23 Structured Data Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a48f7509-c2a0-4022-b9d7-cb82c1cf6c09",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1595bee-d373-4925-ab0a-02a6c2f0418c",
   "metadata": {},
   "source": [
    "###### Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7e513121-d6a0-4a0c-b4c4-8b25b6ab9a6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "s = pd.Series([45,22,17,5,33,16,85])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dc3814a8-7633-422f-954d-0f3b6e78429a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    45\n",
       "1    22\n",
       "2    17\n",
       "3     5\n",
       "4    33\n",
       "5    16\n",
       "6    85\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "83cd7c5c-f143-470a-af78-59eb69f75f23",
   "metadata": {},
   "outputs": [],
   "source": [
    "s = pd.Series([45,22,17,5,33,16,85], dtype = np.int8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a7139ae2-7fd5-4905-9b75-756fc5fd398e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    45\n",
       "1    22\n",
       "2    17\n",
       "3     5\n",
       "4    33\n",
       "5    16\n",
       "6    85\n",
       "dtype: int8"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d44d3613-1805-4dff-84c7-e059aeb02277",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    45\n",
       "1    22\n",
       "2    17\n",
       "3     5\n",
       "4    99\n",
       "5    16\n",
       "6    85\n",
       "dtype: int8"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[4] = 99\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ba25d264-20a3-4ef7-baf7-f835c31dda38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8d868603-a318-4cd3-96c1-12d6c58ac2f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int8')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0ead670f-3c67-49f5-bfca-f9bfbf8d853a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7,)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bc7eb7be-82aa-4900-8f9c-419b03b32d4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0837e204-c955-46a8-81c0-8727ecbdfe32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([45, 22, 17,  5, 99, 16, 85], dtype=int8)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d4ef785-4ef8-438b-96d7-7c0e3de081b4",
   "metadata": {},
   "source": [
    "##### DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c7c95589-1c64-400a-bb04-f3607f6206a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "r = pd.Series([1,2,3,4,5])\n",
    "n = pd.Series(['nitin','rajan','rani','priya','manoj'])\n",
    "m = pd.Series([56.78, 76.23, 67.89, 77.45, 71.34])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "93f65a68-d0cb-4cac-987f-4855c6a48034",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({\n",
    "    'roll': r,\n",
    "    'name': n,\n",
    "    'marks': m\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "aa26eb33-7850-4c4f-b7cc-a0f30bf766be",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>roll</th>\n",
       "      <th>name</th>\n",
       "      <th>marks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>nitin</td>\n",
       "      <td>56.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>rajan</td>\n",
       "      <td>76.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>rani</td>\n",
       "      <td>67.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>priya</td>\n",
       "      <td>77.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>manoj</td>\n",
       "      <td>71.34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   roll   name  marks\n",
       "0     1  nitin  56.78\n",
       "1     2  rajan  76.23\n",
       "2     3   rani  67.89\n",
       "3     4  priya  77.45\n",
       "4     5  manoj  71.34"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df"
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  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ea465479-66f5-4e3a-8856-6ba212bece77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "385800c2-6422-4b18-8164-96c93569acdd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "roll       int64\n",
       "name      object\n",
       "marks    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f132a8c4-8a47-4030-95bc-b86ee6cbad94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 3)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4e85468e-bec1-4989-bb38-bb26ce1c07d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.size"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcc264e2-532c-44b1-9891-9615a96d83a0",
   "metadata": {},
   "source": [
    "###### Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a7f75a0e-0c11-418b-8bb6-5cb034a776c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://mitu.co.in/dataset\n",
    "# Download all the files whose name starts with student*\n",
    "# Store in current working directory in folder datasets/"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82d5924f-6030-448b-8d92-20f4d0a1c9d4",
   "metadata": {},
   "source": [
    "###### Load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "510f97ea-f7be-419b-b5d3-764e5cfc0fa7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('datasets/student.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4d033a90-c075-4208-bbab-2d33d4d7021f",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>roll</th>\n",
       "      <th>name</th>\n",
       "      <th>class</th>\n",
       "      <th>marks</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>anil</td>\n",
       "      <td>TE</td>\n",
       "      <td>56.77</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>amit</td>\n",
       "      <td>TE</td>\n",
       "      <td>59.77</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>aniket</td>\n",
       "      <td>BE</td>\n",
       "      <td>76.88</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>ajinkya</td>\n",
       "      <td>TE</td>\n",
       "      <td>69.66</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>asha</td>\n",
       "      <td>TE</td>\n",
       "      <td>63.28</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>ayesha</td>\n",
       "      <td>BE</td>\n",
       "      <td>49.55</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>amar</td>\n",
       "      <td>BE</td>\n",
       "      <td>65.34</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>amita</td>\n",
       "      <td>BE</td>\n",
       "      <td>68.33</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>amol</td>\n",
       "      <td>TE</td>\n",
       "      <td>56.75</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>anmol</td>\n",
       "      <td>BE</td>\n",
       "      <td>78.66</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   roll     name class  marks  age\n",
       "0     1     anil    TE  56.77   22\n",
       "1     2     amit    TE  59.77   21\n",
       "2     3   aniket    BE  76.88   19\n",
       "3     4  ajinkya    TE  69.66   20\n",
       "4     5     asha    TE  63.28   20\n",
       "5     6   ayesha    BE  49.55   20\n",
       "6     7     amar    BE  65.34   19\n",
       "7     8    amita    BE  68.33   23\n",
       "8     9     amol    TE  56.75   20\n",
       "9    10    anmol    BE  78.66   21"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3d8f2fee-9afd-47b8-bbc1-606ab82b7cb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6d156dfe-4dcc-4683-815a-a1dac36a73f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.read_csv('datasets/student1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "abe6c3ec-13a8-466b-baaf-f729c29a789e",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>anil</th>\n",
       "      <th>TE</th>\n",
       "      <th>56.77</th>\n",
       "      <th>22</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>amit</td>\n",
       "      <td>TE</td>\n",
       "      <td>59.77</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>aniket</td>\n",
       "      <td>BE</td>\n",
       "      <td>76.88</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>ajinkya</td>\n",
       "      <td>TE</td>\n",
       "      <td>69.66</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>asha</td>\n",
       "      <td>TE</td>\n",
       "      <td>63.28</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>ayesha</td>\n",
       "      <td>BE</td>\n",
       "      <td>49.55</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7</td>\n",
       "      <td>amar</td>\n",
       "      <td>BE</td>\n",
       "      <td>65.34</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8</td>\n",
       "      <td>amita</td>\n",
       "      <td>BE</td>\n",
       "      <td>68.33</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9</td>\n",
       "      <td>amol</td>\n",
       "      <td>TE</td>\n",
       "      <td>56.75</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10</td>\n",
       "      <td>anmol</td>\n",
       "      <td>BE</td>\n",
       "      <td>78.66</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "    1     anil  TE  56.77  22\n",
       "0   2     amit  TE  59.77  21\n",
