       25          3\n",
       "30          17        Yasmin Palan        7          7\n",
       "31          17        Yasmin Palan        7         10\n",
       "32          18     Fardeen Mahabir       13          6\n",
       "33          18     Fardeen Mahabir       13          1\n",
       "34          18     Fardeen Mahabir       13          8\n",
       "35          19        Qabeel Raman       12          4\n",
       "36          19        Qabeel Raman       12          2\n",
       "37          21          Seema Kota       15          1\n",
       "38          22          Yash Sethi       21          3\n",
       "39          22          Yash Sethi       21          5\n",
       "40          22          Yash Sethi       21          6\n",
       "41          23      Chhavi Lachman       18          1\n",
       "42          23      Chhavi Lachman       18          4\n",
       "43          23      Chhavi Lachman       18          3\n",
       "44          23      Chhavi Lachman       18          6\n",
       "45          23      Chhavi Lachman       18          9\n",
       "46          23      Chhavi Lachman       18          5\n",
       "47          24        Radhika Suri       17          4\n",
       "48          25     Shashank D’Alia        2          1\n",
       "49          25     Shashank D’Alia        2         10"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "students.merge(regs,how='inner',on='student_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>9999</td>\n",
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       "      <td>2499</td>\n",
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       "      <th>22</th>\n",
       "      <td>5</td>\n",
       "      <td>tableau</td>\n",
       "      <td>2499</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5</td>\n",
       "      <td>tableau</td>\n",
       "      <td>2499</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>5</td>\n",
       "      <td>tableau</td>\n",
       "      <td>2499</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>5</td>\n",
       "      <td>tableau</td>\n",
       "      <td>2499</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>5</td>\n",
       "      <td>tableau</td>\n",
       "      <td>2499</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>6</td>\n",
       "      <td>power bi</td>\n",
       "      <td>1899</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>6</td>\n",
       "      <td>power bi</td>\n",
       "      <td>1899</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>6</td>\n",
       "      <td>power bi</td>\n",
       "      <td>1899</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>6</td>\n",
       "      <td>power bi</td>\n",
       "      <td>1899</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>6</td>\n",
       "      <td>power bi</td>\n",
       "      <td>1899</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>6</td>\n",
       "      <td>power bi</td>\n",
       "      <td>1899</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>7</td>\n",
       "      <td>ms sxcel</td>\n",
       "      <td>1599</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>7</td>\n",
       "      <td>ms sxcel</td>\n",
       "      <td>1599</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>7</td>\n",
       "      <td>ms sxcel</td>\n",
       "      <td>1599</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>7</td>\n",
       "      <td>ms sxcel</td>\n",
       "      <td>1599</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>7</td>\n",
       "      <td>ms sxcel</td>\n",
       "      <td>1599</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>8</td>\n",
       "      <td>pandas</td>\n",
       "      <td>1099</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>8</td>\n",
       "      <td>pandas</td>\n",
       "      <td>1099</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>8</td>\n",
       "      <td>pandas</td>\n",
       "      <td>1099</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>8</td>\n",
       "      <td>pandas</td>\n",
       "      <td>1099</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>9</td>\n",
       "      <td>plotly</td>\n",
       "      <td>699</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>9</td>\n",
       "      <td>plotly</td>\n",
       "      <td>699</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>9</td>\n",
       "      <td>plotly</td>\n",
       "      <td>699</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>9</td>\n",
       "      <td>plotly</td>\n",
       "      <td>699</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>9</td>\n",
       "      <td>plotly</td>\n",
       "      <td>699</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>10</td>\n",
       "      <td>pyspark</td>\n",
       "      <td>2499</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>10</td>\n",
       "      <td>pyspark</td>\n",
       "      <td>2499</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>10</td>\n",
       "      <td>pyspark</td>\n",
       "      <td>2499</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>10</td>\n",
       "      <td>pyspark</td>\n",
       "      <td>2499</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>10</td>\n",
       "      <td>pyspark</td>\n",
       "      <td>2499</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>10</td>\n",
       "      <td>pyspark</td>\n",
       "      <td>2499</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>11</td>\n",
       "      <td>Numpy</td>\n",
       "      <td>699</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>12</td>\n",
       "      <td>C++</td>\n",
       "      <td>1299</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    course_id       course_name  price  student_id\n",
       "0           1            python   2499        23.0\n",
       "1           1            python   2499        18.0\n",
       "2           1            python   2499         1.0\n",
       "3           1            python   2499        15.0\n",
       "4           1            python   2499        21.0\n",
       "5           1            python   2499        25.0\n",
       "6           1            python   2499        12.0\n",
       "7           1            python   2499        14.0\n",
       "8           1            python   2499        38.0\n",
       "9           2               sql   3499        19.0\n",
       "10          2               sql   3499         7.0\n",
       "11          3     data analysis   4999        22.0\n",
       "12          3     data analysis   4999        13.0\n",
       "13          3     data analysis   4999        23.0\n",
       "14          3     data analysis   4999         3.0\n",
       "15          3     data analysis   4999        16.0\n",
       "16          4  machine learning   9999        23.0\n",
       "17          4  machine learning   9999        19.0\n",
       "18          4  machine learning   9999        24.0\n",
       "19          4  machine learning   9999        14.0\n",
       "20          5           tableau   2499        15.0\n",
       "21          5           tableau   2499        16.0\n",
       "22          5           tableau   2499         3.0\n",
       "23          5           tableau   2499        12.0\n",
       "24          5           tableau   2499         2.0\n",
       "25          5           tableau   2499        22.0\n",
       "26          5           tableau   2499        23.0\n",
       "27          6          power bi   1899        18.0\n",
       "28          6          power bi   1899         1.0\n",
       "29          6          power bi   1899        23.0\n",
       "30          6          power bi   1899        14.0\n",
       "31          6          power bi   1899         7.0\n",
       "32          6          power bi   1899        22.0\n",
       "33          7          ms sxcel   1599        11.0\n",
       "34          7          ms sxcel   1599        17.0\n",
       "35          7          ms sxcel   1599        16.0\n",
       "36          7          ms sxcel   1599         7.0\n",
       "37          7          ms sxcel   1599        12.0\n",
       "38          8            pandas   1099         7.0\n",
       "39          8            pandas   1099        11.0\n",
       "40          8            pandas   1099        18.0\n",
       "41          8            pandas   1099        50.0\n",
       "42          9            plotly    699        16.0\n",
       "43          9            plotly    699        14.0\n",
       "44          9            plotly    699         1.0\n",
       "45          9            plotly    699        23.0\n",
       "46          9            plotly    699        42.0\n",
       "47         10           pyspark   2499         7.0\n",
       "48         10           pyspark   2499        25.0\n",
       "49         10           pyspark   2499        12.0\n",
       "50         10           pyspark   2499        17.0\n",
       "51         10           pyspark   2499         1.0\n",
       "52         10           pyspark   2499        11.0\n",
       "53         11             Numpy    699         NaN\n",
       "54         12               C++   1299         NaN"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#left join\n",
    "courses.merge(regs,how='left',on='course_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>student_id</th>\n",
       "      <th>name</th>\n",
