13 Pandas_Analysis_17jun.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6ec14621-debf-4b0a-9681-5d8103e32233",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ac26410-72e3-4c9b-bde1-74149ca39011",
   "metadata": {},
   "source": [
    "## Series "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3293fc64-0679-4a3d-9e97-2a94217b7ae9",
   "metadata": {},
   "outputs": [],
   "source": [
    "s = pd.Series([12, 34, 56, 73, 23])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f9bc7802-277f-446f-83a0-9a312d405daf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    12\n",
      "1    34\n",
      "2    56\n",
      "3    73\n",
      "4    23\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c28dc12c-905e-408f-b3c3-5b585e53b300",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    12.00\n",
       "1    34.00\n",
       "2    56.00\n",
       "3    73.00\n",
       "4    23.00\n",
       "5    21.22\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series([12, 34, 56, 73, 23, 21.22])\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e95f2d87-f108-48b4-b8ad-b77d0d798838",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a9447b3e-09ff-4c31-8e53-1bae6da3c0d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "25364480-d177-47af-9578-bbe4758e516b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "73.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9edeb395-5758-4254-b2ce-de899ab31b3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "s[3] = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c5ba3c37-5c24-4695-b629-458282b81fc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     12.00\n",
       "1     34.00\n",
       "2     56.00\n",
       "3    100.00\n",
       "4     23.00\n",
       "5     21.22\n",
       "dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9a386832-7380-40f4-ade6-e5f237a7a7d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100    12.00\n",
       "101    34.00\n",
       "102    56.00\n",
       "103    73.00\n",
       "104    23.00\n",
       "105    21.22\n",
       "dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series([12, 34, 56, 73, 23, 21.22], index = range(100, 106))\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "10bb32c9-e36b-4fd4-b6f4-55ab247d0e83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "73.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[103]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cd808d8-b7b9-4634-bc63-38d088acf7eb",
   "metadata": {},
   "source": [
    "# Dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b76ec63d-c975-466f-9bb8-0dcf526ab569",
   "metadata": {},
   "outputs": [],
   "source": [
    "roll = pd.Series([1, 2, 3, 4, 5])\n",
    "name = pd.Series(['Karan', 'Aryan', 'Tejas', 'Tanvi', 'Anushka'])\n",
    "marks = pd.Series([67, 69, 71, 73, 75])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "73a6d62a-9575-4001-8c3e-8850f83e55c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({\n",
    "    'roll': roll,\n",
    "    'name': name,\n",
    "    'marks': marks\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "9f4b035d-36cc-446f-933c-b15b3ce91747",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>Karan</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Aryan</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Tejas</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Tanvi</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Anushka</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   roll     name  marks\n",
       "0     1    Karan     67\n",
       "1     2    Aryan     69\n",
       "2     3    Tejas     71\n",
       "3     4    Tanvi     73\n",
       "4     5  Anushka     75"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "fca80a6f-20f4-4b67-af5d-22786a279d23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 3)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "dbffca76-3402-400d-8fdb-27ec6b630e6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "2c26f157-45db-43dc-b420-ccc43ea3d304",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['roll', 'name', 'marks'], dtype='object')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "f7b3ef38-f978-4757-afb8-dae1c2a75ef1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/aditya/Internships/1/17Jun_Pandas'"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# current working directory\n",
    "\n",
    "import os\n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "3a74ed20-b0ec-4359-a176-56ee79dabd04",
   "metadata": {},
   "outputs": [],
   "source": [
    "# loaction: https://mitu.co.in/dataset\n",
    "# download all the files starting from student* and put them \n",
    "# in your current working directory"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f78e034-0c89-4771-98b7-156a79a1f27e",
   "metadata": {},
   "source": [
    "#### import the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "834c4eab-77e2-49c5-90a0-e1f335ad9afd",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('student.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "1ea480b6-4f98-4329-9e3f-0db7e31b58a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
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       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "d39e0cf4-4bdf-4c75-bd46-f23915e85f1a",
   "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",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <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",
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       "    <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",
       "</table>\n",
       "</div>"
      ],