       "1   3   aniket  BE  76.88  19\n",
       "2   4  ajinkya  TE  69.66  20\n",
       "3   5     asha  TE  63.28  20\n",
       "4   6   ayesha  BE  49.55  20\n",
       "5   7     amar  BE  65.34  19\n",
       "6   8    amita  BE  68.33  23\n",
       "7   9     amol  TE  56.75  20\n",
       "8  10    anmol  BE  78.66  21"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
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  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "6fb43ee2-ddc9-4b79-baeb-5f1ae2ff1978",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.read_csv('datasets/student1.csv', names=['A','B','C','D','E'])"
   ]
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  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "686f41e4-6dea-4e96-83f0-111ec68e3c54",
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       "    A        B   C      D   E\n",
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       "2   3   aniket  BE  76.88  19\n",
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       "4   5     asha  TE  63.28  20\n",
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       "7   8    amita  BE  68.33  23\n",
       "8   9     amol  TE  56.75  20\n",
       "9  10    anmol  BE  78.66  21"
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     "metadata": {},
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   "id": "65b3ebf6-a100-4555-870c-b8d02654aa74",
   "metadata": {},
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   "source": [
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   "execution_count": 36,
   "id": "5428c729-30d1-491a-9da9-6423cac2c403",
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   "outputs": [],
   "source": [
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   "execution_count": 35,
   "id": "c5061df4-c46b-42d2-b650-6ab22d39aa81",
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       "    1     anil  TE  56.77  22\n",
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       "2   4  ajinkya  TE  69.66  20\n",
       "3   5     asha  TE  63.28  20\n",
       "4   6   ayesha  BE  49.55  20\n",
       "5   7     amar  BE  65.34  19\n",
       "6   8    amita  BE  68.33  23\n",
       "7   9     amol  TE  56.75  20\n",
       "8  10    anmol  BE  78.66  21"
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   "execution_count": 37,
   "id": "18b4c7ef-1c47-4b62-b493-32a5040c1034",
   "metadata": {},
   "outputs": [],
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   "execution_count": 38,
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   "execution_count": 39,
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       "   roll     name class  marks  age\n",
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       "1     2     amit    TE  59.77   21\n",
       "2     3   aniket    BE  76.88   19\n",
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       "4     5     asha    TE  63.28   20\n",
       "5     6   ayesha    BE  49.55   20\n",
       "6     7     amar    BE  65.34   19\n",
       "7     8    amita    BE  68.33   23\n",
       "8     9     amol    TE  56.75   20\n",
       "9    10    anmol    BE  78.66   21"
      ]
     },
     "execution_count": 39,
     "metadata": {},
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   ],
   "source": [
    "df3 = pd.read_csv('datasets/student3.tsv', sep='\\t')\n",
    "df3"
   ]
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  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "b9bb305f-a48a-4941-a087-acddfec8781b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.read_csv('https://raw.githubusercontent.com/mwaskom/seaborn-data/master/iris.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "0d156291-d9fd-4e92-af56-b1ad979aa3f0",
   "metadata": {},
   "outputs": [
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       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width    species\n",
       "0             5.1          3.5           1.4          0.2     setosa\n",
       "1             4.9          3.0           1.4          0.2     setosa\n",
       "2             4.7          3.2           1.3          0.2     setosa\n",
       "3             4.6          3.1           1.5          0.2     setosa\n",
       "4             5.0          3.6           1.4          0.2     setosa\n",
       "..            ...          ...           ...          ...        ...\n",
       "145           6.7          3.0           5.2          2.3  virginica\n",
       "146           6.3          2.5           5.0          1.9  virginica\n",
       "147           6.5          3.0           5.2          2.0  virginica\n",
       "148           6.2          3.4           5.4          2.3  virginica\n",
       "149           5.9          3.0           5.1          1.8  virginica\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fb10538-bcc1-4c15-8a16-dcce5491e7ec",
   "metadata": {},
   "source": [
    "###### Access data information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "e5864e5f-ea91-470c-9c10-d92fb36bb55d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 5)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "7634a969-af80-40d6-b361-7acc373646af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "283741a9-fa0c-4c13-b021-af0fd9bc87e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "roll       int64\n",
       "name      object\n",
       "class     object\n",
       "marks    float64\n",
       "age        int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "9f6e3f4f-032a-426d-94cc-f87234f0da61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['roll', 'name', 'class', 'marks', 'age'], dtype='object')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "5ac0667e-37c3-4216-8843-ce4a17f95379",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=10, step=1)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "cc8318c0-1f97-4266-a83c-f2af02ee6f92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 'anil', 'TE', 56.77, 22],\n",
       "       [2, 'amit', 'TE', 59.77, 21],\n",
       "       [3, 'aniket', 'BE', 76.88, 19],\n",
       "       [4, 'ajinkya', 'TE', 69.66, 20],\n",
       "       [5, 'asha', 'TE', 63.28, 20],\n",
       "       [6, 'ayesha', 'BE', 49.55, 20],\n",
       "       [7, 'amar', 'BE', 65.34, 19],\n",
       "       [8, 'amita', 'BE', 68.33, 23],\n",
       "       [9, 'amol', 'TE', 56.75, 20],\n",
       "       [10, 'anmol', 'BE', 78.66, 21]], dtype=object)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "9b220b0d-3be8-4c7f-8ab9-59ff2565221b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>name</th>\n",
       "      <td>anil</td>\n",
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       "      <td>asha</td>\n",
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       "    <tr>\n",
       "      <th>class</th>\n",
       "      <td>TE</td>\n",
       "      <td>TE</td>\n",
       "      <td>BE</td>\n",
       "      <td>TE</td>\n",
       "      <td>TE</td>\n",
       "      <td>BE</td>\n",
       "      <td>BE</td>\n",
       "      <td>BE</td>\n",
       "      <td>TE</td>\n",
       "      <td>BE</td>\n",
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       "    <tr>\n",
       "      <th>marks</th>\n",
       "      <td>56.77</td>\n",
       "      <td>59.77</td>\n",
       "      <td>76.88</td>\n",
       "      <td>69.66</td>\n",
       "      <td>63.28</td>\n",
       "      <td>49.55</td>\n",
       "      <td>65.34</td>\n",
       "      <td>68.33</td>\n",
       "      <td>56.75</td>\n",
       "      <td>78.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>22</td>\n",
       "      <td>21</td>\n",
       "      <td>19</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>19</td>\n",
       "      <td>23</td>\n",
       "      <td>20</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           0      1       2        3      4       5      6      7      8  \\\n",
       "roll       1      2       3        4      5       6      7      8      9   \n",
       "name    anil   amit  aniket  ajinkya   asha  ayesha   amar  amita   amol   \n",
       "class     TE     TE      BE       TE     TE      BE     BE     BE     TE   \n",
       "marks  56.77  59.77   76.88    69.66  63.28   49.55  65.34  68.33  56.75   \n",
       "age       22     21      19       20     20      20     19     23     20   \n",
       "\n",
       "           9  \n",
       "roll      10  \n",
       "name   anmol  \n",
       "class     BE  \n",
       "marks  78.66  \n",
       "age       21  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "7662626a-c6d4-4afd-90f3-54af1d4bb5fb",
   "metadata": {},
   "outputs": [
    {
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       "      <td>22</td>\n",
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       "    <tr>\n",
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       "      <td>21</td>\n",
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       "      <th>2</th>\n",
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       "      <td>aniket</td>\n",