       "      <th>partner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Kailash Harjo</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Esha Butala</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Parveen Bhalla</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Marlo Dugal</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Kusum Bahri</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Lakshmi Contractor</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Tarun Thaker</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Radheshyam Dey</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Nitika Chatterjee</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>Aayushman Sant</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>David Mukhopadhyay</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>Radha Dutt</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>Munni Varghese</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>Pranab Natarajan</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>Preet Sha</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>Elias Dodiya</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>Yasmin Palan</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>Fardeen Mahabir</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>Qabeel Raman</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>Hanuman Hegde</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>Seema Kota</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>Yash Sethi</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>Chhavi Lachman</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>Radhika Suri</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>Shashank D’Alia</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>Nitish</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>Ankit</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>Rahul</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    student_id                name  partner\n",
       "0            1       Kailash Harjo       23\n",
       "1            2         Esha Butala        1\n",
       "2            3      Parveen Bhalla        3\n",
       "3            4         Marlo Dugal       14\n",
       "4            5         Kusum Bahri        6\n",
       "5            6  Lakshmi Contractor       10\n",
       "6            7        Tarun Thaker        9\n",
       "7            8      Radheshyam Dey        5\n",
       "8            9   Nitika Chatterjee        4\n",
       "9           10      Aayushman Sant        8\n",
       "10          11  David Mukhopadhyay       20\n",
       "11          12          Radha Dutt       19\n",
       "12          13      Munni Varghese       24\n",
       "13          14    Pranab Natarajan       22\n",
       "14          15           Preet Sha       16\n",
       "15          16        Elias Dodiya       25\n",
       "16          17        Yasmin Palan        7\n",
       "17          18     Fardeen Mahabir       13\n",
       "18          19        Qabeel Raman       12\n",
       "19          20       Hanuman Hegde       11\n",
       "20          21          Seema Kota       15\n",
       "21          22          Yash Sethi       21\n",
       "22          23      Chhavi Lachman       18\n",
       "23          24        Radhika Suri       17\n",
       "24          25     Shashank D’Alia        2\n",
       "25          26              Nitish       28\n",
       "26          27               Ankit       26\n",
       "27          28               Rahul       17"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#right join\n",
    "temp_df = pd.DataFrame({\n",
    "    'student_id':[26,27,28],\n",
    "    'name':['Nitish','Ankit','Rahul'],\n",
    "    'partner':[28,26,17]\n",
    "})\n",
    "\n",
    "students = pd.concat([students,temp_df],ignore_index=True)\n",
    "students"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>student_id</th>\n",
       "      <th>name</th>\n",
       "      <th>partner</th>\n",
       "      <th>course_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23</td>\n",
       "      <td>Chhavi Lachman</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15</td>\n",
       "      <td>Preet Sha</td>\n",
       "      <td>16.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>Fardeen Mahabir</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23</td>\n",
       "      <td>Chhavi Lachman</td>\n",
       "      <td>18.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16</td>\n",
       "      <td>Elias Dodiya</td>\n",
       "      <td>25.0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>18</td>\n",
       "      <td>Fardeen Mahabir</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>Kailash Harjo</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>Tarun Thaker</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>22</td>\n",
       "      <td>Yash Sethi</td>\n",
       "      <td>21.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>15</td>\n",
       "      <td>Preet Sha</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>19</td>\n",
       "      <td>Qabeel Raman</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>Kailash Harjo</td>\n",
       "      <td>23.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>7</td>\n",
       "      <td>Tarun Thaker</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>11</td>\n",
       "      <td>David Mukhopadhyay</td>\n",
       "      <td>20.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>13</td>\n",
       "      <td>Munni Varghese</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>24</td>\n",
       "      <td>Radhika Suri</td>\n",
       "      <td>17.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>21</td>\n",
       "      <td>Seema Kota</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>16</td>\n",
       "      <td>Elias Dodiya</td>\n",
       "      <td>25.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>23</td>\n",
       "      <td>Chhavi Lachman</td>\n",
       "      <td>18.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>17</td>\n",
       "      <td>Yasmin Palan</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>23</td>\n",
       "      <td>Chhavi Lachman</td>\n",
       "      <td>18.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>25</td>\n",
       "      <td>Shashank D’Alia</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>19</td>\n",
       "      <td>Qabeel Raman</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>25</td>\n",
       "      <td>Shashank D’Alia</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>3</td>\n",
       "      <td>Parveen Bhalla</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3</td>\n",
       "      <td>Parveen Bhalla</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>16</td>\n",
       "      <td>Elias Dodiya</td>\n",
       "      <td>25.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>12</td>\n",
       "      <td>Radha Dutt</td>\n",
       "      <td>19.0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>12</td>\n",
       "      <td>Radha Dutt</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>14</td>\n",
       "      <td>Pranab Natarajan</td>\n",
       "      <td>22.0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>7</td>\n",
       "      <td>Tarun Thaker</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>7</td>\n",
       "      <td>Tarun Thaker</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>16</td>\n",
       "      <td>Elias Dodiya</td>\n",
       "      <td>25.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>17</td>\n",
       "      <td>Yasmin Palan</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>11</td>\n",
       "      <td>David Mukhopadhyay</td>\n",
       "      <td>20.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>14</td>\n",
       "      <td>Pranab Natarajan</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>12</td>\n",
       "      <td>Radha Dutt</td>\n",
       "      <td>19.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>12</td>\n",
       "      <td>Radha Dutt</td>\n",
       "      <td>19.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>18</td>\n",
       "      <td>Fardeen Mahabir</td>\n",
       "      <td>13.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>1</td>\n",
       "      <td>Kailash Harjo</td>\n",
       "      <td>23.0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>1</td>\n",
       "      <td>Kailash Harjo</td>\n",
       "      <td>23.0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2</td>\n",
       "      <td>Esha Butala</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>7</td>\n",
       "      <td>Tarun Thaker</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>22</td>\n",
       "      <td>Yash Sethi</td>\n",
       "      <td>21.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>22</td>\n",
       "      <td>Yash Sethi</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>23</td>\n",
       "      <td>Chhavi Lachman</td>\n",
       "      <td>18.0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>23</td>\n",
       "      <td>Chhavi Lachman</td>\n",
       "      <td>18.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>14</td>\n",
       "      <td>Pranab Natarajan</td>\n",
       "      <td>22.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>14</td>\n",
       "      <td>Pranab Natarajan</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>11</td>\n",
       "      <td>David Mukhopadhyay</td>\n",
       "      <td>20.0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>42</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    student_id                name  partner  course_id\n",
       "0           23      Chhavi Lachman     18.0          1\n",
       "1           15           Preet Sha     16.0          5\n",
       "2           18     Fardeen Mahabir     13.0          6\n",
       "3           23      Chhavi Lachman     18.0          4\n",
       "4           16        Elias Dodiya     25.0          9\n",
       "5           18     Fardeen Mahabir     13.0          1\n",
       "6            1       Kailash Harjo     23.0          1\n",