      "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": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.read_csv('student1.csv')\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "eb247e16-67cb-43c8-9aeb-931d3e77362d",
   "metadata": {},
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</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": [
       "    A        B   C      D   E\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": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "headers = ['A', 'B', 'C', 'D', 'E']\n",
    "df1 = pd.read_csv('student1.csv', names = headers)\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "73b9303a-57c3-4ce3-80ff-a0269ba3b6f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>roll\\tname\\tclass\\tmarks\\tage</th>\n",
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       "      <td>10\\tanmol\\tBE\\t78.66\\t21</td>\n",
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      ],
      "text/plain": [
       "  roll\\tname\\tclass\\tmarks\\tage\n",
       "0        1\\tanil\\tTE\\t56.77\\t22\n",
       "1        2\\tamit\\tTE\\t59.77\\t21\n",
       "2      3\\taniket\\tBE\\t76.88\\t19\n",
       "3     4\\tajinkya\\tTE\\t69.66\\t20\n",
       "4        5\\tasha\\tTE\\t63.28\\t20\n",
       "5      6\\tayesha\\tBE\\t49.55\\t20\n",
       "6        7\\tamar\\tBE\\t65.34\\t19\n",
       "7       8\\tamita\\tBE\\t68.33\\t23\n",
       "8        9\\tamol\\tTE\\t56.75\\t20\n",
       "9      10\\tanmol\\tBE\\t78.66\\t21"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.read_csv('student3.tsv')\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "497cf200-947f-4ba7-b2be-2183d14e438c",
   "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>class</th>\n",
       "      <th>marks</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <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",
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       "    <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": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.read_csv('student3.tsv', sep = '\\t')\n",
    "df1`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "172cb104-19a2-4cb3-b0e1-75448b7002ab",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[64], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m df1 \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mread_csv(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhttps://gist.githubusercontent.com/netj/8836201/raw/6f9306ad21398ea43cba4f7d537619d0e07d5ae3/iris.csv\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m      2\u001b[0m df1\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/pandas/io/parsers/readers.py:1026\u001b[0m, in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[1;32m   1013\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[1;32m   1014\u001b[0m     dialect,\n\u001b[1;32m   1015\u001b[0m     delimiter,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1022\u001b[0m     dtype_backend\u001b[38;5;241m=\u001b[39mdtype_backend,\n\u001b[1;32m   1023\u001b[0m )\n\u001b[1;32m   1024\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[0;32m-> 1026\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _read(filepath_or_buffer, kwds)\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/pandas/io/parsers/readers.py:620\u001b[0m, in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    617\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[1;32m    619\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[0;32m--> 620\u001b[0m parser \u001b[38;5;241m=\u001b[39m TextFileReader(filepath_or_buffer, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[1;32m    622\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[1;32m    623\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m parser\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/pandas/io/parsers/readers.py:1620\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m   1617\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m   1619\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m-> 1620\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_make_engine(f, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine)\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/pandas/io/parsers/readers.py:1880\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[0;34m(self, f, engine)\u001b[0m\n\u001b[1;32m   1878\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[1;32m   1879\u001b[0m         mode \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m-> 1880\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;241m=\u001b[39m get_handle(\n\u001b[1;32m   1881\u001b[0m     f,\n\u001b[1;32m   1882\u001b[0m     mode,\n\u001b[1;32m   1883\u001b[0m     encoding\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mencoding\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m),\n\u001b[1;32m   1884\u001b[0m     compression\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcompression\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m),\n\u001b[1;32m   1885\u001b[0m     memory_map\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmemory_map\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mFalse\u001b[39;00m),\n\u001b[1;32m   1886\u001b[0m     is_text\u001b[38;5;241m=\u001b[39mis_text,\n\u001b[1;32m   1887\u001b[0m     errors\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mencoding_errors\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstrict\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[1;32m   1888\u001b[0m     storage_options\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstorage_options\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m),\n\u001b[1;32m   1889\u001b[0m )\n\u001b[1;32m   1890\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1891\u001b[0m f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles\u001b[38;5;241m.