       "      <td>BE</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>asha</td>\n",
       "      <td>TE</td>\n",
       "      <td>63.28</td>\n",
       "      <td>20</td>\n",
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       "   roll     name class  marks  age\n",
       "0     1     anil    TE  56.77   22\n",
       "1     2     amit    TE  59.77   21\n",
       "2     3   aniket    BE  76.88   19\n",
       "3     4  ajinkya    TE  69.66   20\n",
       "4     5     asha    TE  63.28   20"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "c6871844-6e87-4656-9a7c-2ddf07969fc8",
   "metadata": {},
   "outputs": [
    {
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>amit</td>\n",
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       "      <td>59.77</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
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      "text/plain": [
       "   roll    name class  marks  age\n",
       "0     1    anil    TE  56.77   22\n",
       "1     2    amit    TE  59.77   21\n",
       "2     3  aniket    BE  76.88   19"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "cb04fdd1-74de-436f-bf3d-d277913471ed",
   "metadata": {},
   "outputs": [
    {
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       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>anmol</td>\n",
       "      <td>BE</td>\n",
       "      <td>78.66</td>\n",
       "      <td>21</td>\n",
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      ],
      "text/plain": [
       "   roll    name class  marks  age\n",
       "5     6  ayesha    BE  49.55   20\n",
       "6     7    amar    BE  65.34   19\n",
       "7     8   amita    BE  68.33   23\n",
       "8     9    amol    TE  56.75   20\n",
       "9    10   anmol    BE  78.66   21"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "cb7232ac-781c-4a7b-8f17-57f01bf49992",
   "metadata": {},
   "outputs": [
    {
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       "      <td>amita</td>\n",
       "      <td>BE</td>\n",
       "      <td>68.33</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>amol</td>\n",
       "      <td>TE</td>\n",
       "      <td>56.75</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>anmol</td>\n",
       "      <td>BE</td>\n",
       "      <td>78.66</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   roll   name class  marks  age\n",
       "7     8  amita    BE  68.33   23\n",
       "8     9   amol    TE  56.75   20\n",
       "9    10  anmol    BE  78.66   21"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1dd3b957-c11f-4fb1-bf06-bc01d918715b",
   "metadata": {},
   "source": [
    "##### Index Based Slicing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "94499691-aa62-4f36-9902-6670b4971c43",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>class</th>\n",
       "      <th>marks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>aniket</td>\n",
       "      <td>BE</td>\n",
       "      <td>76.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ajinkya</td>\n",
       "      <td>TE</td>\n",
       "      <td>69.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>asha</td>\n",
       "      <td>TE</td>\n",
       "      <td>63.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ayesha</td>\n",
       "      <td>BE</td>\n",
       "      <td>49.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>amar</td>\n",
       "      <td>BE</td>\n",
       "      <td>65.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>amita</td>\n",
       "      <td>BE</td>\n",
       "      <td>68.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      name class  marks\n",
       "2   aniket    BE  76.88\n",
       "3  ajinkya    TE  69.66\n",
       "4     asha    TE  63.28\n",
       "5   ayesha    BE  49.55\n",
       "6     amar    BE  65.34\n",
       "7    amita    BE  68.33"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = df.iloc[2:8,1:4]\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "a270abaa-40b1-4915-bafa-68b30fc339eb",
   "metadata": {},
   "outputs": [
    {
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       "      <th>name</th>\n",
       "      <th>class</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>anil</td>\n",
       "      <td>TE</td>\n",
       "      <td>56.77</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>amit</td>\n",
       "      <td>TE</td>\n",
       "      <td>59.77</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>aniket</td>\n",
       "      <td>BE</td>\n",
       "      <td>76.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ajinkya</td>\n",
       "      <td>TE</td>\n",
       "      <td>69.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>asha</td>\n",
       "      <td>TE</td>\n",
       "      <td>63.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ayesha</td>\n",
       "      <td>BE</td>\n",
       "      <td>49.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>amar</td>\n",
       "      <td>BE</td>\n",
       "      <td>65.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>amita</td>\n",
       "      <td>BE</td>\n",
       "      <td>68.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      name class  marks\n",
       "0     anil    TE  56.77\n",
       "1     amit    TE  59.77\n",
       "2   aniket    BE  76.88\n",
       "3  ajinkya    TE  69.66\n",
       "4     asha    TE  63.28\n",
       "5   ayesha    BE  49.55\n",
       "6     amar    BE  65.34\n",
       "7    amita    BE  68.33"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:8,1:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "b0375fdc-5655-4f8e-b27d-fceb0ebf0961",
   "metadata": {},
   "outputs": [
    {
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       "      <th>marks</th>\n",
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       "      <td>aniket</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ajinkya</td>\n",
       "      <td>TE</td>\n",
       "      <td>69.66</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>asha</td>\n",
       "      <td>TE</td>\n",
       "      <td>63.28</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ayesha</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>amol</td>\n",
       "      <td>TE</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>anmol</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "      name class  marks\n",
       "2   aniket    BE  76.88\n",
       "3  ajinkya    TE  69.66\n",
       "4     asha    TE  63.28\n",
       "5   ayesha    BE  49.55\n",
       "6     amar    BE  65.34\n",
       "7    amita    BE  68.33\n",
       "8     amol    TE  56.75\n",
       "9    anmol    BE  78.66"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[2:,1:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "16103bbc-dea6-4333-9f80-aa43e655101c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>19</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>amita</td>\n",
       "      <td>BE</td>\n",
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       "      <td>23</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
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       "      <td>20</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>anmol</td>\n",
       "      <td>BE</td>\n",
       "      <td>78.66</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "      name class  marks  age\n",
       "2   aniket    BE  76.88   19\n",
       "3  ajinkya    TE  69.66   20\n",
       "4     asha    TE  63.28   20\n",
       "5   ayesha    BE  49.55   20\n",
       "6     amar    BE  65.34   19\n",
       "7    amita    BE  68.33   23\n",
       "8     amol    TE  56.75   20\n",
       "9    anmol    BE  78.66   21"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[2:,1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "da6dbdff-9b79-4612-9da2-adf2d42c70d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>5</th>\n",
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       "</div>"
      ],
      "text/plain": [
       "   roll     name class  marks  age\n",
       "3     4  ajinkya    TE  69.66   20\n",
       "7     8    amita    BE  68.33   23\n",
       "5     6   ayesha    BE  49.55   20\n",
       "9    10    anmol    BE  78.66   21"
      ]
     },
     "execution_count": 61,
     "metadata": {},
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    }
   ],
   "source": [
    "df.iloc[[3,7,5,9],:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "24abe257-fa79-45b2-96e4-8af14e36d979",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "      name  marks\n",
       "3  ajinkya  69.66\n",
       "7    amita  68.33\n",
       "5   ayesha  49.55\n",
       "9    anmol  78.66"
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     "metadata": {},
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   ],
   "source": [
    "df.iloc[[3,7,5,9],[1,3]]"
   ]
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  {
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   "execution_count": 63,