       "7            7        Tarun Thaker      9.0          8\n",
       "8           22          Yash Sethi     21.0          3\n",
       "9           15           Preet Sha     16.0          1\n",
       "10          19        Qabeel Raman     12.0          4\n",
       "11           1       Kailash Harjo     23.0          6\n",
       "12           7        Tarun Thaker      9.0         10\n",
       "13          11  David Mukhopadhyay     20.0          7\n",
       "14          13      Munni Varghese     24.0          3\n",
       "15          24        Radhika Suri     17.0          4\n",
       "16          21          Seema Kota     15.0          1\n",
       "17          16        Elias Dodiya     25.0          5\n",
       "18          23      Chhavi Lachman     18.0          3\n",
       "19          17        Yasmin Palan      7.0          7\n",
       "20          23      Chhavi Lachman     18.0          6\n",
       "21          25     Shashank D’Alia      2.0          1\n",
       "22          19        Qabeel Raman     12.0          2\n",
       "23          25     Shashank D’Alia      2.0         10\n",
       "24           3      Parveen Bhalla      3.0          3\n",
       "25           3      Parveen Bhalla      3.0          5\n",
       "26          16        Elias Dodiya     25.0          7\n",
       "27          12          Radha Dutt     19.0         10\n",
       "28          12          Radha Dutt     19.0          1\n",
       "29          14    Pranab Natarajan     22.0          9\n",
       "30           7        Tarun Thaker      9.0          7\n",
       "31           7        Tarun Thaker      9.0          2\n",
       "32          16        Elias Dodiya     25.0          3\n",
       "33          17        Yasmin Palan      7.0         10\n",
       "34          11  David Mukhopadhyay     20.0          8\n",
       "35          14    Pranab Natarajan     22.0          6\n",
       "36          12          Radha Dutt     19.0          5\n",
       "37          12          Radha Dutt     19.0          7\n",
       "38          18     Fardeen Mahabir     13.0          8\n",
       "39           1       Kailash Harjo     23.0         10\n",
       "40           1       Kailash Harjo     23.0          9\n",
       "41           2         Esha Butala      1.0          5\n",
       "42           7        Tarun Thaker      9.0          6\n",
       "43          22          Yash Sethi     21.0          5\n",
       "44          22          Yash Sethi     21.0          6\n",
       "45          23      Chhavi Lachman     18.0          9\n",
       "46          23      Chhavi Lachman     18.0          5\n",
       "47          14    Pranab Natarajan     22.0          4\n",
       "48          14    Pranab Natarajan     22.0          1\n",
       "49          11  David Mukhopadhyay     20.0         10\n",
       "50          42                 NaN      NaN          9\n",
       "51          50                 NaN      NaN          8\n",
       "52          38                 NaN      NaN          1"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "students.merge(regs,how='right',on='student_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>student_id</th>\n",
       "      <th>course_id</th>\n",
       "      <th>name</th>\n",
       "      <th>partner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>Chhavi Lachman</td>\n",
       "      <td>18.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>Preet Sha</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "      <td>Fardeen Mahabir</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23</td>\n",
       "      <td>4</td>\n",
       "      <td>Chhavi Lachman</td>\n",
       "      <td>18.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16</td>\n",
       "      <td>9</td>\n",
       "      <td>Elias Dodiya</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>Fardeen Mahabir</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Kailash Harjo</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>Tarun Thaker</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "      <td>Yash Sethi</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>Preet Sha</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>Qabeel Raman</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>Kailash Harjo</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>Tarun Thaker</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>David Mukhopadhyay</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>Munni Varghese</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>24</td>\n",
       "      <td>4</td>\n",
       "      <td>Radhika Suri</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>Seema Kota</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "      <td>Elias Dodiya</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>Chhavi Lachman</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>17</td>\n",
       "      <td>7</td>\n",
       "      <td>Yasmin Palan</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>23</td>\n",
       "      <td>6</td>\n",
       "      <td>Chhavi Lachman</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>Shashank D’Alia</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>Qabeel Raman</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>25</td>\n",
       "      <td>10</td>\n",
       "      <td>Shashank D’Alia</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Parveen Bhalla</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>Parveen Bhalla</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>16</td>\n",
       "      <td>7</td>\n",
       "      <td>Elias Dodiya</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Radha Dutt</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>Radha Dutt</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>Pranab Natarajan</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>Tarun Thaker</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>Tarun Thaker</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>Elias Dodiya</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>17</td>\n",
       "      <td>10</td>\n",
       "      <td>Yasmin Palan</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>David Mukhopadhyay</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>Pranab Natarajan</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>Radha Dutt</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>Radha Dutt</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>18</td>\n",
       "      <td>8</td>\n",
       "      <td>Fardeen Mahabir</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>Kailash Harjo</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>Kailash Harjo</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>Esha Butala</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>Tarun Thaker</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>22</td>\n",
       "      <td>5</td>\n",
       "      <td>Yash Sethi</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>22</td>\n",
       "      <td>6</td>\n",
       "      <td>Yash Sethi</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>23</td>\n",
       "      <td>9</td>\n",
       "      <td>Chhavi Lachman</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>23</td>\n",
       "      <td>5</td>\n",
       "      <td>Chhavi Lachman</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>Pranab Natarajan</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>Pranab Natarajan</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>David Mukhopadhyay</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>42</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    student_id  course_id                name  partner\n",
       "0           23          1      Chhavi Lachman     18.0\n",
       "1           15          5           Preet Sha     16.0\n",
       "2           18          6     Fardeen Mahabir     13.0\n",
       "3           23          4      Chhavi Lachman     18.0\n",
       "4           16          9        Elias Dodiya     25.0\n",
       "5           18          1     Fardeen Mahabir     13.0\n",
       "6            1          1       Kailash Harjo     23.0\n",
       "7            7          8        Tarun Thaker      9.0\n",
       "8           22          3          Yash Sethi     21.0\n",
       "9           15          1           Preet Sha     16.0\n",
       "10          19          4        Qabeel Raman     12.0\n",
       "11           1          6       Kailash Harjo     23.0\n",
       "12           7         10        Tarun Thaker      9.0\n",
       "13          11          7  David Mukhopadhyay     20.0\n",
       "14          13          3      Munni Varghese     24.0\n",
       "15          24          4        Radhika Suri     17.0\n",
       "16          21          1          Seema Kota     15.0\n",
       "17          16          5        Elias Dodiya     25.0\n",
       "18          23          3      Chhavi Lachman     18.0\n",
       "19          17          7        Yasmin Palan      7.0\n",
       "20          23          6      Chhavi Lachman     18.0\n",
       "21          25          1     Shashank D’Alia      2.0\n",
       "22          19          2        Qabeel Raman     12.0\n",
       "23          25         10     Shashank D’Alia      2.0\n",
       "24           3          3      Parveen Bhalla      3.0\n",
       "25           3          5      Parveen Bhalla      3.0\n",
       "26          16          7        Elias Dodiya     25.0\n",
       "27          12         10          Radha Dutt     19.0\n",
       "28          12          1          Radha Dutt     19.0\n",
       "29          14          9    Pranab Natarajan     22.0\n",
       "30           7          7        Tarun Thaker      9.0\n",
       "31           7          2        Tarun Thaker      9.0\n",