\u001b[39mhandle\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/pandas/io/common.py:728\u001b[0m, in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    725\u001b[0m     codecs\u001b[38;5;241m.\u001b[39mlookup_error(errors)\n\u001b[1;32m    727\u001b[0m \u001b[38;5;66;03m# open URLs\u001b[39;00m\n\u001b[0;32m--> 728\u001b[0m ioargs \u001b[38;5;241m=\u001b[39m _get_filepath_or_buffer(\n\u001b[1;32m    729\u001b[0m     path_or_buf,\n\u001b[1;32m    730\u001b[0m     encoding\u001b[38;5;241m=\u001b[39mencoding,\n\u001b[1;32m    731\u001b[0m     compression\u001b[38;5;241m=\u001b[39mcompression,\n\u001b[1;32m    732\u001b[0m     mode\u001b[38;5;241m=\u001b[39mmode,\n\u001b[1;32m    733\u001b[0m     storage_options\u001b[38;5;241m=\u001b[39mstorage_options,\n\u001b[1;32m    734\u001b[0m )\n\u001b[1;32m    736\u001b[0m handle \u001b[38;5;241m=\u001b[39m ioargs\u001b[38;5;241m.\u001b[39mfilepath_or_buffer\n\u001b[1;32m    737\u001b[0m handles: \u001b[38;5;28mlist\u001b[39m[BaseBuffer]\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/pandas/io/common.py:384\u001b[0m, in \u001b[0;36m_get_filepath_or_buffer\u001b[0;34m(filepath_or_buffer, encoding, compression, mode, storage_options)\u001b[0m\n\u001b[1;32m    382\u001b[0m \u001b[38;5;66;03m# assuming storage_options is to be interpreted as headers\u001b[39;00m\n\u001b[1;32m    383\u001b[0m req_info \u001b[38;5;241m=\u001b[39m urllib\u001b[38;5;241m.\u001b[39mrequest\u001b[38;5;241m.\u001b[39mRequest(filepath_or_buffer, headers\u001b[38;5;241m=\u001b[39mstorage_options)\n\u001b[0;32m--> 384\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m urlopen(req_info) \u001b[38;5;28;01mas\u001b[39;00m req:\n\u001b[1;32m    385\u001b[0m     content_encoding \u001b[38;5;241m=\u001b[39m req\u001b[38;5;241m.\u001b[39mheaders\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mContent-Encoding\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m    386\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m content_encoding \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgzip\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m    387\u001b[0m         \u001b[38;5;66;03m# Override compression based on Content-Encoding header\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/pandas/io/common.py:289\u001b[0m, in \u001b[0;36murlopen\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    283\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    284\u001b[0m \u001b[38;5;124;03mLazy-import wrapper for stdlib urlopen, as that imports a big chunk of\u001b[39;00m\n\u001b[1;32m    285\u001b[0m \u001b[38;5;124;03mthe stdlib.\u001b[39;00m\n\u001b[1;32m    286\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    287\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01murllib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mrequest\u001b[39;00m\n\u001b[0;32m--> 289\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m urllib\u001b[38;5;241m.\u001b[39mrequest\u001b[38;5;241m.\u001b[39murlopen(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/urllib/request.py:216\u001b[0m, in \u001b[0;36murlopen\u001b[0;34m(url, data, timeout, cafile, capath, cadefault, context)\u001b[0m\n\u001b[1;32m    214\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    215\u001b[0m     opener \u001b[38;5;241m=\u001b[39m _opener\n\u001b[0;32m--> 216\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m opener\u001b[38;5;241m.\u001b[39mopen(url, data, timeout)\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/urllib/request.py:519\u001b[0m, in \u001b[0;36mOpenerDirector.open\u001b[0;34m(self, fullurl, data, timeout)\u001b[0m\n\u001b[1;32m    516\u001b[0m     req \u001b[38;5;241m=\u001b[39m meth(req)\n\u001b[1;32m    518\u001b[0m sys\u001b[38;5;241m.\u001b[39maudit(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124murllib.Request\u001b[39m\u001b[38;5;124m'\u001b[39m, req\u001b[38;5;241m.\u001b[39mfull_url, req\u001b[38;5;241m.\u001b[39mdata, req\u001b[38;5;241m.\u001b[39mheaders, req\u001b[38;5;241m.\u001b[39mget_method())\n\u001b[0;32m--> 519\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_open(req, data)\n\u001b[1;32m    521\u001b[0m \u001b[38;5;66;03m# post-process response\u001b[39;00m\n\u001b[1;32m    522\u001b[0m meth_name \u001b[38;5;241m=\u001b[39m protocol\u001b[38;5;241m+\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_response\u001b[39m\u001b[38;5;124m\"\u001b[39m\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/urllib/request.py:536\u001b[0m, in \u001b[0;36mOpenerDirector._open\u001b[0;34m(self, req, data)\u001b[0m\n\u001b[1;32m    533\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m result\n\u001b[1;32m    535\u001b[0m protocol \u001b[38;5;241m=\u001b[39m req\u001b[38;5;241m.\u001b[39mtype\n\u001b[0;32m--> 536\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_chain(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandle_open, protocol, protocol \u001b[38;5;241m+\u001b[39m\n\u001b[1;32m    537\u001b[0m                           \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m_open\u001b[39m\u001b[38;5;124m'\u001b[39m, req)\n\u001b[1;32m    538\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m result:\n\u001b[1;32m    539\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/urllib/request.py:496\u001b[0m, in \u001b[0;36mOpenerDirector._call_chain\u001b[0;34m(self, chain, kind, meth_name, *args)\u001b[0m\n\u001b[1;32m    494\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m handler \u001b[38;5;129;01min\u001b[39;00m handlers:\n\u001b[1;32m    495\u001b[0m     func \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(handler, meth_name)\n\u001b[0;32m--> 496\u001b[0m     result \u001b[38;5;241m=\u001b[39m func(\u001b[38;5;241m*\u001b[39margs)\n\u001b[1;32m    497\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m result \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    498\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/urllib/request.py:1391\u001b[0m, in \u001b[0;36mHTTPSHandler.https_open\u001b[0;34m(self, req)\u001b[0m\n\u001b[1;32m   1390\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mhttps_open\u001b[39m(\u001b[38;5;28mself\u001b[39m, req):\n\u001b[0;32m-> 1391\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdo_open(http\u001b[38;5;241m.