   "id": "d6a5f4e5-2ed6-43bd-966c-85eefeac6800",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "roll         5\n",
       "name      asha\n",
       "class       TE\n",
       "marks    63.28\n",
       "age         20\n",
       "Name: 4, dtype: object"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[4,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "4f3134b7-ad88-4597-bd7a-ee735185d8e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       anil\n",
       "1       amit\n",
       "2     aniket\n",
       "3    ajinkya\n",
       "4       asha\n",
       "5     ayesha\n",
       "6       amar\n",
       "7      amita\n",
       "8       amol\n",
       "9      anmol\n",
       "Name: name, dtype: object"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "47726160-ba53-4a27-8853-dc5ce3daecdd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ajinkya'"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[3,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14b4220e-7df9-43fc-91b8-58a756cca921",
   "metadata": {},
   "source": [
    "###### Label Based Slicing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "b1e74817-f61e-426c-9e6e-ab962d9d4952",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       anil\n",
       "1       amit\n",
       "2     aniket\n",
       "3    ajinkya\n",
       "4       asha\n",
       "5     ayesha\n",
       "6       amar\n",
       "7      amita\n",
       "8       amol\n",
       "9      anmol\n",
       "Name: name, dtype: object"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:,'name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "024a88ea-6e08-4513-a867-2e6e4d668f1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>marks</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <th>3</th>\n",
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       "      <td>69.66</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>asha</td>\n",
       "      <td>63.28</td>\n",
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       "      <td>amar</td>\n",
       "      <td>65.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>amita</td>\n",
       "      <td>68.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>amol</td>\n",
       "      <td>56.75</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>anmol</td>\n",
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       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      name  marks\n",
       "0     anil  56.77\n",
       "1     amit  59.77\n",
       "2   aniket  76.88\n",
       "3  ajinkya  69.66\n",
       "4     asha  63.28\n",
       "5   ayesha  49.55\n",
       "6     amar  65.34\n",
       "7    amita  68.33\n",
       "8     amol  56.75\n",
       "9    anmol  78.66"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:,['name','marks']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "0ed90fe3-dd24-4e9a-b696-ecb2e9c7b592",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       anil\n",
       "1       amit\n",
       "2     aniket\n",
       "3    ajinkya\n",
       "4       asha\n",
       "5     ayesha\n",
       "6       amar\n",
       "7      amita\n",
       "8       amol\n",
       "9      anmol\n",
       "Name: name, dtype: object"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "3c6d5b3d-6d34-4fbf-98de-c67a498588fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>68.33</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>amol</td>\n",
       "      <td>56.75</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
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      ],
      "text/plain": [
       "      name  marks\n",
       "0     anil  56.77\n",
       "1     amit  59.77\n",
       "2   aniket  76.88\n",
       "3  ajinkya  69.66\n",
       "4     asha  63.28\n",
       "5   ayesha  49.55\n",
       "6     amar  65.34\n",
       "7    amita  68.33\n",
       "8     amol  56.75\n",
       "9    anmol  78.66"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['name','marks']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "a8a36b82-5ffa-48e2-bef7-1c340c2923c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       anil\n",
       "1       amit\n",
       "2     aniket\n",
       "3    ajinkya\n",
       "4       asha\n",
       "5     ayesha\n",
       "6       amar\n",
       "7      amita\n",
       "8       amol\n",
       "9      anmol\n",
       "Name: name, dtype: object"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "0458d5b7-036d-4c45-8fbb-a11add0cfbad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['anil', 'amit', 'aniket', 'ajinkya', 'asha', 'ayesha', 'amar',\n",
       "       'amita', 'amol', 'anmol'], dtype=object)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.name.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "ca8ed75e-e54d-47bd-816f-336ff6837d73",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "   roll     name class  marks  age\n",
       "0     1     anil    TE  56.77   22\n",
       "1     2     amit    TE  59.77   21\n",
       "2     3   aniket    BE  76.88   19\n",
       "3     4  ajinkya    TE  69.66   20\n",
       "4     5     asha    TE  63.28   20\n",
       "6     7     amar    BE  65.34   19\n",
       "7     8    amita    BE  68.33   23\n",
       "8     9     amol    TE  56.75   20\n",
       "9    10    anmol    BE  78.66   21"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "224986c3-756b-4db7-b1dc-94894fa01c72",
   "metadata": {},
   "outputs": [
    {
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       "   roll     name class  marks  age\n",
       "0     1     anil    TE  56.77   22\n",
       "1     2     amit    TE  59.77   21\n",
       "2     3   aniket    BE  76.88   19\n",
       "3     4  ajinkya    TE  69.66   20\n",
       "6     7     amar    BE  65.34   19\n",
       "7     8    amita    BE  68.33   23\n",
       "8     9     amol    TE  56.75   20"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop([5,4,9])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "a438a253-c68e-4b22-977a-aee7485fdb37",
   "metadata": {},
   "outputs": [
    {
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       "</div>"
      ],
      "text/plain": [
       "   roll     name  marks  age\n",
       "0     1     anil  56.77   22\n",
       "1     2     amit  59.77   21\n",
       "2     3   aniket  76.88   19\n",
       "3     4  ajinkya  69.66   20\n",
       "4     5     asha  63.28   20\n",
       "5     6   ayesha  49.55   20\n",
       "6     7     amar  65.34   19\n",
       "7     8    amita  68.33   23\n",
       "8     9     amol  56.75   20\n",
       "9    10    anmol  78.66   21"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop('class', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "5c3b8682-4230-4337-9e2e-e8879014c69d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    .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>roll</th>\n",
       "      <th>name</th>\n",
       "      <th>marks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>anil</td>\n",
       "      <td>56.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>amit</td>\n",
       "      <td>59.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>aniket</td>\n",
       "      <td>76.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>ajinkya</td>\n",
       "      <td>69.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>asha</td>\n",
       "      <td>63.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>ayesha</td>\n",
       "      <td>49.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>amar</td>\n",
       "      <td>65.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>amita</td>\n",
       "      <td>68.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>amol</td>\n",
       "      <td>56.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>anmol</td>\n",
       "      <td>78.66</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   roll     name  marks\n",
       "0     1     anil  56.77\n",
       "1     2     amit  59.77\n",
       "2     3   aniket  76.88\n",
       "3     4  ajinkya  69.66\n",
       "4     5     asha  63.28\n",
       "5     6   ayesha  49.55\n",
       "6     7     amar  65.34\n",
       "7     8    amita  68.33\n",
       "8     9     amol  56.75\n",
       "9    10    anmol  78.66"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(['class','age'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "6d8e781d-2915-4e4d-aa25-31476c15ddfd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 5)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "e4152c6a-57b1-417a-ad08-44b26508bf88",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['class','age'], axis = 1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "6d7a9293-21d6-4d90-a030-53404d4f086f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 3)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ebd67a8-4871-4d7e-8e40-a0f775266c14",
   "metadata": {},
   "source": [