       "32          16          3        Elias Dodiya     25.0\n",
       "33          17         10        Yasmin Palan      7.0\n",
       "34          11          8  David Mukhopadhyay     20.0\n",
       "35          14          6    Pranab Natarajan     22.0\n",
       "36          12          5          Radha Dutt     19.0\n",
       "37          12          7          Radha Dutt     19.0\n",
       "38          18          8     Fardeen Mahabir     13.0\n",
       "39           1         10       Kailash Harjo     23.0\n",
       "40           1          9       Kailash Harjo     23.0\n",
       "41           2          5         Esha Butala      1.0\n",
       "42           7          6        Tarun Thaker      9.0\n",
       "43          22          5          Yash Sethi     21.0\n",
       "44          22          6          Yash Sethi     21.0\n",
       "45          23          9      Chhavi Lachman     18.0\n",
       "46          23          5      Chhavi Lachman     18.0\n",
       "47          14          4    Pranab Natarajan     22.0\n",
       "48          14          1    Pranab Natarajan     22.0\n",
       "49          11         10  David Mukhopadhyay     20.0\n",
       "50          42          9                 NaN      NaN\n",
       "51          50          8                 NaN      NaN\n",
       "52          38          1                 NaN      NaN"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regs.merge(students,how='left',on='student_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>student_id</th>\n",
       "      <th>name</th>\n",
       "      <th>partner</th>\n",
       "      <th>course_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>23</td>\n",
       "      <td>Chhavi Lachman</td>\n",
       "      <td>18.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>24</td>\n",
       "      <td>Radhika Suri</td>\n",
       "      <td>17.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>25</td>\n",
       "      <td>Shashank D’Alia</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>25</td>\n",
       "      <td>Shashank D’Alia</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>26</td>\n",
       "      <td>Nitish</td>\n",
       "      <td>28.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>27</td>\n",
       "      <td>Ankit</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>28</td>\n",
       "      <td>Rahul</td>\n",
       "      <td>17.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>42</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    student_id             name  partner  course_id\n",
       "53          23   Chhavi Lachman     18.0        5.0\n",
       "54          24     Radhika Suri     17.0        4.0\n",
       "55          25  Shashank D’Alia      2.0        1.0\n",
       "56          25  Shashank D’Alia      2.0       10.0\n",
       "57          26           Nitish     28.0        NaN\n",
       "58          27            Ankit     26.0        NaN\n",
       "59          28            Rahul     17.0        NaN\n",
       "60          42              NaN      NaN        9.0\n",
       "61          50              NaN      NaN        8.0\n",
       "62          38              NaN      NaN        1.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# outer join\n",
    "students.merge(regs,how='outer',on='student_id').tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "154247"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. find total revenue generated\n",
    "regs.merge(courses,on='course_id')\n",
    "regs.merge(courses,on='course_id')['price'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "89175"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2. find month by month revenue\n",
    "dec.merge(courses,on='course_id')['price'].sum()\n",
    "nov.merge(courses,on='course_id')['price'].sum()                   #or"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "level_0\n",
       "Dec    65072\n",
       "Nov    89175\n",
       "Name: price, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = pd.concat([nov,dec],keys=['Nov','Dec']).reset_index()\n",
    "temp.merge(courses,on='course_id').groupby('level_0')['price'].sum()"
   ]
  },
  {
   "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>name</th>\n",
       "      <th>course_name</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Chhavi Lachman</td>\n",
       "      <td>python</td>\n",
       "      <td>2499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Preet Sha</td>\n",
       "      <td>python</td>\n",
       "      <td>2499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Fardeen Mahabir</td>\n",
       "      <td>python</td>\n",
       "      <td>2499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kailash Harjo</td>\n",
       "      <td>python</td>\n",
       "      <td>2499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Seema Kota</td>\n",
       "      <td>python</td>\n",
       "      <td>2499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Shashank D’Alia</td>\n",
       "      <td>python</td>\n",
       "      <td>2499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Radha Dutt</td>\n",
       "      <td>python</td>\n",
       "      <td>2499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Pranab Natarajan</td>\n",
       "      <td>python</td>\n",
       "      <td>2499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Chhavi Lachman</td>\n",
       "      <td>machine learning</td>\n",
       "      <td>9999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Qabeel Raman</td>\n",
       "      <td>machine learning</td>\n",
       "      <td>9999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Radhika Suri</td>\n",
       "      <td>machine learning</td>\n",
       "      <td>9999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Pranab Natarajan</td>\n",
       "      <td>machine learning</td>\n",
       "      <td>9999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Chhavi Lachman</td>\n",
       "      <td>data analysis</td>\n",
       "      <td>4999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Elias Dodiya</td>\n",
       "      <td>data analysis</td>\n",
       "      <td>4999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Yash Sethi</td>\n",
       "      <td>data analysis</td>\n",
       "      <td>4999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Munni Varghese</td>\n",
       "      <td>data analysis</td>\n",
       "      <td>4999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Parveen Bhalla</td>\n",
       "      <td>data analysis</td>\n",
       "      <td>4999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Chhavi Lachman</td>\n",
       "      <td>power bi</td>\n",
       "      <td>1899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Fardeen Mahabir</td>\n",
       "      <td>power bi</td>\n",
       "      <td>1899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Kailash Harjo</td>\n",
       "      <td>power bi</td>\n",
       "      <td>1899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Tarun Thaker</td>\n",
       "      <td>power bi</td>\n",
       "      <td>1899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Yash Sethi</td>\n",
       "      <td>power bi</td>\n",
       "      <td>1899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Pranab Natarajan</td>\n",
       "      <td>power bi</td>\n",
       "      <td>1899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Chhavi Lachman</td>\n",
       "      <td>plotly</td>\n",
       "      <td>699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Elias Dodiya</td>\n",
       "      <td>plotly</td>\n",
       "      <td>699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Kailash Harjo</td>\n",
       "      <td>plotly</td>\n",
       "      <td>699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Pranab Natarajan</td>\n",
       "      <td>plotly</td>\n",
       "      <td>699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Chhavi Lachman</td>\n",
       "      <td>tableau</td>\n",
       "      <td>2499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Preet Sha</td>\n",
       "      <td>tableau</td>\n",
       "      <td>2499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Elias Dodiya</td>\n",
       "      <td>tableau</td>\n",
       "      <td>2499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Yash Sethi</td>\n",
       "      <td>tableau</td>\n",
       "      <td>2499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Parveen Bhalla</td>\n",
       "      <td>tableau</td>\n",
       "      <td>2499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Radha Dutt</td>\n",
       "      <td>tableau</td>\n",
       "      <td>2499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Esha Butala</td>\n",
       "      <td>tableau</td>\n",
       "      <td>2499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Fardeen Mahabir</td>\n",
       "      <td>pandas</td>\n",
       "      <td>1099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Tarun Thaker</td>\n",
       "      <td>pandas</td>\n",
       "      <td>1099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>David Mukhopadhyay</td>\n",
       "      <td>pandas</td>\n",
       "      <td>1099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Elias Dodiya</td>\n",
       "      <td>ms sxcel</td>\n",
       "      <td>1599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Tarun Thaker</td>\n",
       "      <td>ms sxcel</td>\n",
       "      <td>1599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>David Mukhopadhyay</td>\n",
       "      <td>ms sxcel</td>\n",
       "      <td>1599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Yasmin Palan</td>\n",
       "      <td>ms sxcel</td>\n",
       "      <td>1599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Radha Dutt</td>\n",