\u001b[39mclient\u001b[38;5;241m.\u001b[39mHTTPSConnection, req,\n\u001b[1;32m   1392\u001b[0m         context\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_context, check_hostname\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_hostname)\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/urllib/request.py:1348\u001b[0m, in \u001b[0;36mAbstractHTTPHandler.do_open\u001b[0;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[1;32m   1346\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1347\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1348\u001b[0m         h\u001b[38;5;241m.\u001b[39mrequest(req\u001b[38;5;241m.\u001b[39mget_method(), req\u001b[38;5;241m.\u001b[39mselector, req\u001b[38;5;241m.\u001b[39mdata, headers,\n\u001b[1;32m   1349\u001b[0m                   encode_chunked\u001b[38;5;241m=\u001b[39mreq\u001b[38;5;241m.\u001b[39mhas_header(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTransfer-encoding\u001b[39m\u001b[38;5;124m'\u001b[39m))\n\u001b[1;32m   1350\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err: \u001b[38;5;66;03m# timeout error\u001b[39;00m\n\u001b[1;32m   1351\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m URLError(err)\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/http/client.py:1303\u001b[0m, in \u001b[0;36mHTTPConnection.request\u001b[0;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[1;32m   1300\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrequest\u001b[39m(\u001b[38;5;28mself\u001b[39m, method, url, body\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, headers\u001b[38;5;241m=\u001b[39m{}, \u001b[38;5;241m*\u001b[39m,\n\u001b[1;32m   1301\u001b[0m             encode_chunked\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m):\n\u001b[1;32m   1302\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Send a complete request to the server.\"\"\"\u001b[39;00m\n\u001b[0;32m-> 1303\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_send_request(method, url, body, headers, encode_chunked)\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/http/client.py:1349\u001b[0m, in \u001b[0;36mHTTPConnection._send_request\u001b[0;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[1;32m   1345\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(body, \u001b[38;5;28mstr\u001b[39m):\n\u001b[1;32m   1346\u001b[0m     \u001b[38;5;66;03m# RFC 2616 Section 3.7.1 says that text default has a\u001b[39;00m\n\u001b[1;32m   1347\u001b[0m     \u001b[38;5;66;03m# default charset of iso-8859-1.\u001b[39;00m\n\u001b[1;32m   1348\u001b[0m     body \u001b[38;5;241m=\u001b[39m _encode(body, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbody\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m-> 1349\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mendheaders(body, encode_chunked\u001b[38;5;241m=\u001b[39mencode_chunked)\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/http/client.py:1298\u001b[0m, in \u001b[0;36mHTTPConnection.endheaders\u001b[0;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[1;32m   1296\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1297\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m CannotSendHeader()\n\u001b[0;32m-> 1298\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_send_output(message_body, encode_chunked\u001b[38;5;241m=\u001b[39mencode_chunked)\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/http/client.py:1058\u001b[0m, in \u001b[0;36mHTTPConnection._send_output\u001b[0;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[1;32m   1056\u001b[0m msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\r\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_buffer)\n\u001b[1;32m   1057\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_buffer[:]\n\u001b[0;32m-> 1058\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msend(msg)\n\u001b[1;32m   1060\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m message_body \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m   1061\u001b[0m \n\u001b[1;32m   1062\u001b[0m     \u001b[38;5;66;03m# create a consistent interface to message_body\u001b[39;00m\n\u001b[1;32m   1063\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(message_body, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mread\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[1;32m   1064\u001b[0m         \u001b[38;5;66;03m# Let file-like take precedence over byte-like.  This\u001b[39;00m\n\u001b[1;32m   1065\u001b[0m         \u001b[38;5;66;03m# is needed to allow the current position of mmap'ed\u001b[39;00m\n\u001b[1;32m   1066\u001b[0m         \u001b[38;5;66;03m# files to be taken into account.