    "###### Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "f015c911-830a-4385-8169-9998823ce2a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49.55"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['marks'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "7f3324d9-63d7-4a93-91be-1d195e6ba936",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "78.66"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['marks'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "5ea723f3-6541-401f-ad4b-f2d45e4fc7f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "64.499"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['marks'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "057747c2-a975-499e-a699-a0ac656d4cfd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "64.31"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['marks'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "3fd4605c-fa1a-4ff9-b37f-27dc5f2ece97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "644.9899999999999"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['marks'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "e92a7b84-fb55-4993-ae65-5d9f4e8bfdff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.207178901994535"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['marks'].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "668c2bbc-930b-4259-968b-0255c8cf8f58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "roll     10\n",
       "name     10\n",
       "marks    10\n",
       "dtype: int64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "c854ad93-90be-4b3b-aa32-970800e9b389",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('datasets/student.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "9b95183f-9481-4c1f-9cae-7a4b689a69ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "roll     10\n",
       "name     10\n",
       "class    10\n",
       "marks    10\n",
       "age      10\n",
       "dtype: int64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "7682f7c1-f2d7-4a95-be67-a294c42db366",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10 entries, 0 to 9\n",
      "Data columns (total 5 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   roll    10 non-null     int64  \n",
      " 1   name    10 non-null     object \n",
      " 2   class   10 non-null     object \n",
      " 3   marks   10 non-null     float64\n",
      " 4   age     10 non-null     int64  \n",
      "dtypes: float64(1), int64(2), object(2)\n",
      "memory usage: 528.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "73835f8d-d68b-4f5f-8190-b643acef8208",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>roll</th>\n",
       "      <th>marks</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10.00000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.50000</td>\n",
       "      <td>64.499000</td>\n",
       "      <td>20.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.02765</td>\n",
       "      <td>9.207179</td>\n",
       "      <td>1.269296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>49.550000</td>\n",
       "      <td>19.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.25000</td>\n",
       "      <td>57.520000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.50000</td>\n",
       "      <td>64.310000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.75000</td>\n",
       "      <td>69.327500</td>\n",
       "      <td>21.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10.00000</td>\n",
       "      <td>78.660000</td>\n",
       "      <td>23.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           roll      marks        age\n",
       "count  10.00000  10.000000  10.000000\n",
       "mean    5.50000  64.499000  20.500000\n",
       "std     3.02765   9.207179   1.269296\n",
       "min     1.00000  49.550000  19.000000\n",
       "25%     3.25000  57.520000  20.000000\n",
       "50%     5.50000  64.310000  20.000000\n",
       "75%     7.75000  69.327500  21.000000\n",
       "max    10.00000  78.660000  23.000000"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "2020cfdd-68a3-4321-af39-516f4b1d7b54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "roll         10\n",
       "name     ayesha\n",
       "class        TE\n",
       "marks     78.66\n",
       "age          23\n",
       "dtype: object"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "ddb13f88-d00c-4dcc-929e-d59099e86f12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "roll                                                    55\n",
       "name     anilamitaniketajinkyaashaayeshaamaramitaamolanmol\n",
       "class                                 TETEBETETEBEBEBETEBE\n",
       "marks                                               644.99\n",
       "age                                                    205\n",
       "dtype: object"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "a6ed9232-6df1-4362-82cd-878a75262288",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "roll     3.027650\n",
       "marks    9.207179\n",
       "age      1.269296\n",
       "dtype: float64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.std(numeric_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "d60954be-8fce-4947-a40a-826d647bcb42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "roll                                                    55\n",
       "name     anilamitaniketajinkyaashaayeshaamaramitaamolanmol\n",
       "class                                 TETEBETETEBEBEBETEBE\n",
       "marks                                               644.99\n",
       "age                                                    205\n",
       "dtype: object"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "1c091bfa-8962-4de0-ae1d-9269046612e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     79.77\n",
       "1     82.77\n",
       "2     98.88\n",
       "3     93.66\n",
       "4     88.28\n",
       "5     75.55\n",
       "6     91.34\n",
       "7     99.33\n",
       "8     85.75\n",
       "9    109.66\n",
       "dtype: float64"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sum(axis=1, numeric_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "c723c33f-b31b-436b-ae95-5e3c439a9190",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <td>22</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>aniket</td>\n",
       "      <td>BE</td>\n",
       "      <td>76.88</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>ajinkya</td>\n",
       "      <td>TE</td>\n",
       "      <td>69.66</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>asha</td>\n",
       "      <td>TE</td>\n",
       "      <td>63.28</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>ayesha</td>\n",
       "      <td>BE</td>\n",
       "      <td>49.55</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>amar</td>\n",
       "      <td>BE</td>\n",
       "      <td>65.34</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>amita</td>\n",
       "      <td>BE</td>\n",
       "      <td>68.33</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>amol</td>\n",
       "      <td>TE</td>\n",
       "      <td>56.75</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>anmol</td>\n",
       "      <td>BE</td>\n",
       "      <td>78.66</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   roll     name class  marks  age\n",
       "0     1     anil    TE  56.77   22\n",
       "1     2     amit    TE  59.77   21\n",
       "2     3   aniket    BE  76.88   19\n",
       "3     4  ajinkya    TE  69.66   20\n",
       "4     5     asha    TE  63.28   20\n",
       "5     6   ayesha    BE  49.55   20\n",
       "6     7     amar    BE  65.34   19\n",
       "7     8    amita    BE  68.33   23\n",
       "8     9     amol    TE  56.75   20\n",
       "9    10    anmol    BE  78.66   21"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "e8d931e0-92ed-426c-81bd-5cb70a48b731",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "class\n",
       "TE    5\n",
       "BE    5\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['class'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "a7ac1b4d-a3ca-4588-a596-0532be9df35a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age\n",
       "20    4\n",
       "21    2\n",
       "19    2\n",
       "22    1\n",
       "23    1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['age'].value_counts()"
   ]
  },
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   "cell_type": "code",
   "execution_count": 103,
   "id": "35d109b1-1228-48c5-997d-fe2650f327b7",
   "metadata": {},
   "outputs": [
    {
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       "   roll     name class  marks  age\n",
       "2     3   aniket    BE  76.88   19\n",
       "5     6   ayesha    BE  49.55   20\n",
       "6     7     amar    BE  65.34   19\n",
       "7     8    amita    BE  68.33   23\n",
       "9    10    anmol    BE  78.66   21\n",
       "0     1     anil    TE  56.77   22\n",
       "1     2     amit    TE  59.77   21\n",
       "3     4  ajinkya    TE  69.66   20\n",
       "4     5     asha    TE  63.28   20\n",