       "      <td>ms sxcel</td>\n",
       "      <td>1599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Kailash Harjo</td>\n",
       "      <td>pyspark</td>\n",
       "      <td>2499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Tarun Thaker</td>\n",
       "      <td>pyspark</td>\n",
       "      <td>2499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>David Mukhopadhyay</td>\n",
       "      <td>pyspark</td>\n",
       "      <td>2499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Yasmin Palan</td>\n",
       "      <td>pyspark</td>\n",
       "      <td>2499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Shashank D’Alia</td>\n",
       "      <td>pyspark</td>\n",
       "      <td>2499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Radha Dutt</td>\n",
       "      <td>pyspark</td>\n",
       "      <td>2499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Tarun Thaker</td>\n",
       "      <td>sql</td>\n",
       "      <td>3499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Qabeel Raman</td>\n",
       "      <td>sql</td>\n",
       "      <td>3499</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  name       course_name  price\n",
       "0       Chhavi Lachman            python   2499\n",
       "1            Preet Sha            python   2499\n",
       "2      Fardeen Mahabir            python   2499\n",
       "3        Kailash Harjo            python   2499\n",
       "4           Seema Kota            python   2499\n",
       "5      Shashank D’Alia            python   2499\n",
       "6           Radha Dutt            python   2499\n",
       "7     Pranab Natarajan            python   2499\n",
       "8       Chhavi Lachman  machine learning   9999\n",
       "9         Qabeel Raman  machine learning   9999\n",
       "10        Radhika Suri  machine learning   9999\n",
       "11    Pranab Natarajan  machine learning   9999\n",
       "12      Chhavi Lachman     data analysis   4999\n",
       "13        Elias Dodiya     data analysis   4999\n",
       "14          Yash Sethi     data analysis   4999\n",
       "15      Munni Varghese     data analysis   4999\n",
       "16      Parveen Bhalla     data analysis   4999\n",
       "17      Chhavi Lachman          power bi   1899\n",
       "18     Fardeen Mahabir          power bi   1899\n",
       "19       Kailash Harjo          power bi   1899\n",
       "20        Tarun Thaker          power bi   1899\n",
       "21          Yash Sethi          power bi   1899\n",
       "22    Pranab Natarajan          power bi   1899\n",
       "23      Chhavi Lachman            plotly    699\n",
       "24        Elias Dodiya            plotly    699\n",
       "25       Kailash Harjo            plotly    699\n",
       "26    Pranab Natarajan            plotly    699\n",
       "27      Chhavi Lachman           tableau   2499\n",
       "28           Preet Sha           tableau   2499\n",
       "29        Elias Dodiya           tableau   2499\n",
       "30          Yash Sethi           tableau   2499\n",
       "31      Parveen Bhalla           tableau   2499\n",
       "32          Radha Dutt           tableau   2499\n",
       "33         Esha Butala           tableau   2499\n",
       "34     Fardeen Mahabir            pandas   1099\n",
       "35        Tarun Thaker            pandas   1099\n",
       "36  David Mukhopadhyay            pandas   1099\n",
       "37        Elias Dodiya          ms sxcel   1599\n",
       "38        Tarun Thaker          ms sxcel   1599\n",
       "39  David Mukhopadhyay          ms sxcel   1599\n",
       "40        Yasmin Palan          ms sxcel   1599\n",
       "41          Radha Dutt          ms sxcel   1599\n",
       "42       Kailash Harjo           pyspark   2499\n",
       "43        Tarun Thaker           pyspark   2499\n",
       "44  David Mukhopadhyay           pyspark   2499\n",
       "45        Yasmin Palan           pyspark   2499\n",
       "46     Shashank D’Alia           pyspark   2499\n",
       "47          Radha Dutt           pyspark   2499\n",
       "48        Tarun Thaker               sql   3499\n",
       "49        Qabeel Raman               sql   3499"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3. Print the registration table\n",
    "# cols -> name -> course -> price...........for joining 3 tables,merge 2 times\n",
    "regs.merge(students,on='student_id').merge(courses,on='course_id')[['name','course_name','price']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='course_name'>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 4. Plot bar chart for revenue/course\n",
    "regs.merge(courses,on='course_id').groupby('course_name')['price'].sum().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Chhavi Lachman', 'Fardeen Mahabir', 'Elias Dodiya',\n",
       "       'Kailash Harjo', 'Tarun Thaker', 'Yash Sethi',\n",
       "       'David Mukhopadhyay', 'Yasmin Palan', 'Parveen Bhalla'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 5. find students who enrolled in both the months\n",
    "nov.merge(dec,on='student_id').merge(students,on='student_id')['name'].unique()    #or"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  3,  7, 11, 16, 17, 18, 22, 23], dtype=int64)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "common_id = np.intersect1d(nov['student_id'],dec['student_id'])\n",
    "common_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>student_id</th>\n",
       "      <th>name</th>\n",
       "      <th>partner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Kailash Harjo</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Parveen Bhalla</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Tarun Thaker</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>David Mukhopadhyay</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>Elias Dodiya</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>Yasmin Palan</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>Fardeen Mahabir</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>Yash Sethi</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>Chhavi Lachman</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    student_id                name  partner\n",
       "0            1       Kailash Harjo       23\n",
       "2            3      Parveen Bhalla        3\n",
       "6            7        Tarun Thaker        9\n",
       "10          11  David Mukhopadhyay       20\n",
       "15          16        Elias Dodiya       25\n",
       "16          17        Yasmin Palan        7\n",
       "17          18     Fardeen Mahabir       13\n",
       "21          22          Yash Sethi       21\n",
       "22          23      Chhavi Lachman       18"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "students[students['student_id'].isin(common_id)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>course_id</th>\n",
       "      <th>course_name</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>Numpy</td>\n",
       "      <td>699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>C++</td>\n",
       "      <td>1299</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    course_id course_name  price\n",
       "10         11       Numpy    699\n",
       "11         12         C++   1299"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 6. find course that got no enrollment\n",
    "no_enroll_id = np.setdiff1d(courses['course_id'],regs['course_id'])\n",
    "courses[courses['course_id'].isin(no_enroll_id)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>student_id</th>\n",
       "      <th>name</th>\n",
       "      <th>partner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Marlo Dugal</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Kusum Bahri</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Lakshmi Contractor</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Radheshyam Dey</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Nitika Chatterjee</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>Aayushman Sant</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>Hanuman Hegde</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>Nitish</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>Ankit</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>Rahul</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    student_id                name  partner\n",
       "3            4         Marlo Dugal       14\n",
       "4            5         Kusum Bahri        6\n",
       "5            6  Lakshmi Contractor       10\n",
       "7            8      Radheshyam Dey        5\n",
       "8            9   Nitika Chatterjee        4\n",
       "9           10      Aayushman Sant        8\n",
       "19          20       Hanuman Hegde       11\n",
       "25          26              Nitish       28\n",
       "26          27               Ankit       26\n",
       "27          28               Rahul       17"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 7. find students who did not enroll into any courses\n",
    "student_id_list = np.setdiff1d(students['student_id'],regs['student_id'])\n",
    "students[students['student_id'].isin(student_id_list)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th>name_x</th>\n",
       "      <th>name_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Kailash Harjo</td>\n",
       "      <td>Chhavi Lachman</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Esha Butala</td>\n",
       "      <td>Kailash Harjo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Parveen Bhalla</td>\n",
       "      <td>Parveen Bhalla</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Marlo Dugal</td>\n",
       "      <td>Pranab Natarajan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kusum Bahri</td>\n",
       "      <td>Lakshmi Contractor</td>\n",