\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/http/client.py:996\u001b[0m, in \u001b[0;36mHTTPConnection.send\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m    994\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msock \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    995\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mauto_open:\n\u001b[0;32m--> 996\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconnect()\n\u001b[1;32m    997\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    998\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m NotConnected()\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/http/client.py:1468\u001b[0m, in \u001b[0;36mHTTPSConnection.connect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1465\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mconnect\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m   1466\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mConnect to a host on a given (SSL) port.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m-> 1468\u001b[0m     \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39mconnect()\n\u001b[1;32m   1470\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_tunnel_host:\n\u001b[1;32m   1471\u001b[0m         server_hostname \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_tunnel_host\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/http/client.py:962\u001b[0m, in \u001b[0;36mHTTPConnection.connect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    960\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Connect to the host and port specified in __init__.\"\"\"\u001b[39;00m\n\u001b[1;32m    961\u001b[0m sys\u001b[38;5;241m.\u001b[39maudit(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhttp.client.connect\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28mself\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhost, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mport)\n\u001b[0;32m--> 962\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msock \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_create_connection(\n\u001b[1;32m    963\u001b[0m     (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhost,\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mport), \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtimeout, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msource_address)\n\u001b[1;32m    964\u001b[0m \u001b[38;5;66;03m# Might fail in OSs that don't implement TCP_NODELAY\u001b[39;00m\n\u001b[1;32m    965\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/socket.py:836\u001b[0m, in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address, all_errors)\u001b[0m\n\u001b[1;32m    834\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m source_address:\n\u001b[1;32m    835\u001b[0m     sock\u001b[38;5;241m.\u001b[39mbind(source_address)\n\u001b[0;32m--> 836\u001b[0m sock\u001b[38;5;241m.\u001b[39mconnect(sa)\n\u001b[1;32m    837\u001b[0m \u001b[38;5;66;03m# Break explicitly a reference cycle\u001b[39;00m\n\u001b[1;32m    838\u001b[0m exceptions\u001b[38;5;241m.\u001b[39mclear()\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "df1 = pd.read_csv('https://gist.githubusercontent.com/netj/8836201/raw/6f9306ad21398ea43cba4f7d537619d0e07d5ae3/iris.csv')\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "c0a9dd9f-f832-41ef-9f0d-c9c00d62b795",
   "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>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": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "364a1164-e1ba-4a39-a499-a2fad6cea981",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 5)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "cb224bf8-c811-4f86-9b12-c2c32e0605f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "54f9692d-2fb7-4237-b665-6ecf37b33e53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['roll', 'name', 'class', 'marks', 'age']"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "d75e6dff-4acf-43de-9d94-3d40ba9208dd",
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>roll</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <td>anil</td>\n",
       "      <td>amit</td>\n",
       "      <td>aniket</td>\n",
       "      <td>ajinkya</td>\n",
       "      <td>asha</td>\n",
       "      <td>ayesha</td>\n",
       "      <td>amar</td>\n",
       "      <td>amita</td>\n",
       "      <td>amol</td>\n",
       "      <td>anmol</td>\n",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
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       "      <td>69.66</td>\n",
       "      <td>63.28</td>\n",
       "      <td>49.55</td>\n",
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       "    <tr>\n",
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       "           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",
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       "class     BE  \n",
       "marks  78.66  \n",
       "age       21  "
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   "outputs": [
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   "id": "40d75ab0-fc58-49d1-bcc9-7c6f9cba794d",
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   "id": "b55af8b3-d407-4745-aae4-dde153f5bcb9",
   "metadata": {},
   "outputs": [
    {
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       "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)"
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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"
      ]
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   "execution_count": 86,
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       "   roll    name class  marks  age\n",
       "0     1    anil    TE  56.77   22\n",
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       "2     3  aniket    BE  76.88   19"
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   "execution_count": 90,
   "id": "16233b57-e5e3-4a29-bb42-0ed4622ff94b",