       "8     9     amol    TE  56.75   20"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values('class')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "6aacbace-86ee-4ee5-b70f-840f62a117cc",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   roll     name class  marks  age\n",
       "0     1     anil    TE  56.77   22\n",
       "1     2     amit    TE  59.77   21\n",
       "3     4  ajinkya    TE  69.66   20\n",
       "4     5     asha    TE  63.28   20\n",
       "8     9     amol    TE  56.75   20\n",
       "2     3   aniket    BE  76.88   19\n",
       "5     6   ayesha    BE  49.55   20\n",
       "6     7     amar    BE  65.34   19\n",
       "7     8    amita    BE  68.33   23\n",
       "9    10    anmol    BE  78.66   21"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values('class', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "5669b4d0-bebf-461c-9807-5b06e982403d",
   "metadata": {},
   "outputs": [
    {
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       "      <th>4</th>\n",
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       "      <th>3</th>\n",
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       "      <td>ajinkya</td>\n",
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      ],
      "text/plain": [
       "   roll     name class  marks  age\n",
       "5     6   ayesha    BE  49.55   20\n",
       "6     7     amar    BE  65.34   19\n",
       "7     8    amita    BE  68.33   23\n",
       "2     3   aniket    BE  76.88   19\n",
       "9    10    anmol    BE  78.66   21\n",
       "8     9     amol    TE  56.75   20\n",
       "0     1     anil    TE  56.77   22\n",
       "1     2     amit    TE  59.77   21\n",
       "4     5     asha    TE  63.28   20\n",
       "3     4  ajinkya    TE  69.66   20"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(['class','marks'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3036a782-ca9e-48b0-8898-f44b4b64d2d8",
   "metadata": {},
   "source": [
    "###### Custom Column Operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "b7726b30-4ba8-4d50-88ef-df5b35b423f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    57.27\n",
       "1    60.27\n",
       "2    77.38\n",
       "3    70.16\n",
       "4    63.78\n",
       "5    50.05\n",
       "6    65.84\n",
       "7    68.83\n",
       "8    57.25\n",
       "9    79.16\n",
       "Name: marks, dtype: float64"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['marks'] + 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "2dd31ce6-fdfc-4759-ac1f-4a16836a043e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['marks'] = df['marks'] + 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "625071a7-631f-4a45-9151-58f88da76dc7",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   roll     name class  marks  age\n",
       "0     1     anil    TE  57.27   22\n",
       "1     2     amit    TE  60.27   21\n",
       "2     3   aniket    BE  77.38   19\n",
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       "4     5     asha    TE  63.78   20\n",
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       "6     7     amar    BE  65.84   19\n",
       "7     8    amita    BE  68.83   23\n",
       "8     9     amol    TE  57.25   20\n",
       "9    10    anmol    BE  79.16   21"
      ]
     },
     "execution_count": 111,
     "metadata": {},
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  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "1ae3da81-ce45-4b9a-a2ea-0503c2ccaacc",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['cgpa'] = df['marks'] / 10 + 0.034"
   ]
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  {
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   "execution_count": 113,
   "id": "b04c632b-ce6a-4cf2-a0a2-652d55efd7a3",
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       "   roll     name class  marks  age   cgpa\n",
       "0     1     anil    TE  57.27   22  5.761\n",
       "1     2     amit    TE  60.27   21  6.061\n",
       "2     3   aniket    BE  77.38   19  7.772\n",
       "3     4  ajinkya    TE  70.16   20  7.050\n",
       "4     5     asha    TE  63.78   20  6.412\n",
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       "6     7     amar    BE  65.84   19  6.618\n",
       "7     8    amita    BE  68.83   23  6.917\n",
       "8     9     amol    TE  57.25   20  5.759\n",
       "9    10    anmol    BE  79.16   21  7.950"
      ]
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   "execution_count": 114,
   "id": "e593f594-9e12-4f34-98d6-8fbd24b3b188",
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    {
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      "text/plain": [
       "   roll     name class  marks  age   cgpa  first_class\n",
       "0     1     anil    TE  57.27   22  5.761        False\n",
       "1     2     amit    TE  60.27   21  6.061         True\n",
       "2     3   aniket    BE  77.38   19  7.772         True\n",
       "3     4  ajinkya    TE  70.16   20  7.050         True\n",
       "4     5     asha    TE  63.78   20  6.412         True\n",
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       "6     7     amar    BE  65.84   19  6.618         True\n",
       "7     8    amita    BE  68.83   23  6.917         True\n",
       "8     9     amol    TE  57.25   20  5.759        False\n",
       "9    10    anmol    BE  79.16   21  7.950         True"
      ]
     },
     "execution_count": 114,
     "metadata": {},
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   "source": [
    "df['first_class'] = df['marks'] > 60\n",
    "df"
   ]
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  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "b2cf7cba-fb5a-462a-9731-b7ff9d920e08",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = ['M','M','M','M','F','M','F','F','M','M']"
   ]
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   "execution_count": 117,
   "id": "e001b467-dc2c-4098-a95b-8466a92f5140",
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    {
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       "   roll     name class  marks  age   cgpa  first_class gender\n",
       "0     1     anil    TE  57.27   22  5.761        False      M\n",
       "1     2     amit    TE  60.27   21  6.061         True      M\n",
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       "4     5     asha    TE  63.78   20  6.412         True      F\n",
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       "6     7     amar    BE  65.84   19  6.618         True      F\n",
       "7     8    amita    BE  68.83   23  6.917         True      F\n",
       "8     9     amol    TE  57.25   20  5.759        False      M\n",
       "9    10    anmol    BE  79.16   21  7.950         True      M"
      ]
     },
     "execution_count": 117,
     "metadata": {},
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   "source": [
    "df['gender'] = x\n",
    "df"
   ]
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  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "b47413a7-a2b2-4da6-b713-b969345efe3f",
   "metadata": {},
   "outputs": [
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       "      <td>6.618</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>6.917</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>5.759</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>7.950</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   roll   cgpa gender\n",
       "0     1  5.761      M\n",
       "1     2  6.061      M\n",
       "2     3  7.772      M\n",
       "3     4  7.050      M\n",
       "4     5  6.412      F\n",
       "5     6  5.039      M\n",
       "6     7  6.618      F\n",
       "7     8  6.917      F\n",
       "8     9  5.759      M\n",
       "9    10  7.950      M"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ndf = pd.DataFrame({\n",
    "    'roll':range(1,11),\n",
    "    'cgpa': df['marks'] / 10 + 0.034,\n",
    "    'gender': ['M','M','M','M','F','M','F','F','M','M']\n",
    "})\n",
    "\n",
    "ndf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "17fc7f6b-e309-41c1-94a1-7cce726c2b25",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('datasets/student.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "05ba1c1d-b88f-434d-8bf3-adbe406a8d60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['roll', 'name', 'class', 'marks', 'age'], dtype='object')"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "9b0040e9-495c-4da7-a29a-955ec9ee2585",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['roll', 'cgpa', 'gender'], dtype='object')"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ndf.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "cf7a4bfd-a885-4fc2-905b-0d021d1a019f",
   "metadata": {},
   "outputs": [
    {
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       "      <td>21</td>\n",
       "      <td>7.950</td>\n",
       "      <td>M</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "   roll     name class  marks  age   cgpa gender\n",
       "0     1     anil    TE  56.77   22  5.761      M\n",