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       "      <th>5</th>\n",
       "      <td>Lakshmi Contractor</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Tarun Thaker</td>\n",
       "      <td>Nitika Chatterjee</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Radheshyam Dey</td>\n",
       "      <td>Kusum Bahri</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Nitika Chatterjee</td>\n",
       "      <td>Marlo Dugal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Aayushman Sant</td>\n",
       "      <td>Radheshyam Dey</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>David Mukhopadhyay</td>\n",
       "      <td>Hanuman Hegde</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Radha Dutt</td>\n",
       "      <td>Qabeel Raman</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Munni Varghese</td>\n",
       "      <td>Radhika Suri</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Pranab Natarajan</td>\n",
       "      <td>Yash Sethi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Preet Sha</td>\n",
       "      <td>Elias Dodiya</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Elias Dodiya</td>\n",
       "      <td>Shashank D’Alia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Yasmin Palan</td>\n",
       "      <td>Tarun Thaker</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Fardeen Mahabir</td>\n",
       "      <td>Munni Varghese</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Qabeel Raman</td>\n",
       "      <td>Radha Dutt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Hanuman Hegde</td>\n",
       "      <td>David Mukhopadhyay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Seema Kota</td>\n",
       "      <td>Preet Sha</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Yash Sethi</td>\n",
       "      <td>Seema Kota</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Chhavi Lachman</td>\n",
       "      <td>Fardeen Mahabir</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Radhika Suri</td>\n",
       "      <td>Yasmin Palan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Rahul</td>\n",
       "      <td>Yasmin Palan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Shashank D’Alia</td>\n",
       "      <td>Esha Butala</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Nitish</td>\n",
       "      <td>Rahul</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Ankit</td>\n",
       "      <td>Nitish</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                name_x              name_y\n",
       "0        Kailash Harjo      Chhavi Lachman\n",
       "1          Esha Butala       Kailash Harjo\n",
       "2       Parveen Bhalla      Parveen Bhalla\n",
       "3          Marlo Dugal    Pranab Natarajan\n",
       "4          Kusum Bahri  Lakshmi Contractor\n",
       "5   Lakshmi Contractor      Aayushman Sant\n",
       "6         Tarun Thaker   Nitika Chatterjee\n",
       "7       Radheshyam Dey         Kusum Bahri\n",
       "8    Nitika Chatterjee         Marlo Dugal\n",
       "9       Aayushman Sant      Radheshyam Dey\n",
       "10  David Mukhopadhyay       Hanuman Hegde\n",
       "11          Radha Dutt        Qabeel Raman\n",
       "12      Munni Varghese        Radhika Suri\n",
       "13    Pranab Natarajan          Yash Sethi\n",
       "14           Preet Sha        Elias Dodiya\n",
       "15        Elias Dodiya     Shashank D’Alia\n",
       "16        Yasmin Palan        Tarun Thaker\n",
       "17     Fardeen Mahabir      Munni Varghese\n",
       "18        Qabeel Raman          Radha Dutt\n",
       "19       Hanuman Hegde  David Mukhopadhyay\n",
       "20          Seema Kota           Preet Sha\n",
       "21          Yash Sethi          Seema Kota\n",
       "22      Chhavi Lachman     Fardeen Mahabir\n",
       "23        Radhika Suri        Yasmin Palan\n",
       "24               Rahul        Yasmin Palan\n",
       "25     Shashank D’Alia         Esha Butala\n",
       "26              Nitish               Rahul\n",
       "27               Ankit              Nitish"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 8. Print student name -> partner name for all enrolled students\n",
    "# self join is a varient of inner join where both table is same..use 'left_on','right_on'\n",
    "students.merge(students,how='inner',left_on='partner',right_on='student_id')[['name_x','name_y']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "student_id  name          \n",
       "23          Chhavi Lachman    6\n",
       "7           Tarun Thaker      5\n",
       "1           Kailash Harjo     4\n",
       "Name: name, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 9. find top 3 students who did most number enrollments\n",
    "regs.merge(students,on='student_id').groupby(['student_id','name'])['name'].count().sort_values(ascending=False).head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "student_id  name            \n",
       "23          Chhavi Lachman      22594\n",
       "14          Pranab Natarajan    15096\n",
       "19          Qabeel Raman        13498\n",
       "Name: price, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 10. find top 3 students who spent most amount of money on courses\n",
    "regs.merge(courses,on='course_id').merge(students,on='student_id').groupby(['student_id','name'])['price'].sum().sort_values(ascending=False).head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>4</th>\n",
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       "      <th>5</th>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
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       "      <td>7</td>\n",
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       "      <th>10</th>\n",
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       "      <th>11</th>\n",
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       "      <td>Tarun Thaker</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>11</td>\n",
       "      <td>David Mukhopadhyay</td>\n",
       "      <td>20</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>11</td>\n",
       "      <td>David Mukhopadhyay</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>11</td>\n",
       "      <td>David Mukhopadhyay</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>12</td>\n",
       "      <td>Radha Dutt</td>\n",
       "      <td>19</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>12</td>\n",
       "      <td>Radha Dutt</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>12</td>\n",
       "      <td>Radha Dutt</td>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>12</td>\n",
       "      <td>Radha Dutt</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>13</td>\n",
       "      <td>Munni Varghese</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>14</td>\n",
       "      <td>Pranab Natarajan</td>\n",
       "      <td>22</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>14</td>\n",
       "      <td>Pranab Natarajan</td>\n",
       "      <td>22</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>14</td>\n",
       "      <td>Pranab Natarajan</td>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>14</td>\n",
       "      <td>Pranab Natarajan</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>15</td>\n",
       "      <td>Preet Sha</td>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>15</td>\n",
       "      <td>Preet Sha</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>16</td>\n",
       "      <td>Elias Dodiya</td>\n",
       "      <td>25</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>16</td>\n",
       "      <td>Elias Dodiya</td>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>16</td>\n",
       "      <td>Elias Dodiya</td>\n",
       "      <td>25</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>16</td>\n",
       "      <td>Elias Dodiya</td>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>17</td>\n",
       "      <td>Yasmin Palan</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>17</td>\n",
       "      <td>Yasmin Palan</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>18</td>\n",
       "      <td>Fardeen Mahabir</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>18</td>\n",
       "      <td>Fardeen Mahabir</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>18</td>\n",
       "      <td>Fardeen Mahabir</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>19</td>\n",
       "      <td>Qabeel Raman</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>19</td>\n",
       "      <td>Qabeel Raman</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>21</td>\n",
       "      <td>Seema Kota</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>22</td>\n",
       "      <td>Yash Sethi</td>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>22</td>\n",
       "      <td>Yash Sethi</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>22</td>\n",
       "      <td>Yash Sethi</td>\n",
       "      <td>21</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>23</td>\n",
       "      <td>Chhavi Lachman</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>23</td>\n",
       "      <td>Chhavi Lachman</td>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>23</td>\n",
       "      <td>Chhavi Lachman</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>23</td>\n",
       "      <td>Chhavi Lachman</td>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>23</td>\n",
       "      <td>Chhavi Lachman</td>\n",
       "      <td>18</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>23</td>\n",
       "      <td>Chhavi Lachman</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>24</td>\n",
       "      <td>Radhika Suri</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>25</td>\n",
       "      <td>Shashank D’Alia</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>25</td>\n",