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       "   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"
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     "execution_count": 90,
     "metadata": {},
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   "execution_count": 92,
   "id": "b96caaa5-f7e5-4ca0-9ccb-d61a08920719",
   "metadata": {},
   "outputs": [
    {
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       "   roll   name class  marks  age\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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   "source": [
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  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "38244099-3239-429b-801e-dc59642c4dfa",
   "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"
    }
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    "df.sum()"
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   "execution_count": 98,
   "id": "0d109f08-4bae-4843-b8ac-ec4312ddcbfa",
   "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": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df.max()"
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  {
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   "execution_count": 100,
   "id": "9bda63be-5ea5-4fd0-a96a-1e1f152363eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "roll     10\n",
       "name     10\n",
       "class    10\n",
       "marks    10\n",
       "age      10\n",
       "dtype: int64"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df.count()"
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  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "343a118d-3d8f-44d8-b6e5-d2a04c7a46fc",
   "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: 532.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "0edaf8e7-4e1a-4ad4-b2ca-6384a772a506",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <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": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "045fad1b-feef-4f0d-ab96-f49e9a834ef1",
   "metadata": {},
   "source": [
    "### data slicing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "9889df9e-0372-4cbf-ba9f-5de4a24c007f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>name</th>\n",
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       "    </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": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "eb0ed13f-cb5f-4bab-a707-b2b49507275e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>class</th>\n",
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       "      <td>aniket</td>\n",
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>ajinkya</td>\n",
       "      <td>TE</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>asha</td>\n",
       "      <td>TE</td>\n",
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      "text/plain": [
       "   roll     name class\n",
       "2     3   aniket    BE\n",
       "3     4  ajinkya    TE\n",
       "4     5     asha    TE"
      ]
     },
     "execution_count": 109,
     "metadata": {},
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   ],
   "source": [
    "df.iloc[2:5, 0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "5a7b3c76-fbc3-423d-9f9b-cd1c0ddcb412",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "   roll     name class\n",
       "2     3   aniket    BE\n",
       "3     4  ajinkya    TE\n",
       "4     5     asha    TE"
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   ],
   "source": [
    "x = df.iloc[2:5, 0:3]\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "ace7612a-accd-4baa-92f0-71767bc28a11",
   "metadata": {
    "scrolled": true
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   "outputs": [
    {
     "data": {
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       "      <th>8</th>\n",
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       "   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": 113,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.iloc[7:, :]"
   ]
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  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "894e99e9-eb18-46ff-bf64-5d72319b13e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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",
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       "      <td>20</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
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       "      <td>BE</td>\n",
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       "      <td>20</td>\n",
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       "    <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",
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       "    <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",
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       "    <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": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "d3b38e18-c956-45ea-9f03-1cd1224907fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "roll          6\n",
       "name     ayesha\n",
       "class        BE\n",
       "Name: 5, dtype: object"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[5, 0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "38bec6d4-25e4-4eab-b159-391bd3678aed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    ajinkya\n",
       "4       asha\n",
       "5     ayesha\n",
       "Name: name, dtype: object"
      ]
     },
     "execution_count": 121,
     "metadata": {},
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