       "1     2     amit    TE  59.77   21  6.061      M\n",
       "2     3   aniket    BE  76.88   19  7.772      M\n",
       "3     4  ajinkya    TE  69.66   20  7.050      M\n",
       "4     5     asha    TE  63.28   20  6.412      F\n",
       "5     6   ayesha    BE  49.55   20  5.039      M\n",
       "6     7     amar    BE  65.34   19  6.618      F\n",
       "7     8    amita    BE  68.33   23  6.917      F\n",
       "8     9     amol    TE  56.75   20  5.759      M\n",
       "9    10    anmol    BE  78.66   21  7.950      M"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.merge(ndf, on='roll')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "7c45ad18-6c43-43eb-ba0b-45dc199f60f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df5 = pd.read_csv('datasets/student5.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "7c941696-c656-4537-9a89-20378ce7c3e1",
   "metadata": {},
   "outputs": [
    {
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       "4     5  shalini    TE  63.45   20"
      ]
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     "execution_count": 126,
     "metadata": {},
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   "cell_type": "code",
   "execution_count": 127,
   "id": "fb05ffa1-e23c-4c6c-b3b7-e5cc27a5b489",
   "metadata": {},
   "outputs": [
    {
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       "   roll     name class  marks  age\n",
       "0     1     anil    TE  56.77   22\n",
       "1     2     amit    TE  59.77   21\n",
       "2     3   aniket    BE  76.88   19\n",
       "3     4  ajinkya    TE  69.66   20\n",
       "4     5     asha    TE  63.28   20\n",
       "5     6   ayesha    BE  49.55   20\n",
       "6     7     amar    BE  65.34   19\n",
       "7     8    amita    BE  68.33   23\n",
       "8     9     amol    TE  56.75   20\n",
       "9    10    anmol    BE  78.66   21\n",
       "0     1   surbhi    TE  67.44   23\n",
       "1     2    sujay    TE  74.12   22\n",
       "2     3  subhash    BE  82.44   21\n",
       "3     4   sachin    TE  59.33   21\n",
       "4     5  shalini    TE  63.45   20"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([df, df5])"
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  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "2c249370-87da-4db9-b145-4a2a4a153917",
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   "outputs": [
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       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>amar</td>\n",
       "      <td>BE</td>\n",
       "      <td>65.34</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>amita</td>\n",
       "      <td>BE</td>\n",
       "      <td>68.33</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>amol</td>\n",
       "      <td>TE</td>\n",
       "      <td>56.75</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>anmol</td>\n",
       "      <td>BE</td>\n",
       "      <td>78.66</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>surbhi</td>\n",
       "      <td>TE</td>\n",
       "      <td>67.44</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2</td>\n",
       "      <td>sujay</td>\n",
       "      <td>TE</td>\n",
       "      <td>74.12</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3</td>\n",
       "      <td>subhash</td>\n",
       "      <td>BE</td>\n",
       "      <td>82.44</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4</td>\n",
       "      <td>sachin</td>\n",
       "      <td>TE</td>\n",
       "      <td>59.33</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5</td>\n",
       "      <td>shalini</td>\n",
       "      <td>TE</td>\n",
       "      <td>63.45</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    roll     name class  marks  age\n",
       "0      1     anil    TE  56.77   22\n",
       "1      2     amit    TE  59.77   21\n",
       "2      3   aniket    BE  76.88   19\n",
       "3      4  ajinkya    TE  69.66   20\n",
       "4      5     asha    TE  63.28   20\n",
       "5      6   ayesha    BE  49.55   20\n",
       "6      7     amar    BE  65.34   19\n",
       "7      8    amita    BE  68.33   23\n",
       "8      9     amol    TE  56.75   20\n",
       "9     10    anmol    BE  78.66   21\n",
       "10     1   surbhi    TE  67.44   23\n",
       "11     2    sujay    TE  74.12   22\n",
       "12     3  subhash    BE  82.44   21\n",
       "13     4   sachin    TE  59.33   21\n",
       "14     5  shalini    TE  63.45   20"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([df, df5], ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de00d651-11b5-478d-9051-267d68afbbb2",
   "metadata": {},
   "source": [
    "###### String operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "e5b3fc60-f5aa-4ed5-9eec-f34b19658f76",
   "metadata": {},
   "outputs": [],
   "source": [
    "name = 'Tushar'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "1259a72b-5873-4f77-bd5b-f2baec438a69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'tushar'"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "cdd8b150-c47e-405b-bcfd-dd622b3b6c68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Tooshar'"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name.replace('u','oo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "f001761e-d035-4d6a-9f86-7c00c6edbea0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       ANIL\n",
       "1       AMIT\n",
       "2     ANIKET\n",
       "3    AJINKYA\n",
       "4       ASHA\n",
       "5     AYESHA\n",
       "6       AMAR\n",
       "7      AMITA\n",
       "8       AMOL\n",
       "9      ANMOL\n",
       "Name: name, dtype: object"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['name'].str.upper()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "ac64050d-7e9f-4939-80bc-62578f04524a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       Anil\n",
       "1       Amit\n",
       "2     Aniket\n",
       "3    Ajinkya\n",
       "4       Asha\n",
       "5     Ayesha\n",
       "6       Amar\n",
       "7      Amita\n",
       "8       Amol\n",
       "9      Anmol\n",
       "Name: name, dtype: object"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['name'].str.title()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "7e470d1b-6530-4839-b6b1-8f5d1df699e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     True\n",
       "1    False\n",
       "2    False\n",
       "3    False\n",
       "4    False\n",
       "5    False\n",
       "6    False\n",
       "7    False\n",
       "8     True\n",
       "9     True\n",
       "Name: name, dtype: bool"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['name'].str.endswith('l')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "c6ca2580-bda0-4847-8775-a65618e556c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     True\n",
       "1    False\n",
       "2     True\n",
       "3    False\n",
       "4    False\n",
       "5    False\n",
       "6    False\n",
       "7    False\n",
       "8    False\n",
       "9     True\n",
       "Name: name, dtype: bool"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['name'].str.startswith('an')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "6358e778-deb9-4b5e-86aa-978c4c08f53e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       aneel\n",
       "1       ameet\n",
       "2     aneeket\n",
       "3    ajeenkya\n",
       "4        asha\n",
       "5      ayesha\n",
       "6        amar\n",
       "7      ameeta\n",
       "8        amol\n",
       "9       anmol\n",
       "Name: name, dtype: object"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['name'].str.replace('i','ee')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "4cde1e46-1f63-4d9b-bd89-dcda092f3f39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    1\n",
       "2    1\n",
       "3    2\n",
       "4    2\n",
       "5    2\n",
       "6    2\n",
       "7    2\n",
       "8    1\n",
       "9    1\n",
       "Name: name, dtype: int64"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['name'].str.count('a')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "27500df1-9a2a-47c5-8f7c-f0531ba17ed8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2\n",
       "1    2\n",
       "2    2\n",
       "3    2\n",
       "4   -1\n",
       "5   -1\n",
       "6   -1\n",
       "7    2\n",
       "8   -1\n",
       "9   -1\n",
       "Name: name, dtype: int64"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['name'].str.find('i')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "3393ca2c-381b-49f7-9106-5aa7be834b11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    True\n",
       "1    True\n",
       "2    True\n",
       "3    True\n",
       "4    True\n",
       "5    True\n",
       "6    True\n",
       "7    True\n",
       "8    True\n",
       "9    True\n",
       "Name: name, dtype: bool"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['name'].str.isalpha()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b45e4a8b-a521-4107-a55d-729dc69dd74e",
   "metadata": {},
   "source": [