       "      <td>Shashank D’Alia</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    student_id                name  partner  course_id\n",
       "0            1       Kailash Harjo       23          1\n",
       "1            1       Kailash Harjo       23          6\n",
       "2            1       Kailash Harjo       23         10\n",
       "3            1       Kailash Harjo       23          9\n",
       "4            2         Esha Butala        1          5\n",
       "5            3      Parveen Bhalla        3          3\n",
       "6            3      Parveen Bhalla        3          5\n",
       "7            7        Tarun Thaker        9          8\n",
       "8            7        Tarun Thaker        9         10\n",
       "9            7        Tarun Thaker        9          7\n",
       "10           7        Tarun Thaker        9          2\n",
       "11           7        Tarun Thaker        9          6\n",
       "12          11  David Mukhopadhyay       20          7\n",
       "13          11  David Mukhopadhyay       20          8\n",
       "14          11  David Mukhopadhyay       20         10\n",
       "15          12          Radha Dutt       19         10\n",
       "16          12          Radha Dutt       19          1\n",
       "17          12          Radha Dutt       19          5\n",
       "18          12          Radha Dutt       19          7\n",
       "19          13      Munni Varghese       24          3\n",
       "20          14    Pranab Natarajan       22          9\n",
       "21          14    Pranab Natarajan       22          6\n",
       "22          14    Pranab Natarajan       22          4\n",
       "23          14    Pranab Natarajan       22          1\n",
       "24          15           Preet Sha       16          5\n",
       "25          15           Preet Sha       16          1\n",
       "26          16        Elias Dodiya       25          9\n",
       "27          16        Elias Dodiya       25          5\n",
       "28          16        Elias Dodiya       25          7\n",
       "29          16        Elias Dodiya       25          3\n",
       "30          17        Yasmin Palan        7          7\n",
       "31          17        Yasmin Palan        7         10\n",
       "32          18     Fardeen Mahabir       13          6\n",
       "33          18     Fardeen Mahabir       13          1\n",
       "34          18     Fardeen Mahabir       13          8\n",
       "35          19        Qabeel Raman       12          4\n",
       "36          19        Qabeel Raman       12          2\n",
       "37          21          Seema Kota       15          1\n",
       "38          22          Yash Sethi       21          3\n",
       "39          22          Yash Sethi       21          5\n",
       "40          22          Yash Sethi       21          6\n",
       "41          23      Chhavi Lachman       18          1\n",
       "42          23      Chhavi Lachman       18          4\n",
       "43          23      Chhavi Lachman       18          3\n",
       "44          23      Chhavi Lachman       18          6\n",
       "45          23      Chhavi Lachman       18          9\n",
       "46          23      Chhavi Lachman       18          5\n",
       "47          24        Radhika Suri       17          4\n",
       "48          25     Shashank D’Alia        2          1\n",
       "49          25     Shashank D’Alia        2         10"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Alternate syntax for merge\n",
    "# students.merge(regs)\n",
    "\n",
    "pd.merge(students,regs,how='inner',on='student_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>match_id</th>\n",
       "      <th>inning</th>\n",
       "      <th>batting_team</th>\n",
       "      <th>bowling_team</th>\n",
       "      <th>over</th>\n",
       "      <th>ball</th>\n",
       "      <th>batsman</th>\n",
       "      <th>non_striker</th>\n",
       "      <th>bowler</th>\n",
       "      <th>is_super_over</th>\n",
       "      <th>...</th>\n",
       "      <th>bye_runs</th>\n",
       "      <th>legbye_runs</th>\n",
       "      <th>noball_runs</th>\n",
       "      <th>penalty_runs</th>\n",
       "      <th>batsman_runs</th>\n",
       "      <th>extra_runs</th>\n",
       "      <th>total_runs</th>\n",
       "      <th>player_dismissed</th>\n",
       "      <th>dismissal_kind</th>\n",
       "      <th>fielder</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   match_id  inning         batting_team                 bowling_team  over  \\\n",
       "0         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "\n",
       "   ball    batsman non_striker    bowler  is_super_over  ...  bye_runs  \\\n",
       "0     1  DA Warner    S Dhawan  TS Mills              0  ...         0   \n",
       "\n",
       "   legbye_runs  noball_runs  penalty_runs  batsman_runs  extra_runs  \\\n",
       "0            0            0             0             0           0   \n",
       "\n",
       "   total_runs  player_dismissed dismissal_kind fielder  \n",
       "0           0               NaN            NaN     NaN  \n",
       "\n",
       "[1 rows x 21 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# IPL Problems\n",
    "\n",
    "# find top 3 studiums with highest sixes/match ratio\n",
    "\n",
    "delivery.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>id</th>\n",
       "      <th>season</th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>toss_winner</th>\n",
       "      <th>toss_decision</th>\n",
       "      <th>result</th>\n",
       "      <th>dl_applied</th>\n",
       "      <th>winner</th>\n",
       "      <th>win_by_runs</th>\n",
       "      <th>win_by_wickets</th>\n",
       "      <th>player_of_match</th>\n",
       "      <th>venue</th>\n",
       "      <th>umpire1</th>\n",
       "      <th>umpire2</th>\n",
       "      <th>umpire3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2017-04-05</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>Yuvraj Singh</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>AY Dandekar</td>\n",
       "      <td>NJ Llong</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  season       city        date                team1  \\\n",
       "0   1    2017  Hyderabad  2017-04-05  Sunrisers Hyderabad   \n",
       "\n",
       "                         team2                  toss_winner toss_decision  \\\n",
       "0  Royal Challengers Bangalore  Royal Challengers Bangalore         field   \n",
       "\n",
       "   result  dl_applied               winner  win_by_runs  win_by_wickets  \\\n",
       "0  normal           0  Sunrisers Hyderabad           35               0   \n",
       "\n",
       "  player_of_match                                      venue      umpire1  \\\n",
       "0    Yuvraj Singh  Rajiv Gandhi International Stadium, Uppal  AY Dandekar   \n",
       "\n",
       "    umpire2  umpire3  \n",
       "0  NJ Llong      NaN  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matches.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp_df = delivery.merge(matches,left_on='match_id',right_on='id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "six_df= temp_df[temp_df['batsman_runs'] == 6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_sixes = six_df.groupby('venue')['venue'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_matches = matches['venue'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Holkar Cricket Stadium     17.600000\n",
       "M Chinnaswamy Stadium      13.227273\n",
       "Sharjah Cricket Stadium    12.666667\n",
       "Name: venue, dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(num_sixes/num_matches).sort_values(ascending=False).head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>match_id</th>\n",
       "      <th>inning</th>\n",
       "      <th>batting_team</th>\n",
       "      <th>bowling_team</th>\n",
       "      <th>over</th>\n",
       "      <th>ball</th>\n",
       "      <th>batsman</th>\n",
       "      <th>non_striker</th>\n",
       "      <th>bowler</th>\n",
       "      <th>is_super_over</th>\n",
       "      <th>...</th>\n",
       "      <th>result</th>\n",
       "      <th>dl_applied</th>\n",
       "      <th>winner</th>\n",
       "      <th>win_by_runs</th>\n",
       "      <th>win_by_wickets</th>\n",
       "      <th>player_of_match</th>\n",
       "      <th>venue</th>\n",
       "      <th>umpire1</th>\n",
       "      <th>umpire2</th>\n",
       "      <th>umpire3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>Yuvraj Singh</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>AY Dandekar</td>\n",
       "      <td>NJ Llong</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>Yuvraj Singh</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>AY Dandekar</td>\n",
       "      <td>NJ Llong</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>Yuvraj Singh</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>AY Dandekar</td>\n",
       "      <td>NJ Llong</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>Yuvraj Singh</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>AY Dandekar</td>\n",
       "      <td>NJ Llong</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>Yuvraj Singh</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>AY Dandekar</td>\n",
       "      <td>NJ Llong</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150455</th>\n",
       "      <td>636</td>\n",
       "      <td>2</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>Sachin Baby</td>\n",
       "      <td>CJ Jordan</td>\n",
       "      <td>B Kumar</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>BCJ Cutting</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>HDPK Dharmasena</td>\n",