    "###### Grouping and Aggregation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "b283f45a-eef7-4b4b-a08e-ea4ca74666ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "gr = df.groupby('class')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "0b7b9ad4-3537-43cb-a130-5a6be919b198",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'BE': [2, 5, 6, 7, 9], 'TE': [0, 1, 3, 4, 8]}"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gr.groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "286d448b-e6a3-4964-b6b5-46f563eb9b47",
   "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>roll</th>\n",
       "      <th>name</th>\n",
       "      <th>class</th>\n",
       "      <th>marks</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>anil</td>\n",
       "      <td>TE</td>\n",
       "      <td>56.77</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>amit</td>\n",
       "      <td>TE</td>\n",
       "      <td>59.77</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>ajinkya</td>\n",
       "      <td>TE</td>\n",
       "      <td>69.66</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>asha</td>\n",
       "      <td>TE</td>\n",
       "      <td>63.28</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>amol</td>\n",
       "      <td>TE</td>\n",
       "      <td>56.75</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   roll     name class  marks  age\n",
       "0     1     anil    TE  56.77   22\n",
       "1     2     amit    TE  59.77   21\n",
       "3     4  ajinkya    TE  69.66   20\n",
       "4     5     asha    TE  63.28   20\n",
       "8     9     amol    TE  56.75   20"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gr.get_group('TE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "bf746a01-7013-4128-a723-57871159997b",
   "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>roll</th>\n",
       "      <th>name</th>\n",
       "      <th>marks</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>class</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BE</th>\n",
       "      <td>3</td>\n",
       "      <td>amar</td>\n",
       "      <td>49.55</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TE</th>\n",
       "      <td>1</td>\n",
       "      <td>ajinkya</td>\n",
       "      <td>56.75</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       roll     name  marks  age\n",
       "class                           \n",
       "BE        3     amar  49.55   19\n",
       "TE        1  ajinkya  56.75   20"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gr.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "01a2db8e-8bb7-479c-92de-e0b584da5486",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "class\n",
       "BE    78.66\n",
       "TE    69.66\n",
       "Name: marks, dtype: float64"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gr['marks'].max()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f53d073-61c5-4c6f-9610-c579d99cf7b5",
   "metadata": {},
   "source": [
    "##### Boolean Filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "b80b6b3d-f0be-4521-b986-be658d3ddf5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2     True\n",
       "3     True\n",
       "4     True\n",
       "5    False\n",
       "6     True\n",
       "7     True\n",
       "8    False\n",
       "9     True\n",
       "Name: marks, dtype: bool"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['marks'] > 60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "9859b809-3cef-4e8a-90db-1a1c46b8a29e",
   "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>roll</th>\n",
       "      <th>name</th>\n",
       "      <th>class</th>\n",
       "      <th>marks</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>aniket</td>\n",
       "      <td>BE</td>\n",
       "      <td>76.88</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>ajinkya</td>\n",
       "      <td>TE</td>\n",
       "      <td>69.66</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>asha</td>\n",
       "      <td>TE</td>\n",
       "      <td>63.28</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>amar</td>\n",
       "      <td>BE</td>\n",
       "      <td>65.34</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>amita</td>\n",
       "      <td>BE</td>\n",
       "      <td>68.33</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>anmol</td>\n",
       "      <td>BE</td>\n",
       "      <td>78.66</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   roll     name class  marks  age\n",
       "2     3   aniket    BE  76.88   19\n",
       "3     4  ajinkya    TE  69.66   20\n",
       "4     5     asha    TE  63.28   20\n",
       "6     7     amar    BE  65.34   19\n",
       "7     8    amita    BE  68.33   23\n",
       "9    10    anmol    BE  78.66   21"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask = df['marks'] > 60\n",
    "df[mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "535dd6df-ec22-4126-a5b1-6d1ca11094df",
   "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>roll</th>\n",
       "      <th>name</th>\n",
       "      <th>class</th>\n",
       "      <th>marks</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>aniket</td>\n",
       "      <td>BE</td>\n",
       "      <td>76.88</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>ajinkya</td>\n",
       "      <td>TE</td>\n",
       "      <td>69.66</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>asha</td>\n",
       "      <td>TE</td>\n",
       "      <td>63.28</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>amar</td>\n",
       "      <td>BE</td>\n",
       "      <td>65.34</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>amita</td>\n",
       "      <td>BE</td>\n",
       "      <td>68.33</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>anmol</td>\n",
       "      <td>BE</td>\n",
       "      <td>78.66</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   roll     name class  marks  age\n",
       "2     3   aniket    BE  76.88   19\n",
       "3     4  ajinkya    TE  69.66   20\n",
       "4     5     asha    TE  63.28   20\n",
       "6     7     amar    BE  65.34   19\n",
       "7     8    amita    BE  68.33   23\n",
       "9    10    anmol    BE  78.66   21"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['marks'] > 60]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "52a8141f-58ae-430c-8b89-6d64c8d3bf83",
   "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>roll</th>\n",
       "      <th>name</th>\n",
       "      <th>class</th>\n",
       "      <th>marks</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>anil</td>\n",
       "      <td>TE</td>\n",
       "      <td>56.77</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>aniket</td>\n",
       "      <td>BE</td>\n",
       "      <td>76.88</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>anmol</td>\n",
       "      <td>BE</td>\n",
       "      <td>78.66</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   roll    name class  marks  age\n",
       "0     1    anil    TE  56.77   22\n",
       "2     3  aniket    BE  76.88   19\n",
       "9    10   anmol    BE  78.66   21"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['name'].str.startswith('an')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "942ab606-7c6a-42ab-b488-dbc113b9a288",
   "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>roll</th>\n",
       "      <th>name</th>\n",
       "      <th>class</th>\n",
       "      <th>marks</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>aniket</td>\n",
       "      <td>BE</td>\n",
       "      <td>76.88</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>amar</td>\n",
       "      <td>BE</td>\n",
       "      <td>65.34</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>amita</td>\n",
       "      <td>BE</td>\n",
       "      <td>68.33</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>anmol</td>\n",
       "      <td>BE</td>\n",
       "      <td>78.66</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   roll    name class  marks  age\n",
       "2     3  aniket    BE  76.88   19\n",
       "6     7    amar    BE  65.34   19\n",
       "7     8   amita    BE  68.33   23\n",
       "9    10   anmol    BE  78.66   21"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = df[(df['marks'] > 60) & (df['class'] == 'BE')]\n",
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e16d9ff7-93c3-461a-a611-227c3fa80904",
   "metadata": {},
   "source": [
    "###### Export the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "c45d12c6-5245-4c54-a92c-e5a96256e363",
   "metadata": {},
   "outputs": [],
   "source": [
    "x.to_csv('datasets/output.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "e1f2403b-f88e-43ef-8485-8b0ce05e48af",
   "metadata": {},
   "outputs": [],
   "source": [
    "x.to_html('datasets/output.html', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ea37f03b-c3c3-4c60-994d-78389713c370",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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