       "      <td>BNJ Oxenford</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150456</th>\n",
       "      <td>636</td>\n",
       "      <td>2</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>Sachin Baby</td>\n",
       "      <td>CJ Jordan</td>\n",
       "      <td>B Kumar</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>BCJ Cutting</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>HDPK Dharmasena</td>\n",
       "      <td>BNJ Oxenford</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150457</th>\n",
       "      <td>636</td>\n",
       "      <td>2</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>Iqbal Abdulla</td>\n",
       "      <td>Sachin Baby</td>\n",
       "      <td>B Kumar</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>BCJ Cutting</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>HDPK Dharmasena</td>\n",
       "      <td>BNJ Oxenford</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150458</th>\n",
       "      <td>636</td>\n",
       "      <td>2</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>Sachin Baby</td>\n",
       "      <td>Iqbal Abdulla</td>\n",
       "      <td>B Kumar</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>BCJ Cutting</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>HDPK Dharmasena</td>\n",
       "      <td>BNJ Oxenford</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150459</th>\n",
       "      <td>636</td>\n",
       "      <td>2</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>Iqbal Abdulla</td>\n",
       "      <td>Sachin Baby</td>\n",
       "      <td>B Kumar</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>BCJ Cutting</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>HDPK Dharmasena</td>\n",
       "      <td>BNJ Oxenford</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150460 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        match_id  inning                 batting_team  \\\n",
       "0              1       1          Sunrisers Hyderabad   \n",
       "1              1       1          Sunrisers Hyderabad   \n",
       "2              1       1          Sunrisers Hyderabad   \n",
       "3              1       1          Sunrisers Hyderabad   \n",
       "4              1       1          Sunrisers Hyderabad   \n",
       "...          ...     ...                          ...   \n",
       "150455       636       2  Royal Challengers Bangalore   \n",
       "150456       636       2  Royal Challengers Bangalore   \n",
       "150457       636       2  Royal Challengers Bangalore   \n",
       "150458       636       2  Royal Challengers Bangalore   \n",
       "150459       636       2  Royal Challengers Bangalore   \n",
       "\n",
       "                       bowling_team  over  ball        batsman    non_striker  \\\n",
       "0       Royal Challengers Bangalore     1     1      DA Warner       S Dhawan   \n",
       "1       Royal Challengers Bangalore     1     2      DA Warner       S Dhawan   \n",
       "2       Royal Challengers Bangalore     1     3      DA Warner       S Dhawan   \n",
       "3       Royal Challengers Bangalore     1     4      DA Warner       S Dhawan   \n",
       "4       Royal Challengers Bangalore     1     5      DA Warner       S Dhawan   \n",
       "...                             ...   ...   ...            ...            ...   \n",
       "150455          Sunrisers Hyderabad    20     2    Sachin Baby      CJ Jordan   \n",
       "150456          Sunrisers Hyderabad    20     3    Sachin Baby      CJ Jordan   \n",
       "150457          Sunrisers Hyderabad    20     4  Iqbal Abdulla    Sachin Baby   \n",
       "150458          Sunrisers Hyderabad    20     5    Sachin Baby  Iqbal Abdulla   \n",
       "150459          Sunrisers Hyderabad    20     6  Iqbal Abdulla    Sachin Baby   \n",
       "\n",
       "          bowler  is_super_over  ...  result  dl_applied               winner  \\\n",
       "0       TS Mills              0  ...  normal           0  Sunrisers Hyderabad   \n",
       "1       TS Mills              0  ...  normal           0  Sunrisers Hyderabad   \n",
       "2       TS Mills              0  ...  normal           0  Sunrisers Hyderabad   \n",
       "3       TS Mills              0  ...  normal           0  Sunrisers Hyderabad   \n",
       "4       TS Mills              0  ...  normal           0  Sunrisers Hyderabad   \n",
       "...          ...            ...  ...     ...         ...                  ...   \n",
       "150455   B Kumar              0  ...  normal           0  Sunrisers Hyderabad   \n",
       "150456   B Kumar              0  ...  normal           0  Sunrisers Hyderabad   \n",
       "150457   B Kumar              0  ...  normal           0  Sunrisers Hyderabad   \n",
       "150458   B Kumar              0  ...  normal           0  Sunrisers Hyderabad   \n",
       "150459   B Kumar              0  ...  normal           0  Sunrisers Hyderabad   \n",
       "\n",
       "        win_by_runs  win_by_wickets  player_of_match  \\\n",
       "0                35               0     Yuvraj Singh   \n",
       "1                35               0     Yuvraj Singh   \n",
       "2                35               0     Yuvraj Singh   \n",
       "3                35               0     Yuvraj Singh   \n",
       "4                35               0     Yuvraj Singh   \n",
       "...             ...             ...              ...   \n",
       "150455            8               0      BCJ Cutting   \n",
       "150456            8               0      BCJ Cutting   \n",
       "150457            8               0      BCJ Cutting   \n",
       "150458            8               0      BCJ Cutting   \n",
       "150459            8               0      BCJ Cutting   \n",
       "\n",
       "                                            venue          umpire1  \\\n",
       "0       Rajiv Gandhi International Stadium, Uppal      AY Dandekar   \n",
       "1       Rajiv Gandhi International Stadium, Uppal      AY Dandekar   \n",
       "2       Rajiv Gandhi International Stadium, Uppal      AY Dandekar   \n",
       "3       Rajiv Gandhi International Stadium, Uppal      AY Dandekar   \n",
       "4       Rajiv Gandhi International Stadium, Uppal      AY Dandekar   \n",
       "...                                           ...              ...   \n",
       "150455                      M Chinnaswamy Stadium  HDPK Dharmasena   \n",
       "150456                      M Chinnaswamy Stadium  HDPK Dharmasena   \n",
       "150457                      M Chinnaswamy Stadium  HDPK Dharmasena   \n",
       "150458                      M Chinnaswamy Stadium  HDPK Dharmasena   \n",
       "150459                      M Chinnaswamy Stadium  HDPK Dharmasena   \n",
       "\n",
       "             umpire2 umpire3  \n",
       "0           NJ Llong     NaN  \n",
       "1           NJ Llong     NaN  \n",
       "2           NJ Llong     NaN  \n",
       "3           NJ Llong     NaN  \n",
       "4           NJ Llong     NaN  \n",
       "...              ...     ...  \n",
       "150455  BNJ Oxenford     NaN  \n",
       "150456  BNJ Oxenford     NaN  \n",
       "150457  BNJ Oxenford     NaN  \n",
       "150458  BNJ Oxenford     NaN  \n",
       "150459  BNJ Oxenford     NaN  \n",
       "\n",
       "[150460 rows x 39 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find orange cap holder of all the seasons:batsman who scored highest in that particular season\n",
    "temp_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>season</th>\n",
       "      <th>batsman</th>\n",
       "      <th>batsman_runs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>2008</td>\n",
       "      <td>SE Marsh</td>\n",
       "      <td>616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>2009</td>\n",
       "      <td>ML Hayden</td>\n",
       "      <td>572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>446</th>\n",
       "      <td>2010</td>\n",
       "      <td>SR Tendulkar</td>\n",
       "      <td>618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>2011</td>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>684</th>\n",
       "      <td>2012</td>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>910</th>\n",
       "      <td>2013</td>\n",
       "      <td>MEK Hussey</td>\n",
       "      <td>733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1088</th>\n",
       "      <td>2014</td>\n",
       "      <td>RV Uthappa</td>\n",
       "      <td>660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1148</th>\n",
       "      <td>2015</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1383</th>\n",
       "      <td>2016</td>\n",
       "      <td>V Kohli</td>\n",
       "      <td>973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1422</th>\n",
       "      <td>2017</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>641</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      season       batsman  batsman_runs\n",
       "115     2008      SE Marsh           616\n",
       "229     2009     ML Hayden           572\n",
       "446     2010  SR Tendulkar           618\n",
       "502     2011      CH Gayle           608\n",
       "684     2012      CH Gayle           733\n",
       "910     2013    MEK Hussey           733\n",
       "1088    2014    RV Uthappa           660\n",
       "1148    2015     DA Warner           562\n",
       "1383    2016       V Kohli           973\n",
       "1422    2017     DA Warner           641"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_df.groupby(['season','batsman'])['batsman_runs'].sum().reset_index().sort_values('batsman_runs',ascending=False).drop_duplicates(subset=['season'],keep='first').sort_values('season')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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