Azure DATAs.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bca25437",
   "metadata": {},
   "source": [
    "### difference between list and array is\n",
    "* Multiplying a NumPy array on the other hand performs an element-wise calculation in which the array behaves like a vector, so we end up with an array of the same size in which each element has been multiplied by 2.\n",
    "* multiplying a list by 2 creates a new list of twice the length with the original sequence of list elements repeated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2871c71b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[50, 50, 47, 97, 49, 3, 53, 42, 26, 74, 82, 62, 37, 15, 70, 27, 36, 35, 48, 52, 63, 64]\n"
     ]
    }
   ],
   "source": [
    "data = [50,50,47,97,49,3,53,42,26,74,82,62,37,15,70,27,36,35,48,52,63,64]\n",
    "print(data)\n",
    "\n",
    "# Its the list datatype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "43b2d518",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[50, 50, 47, 97, 49, 3, 53, 42, 26, 74, 82, 62, 37, 15, 70, 27, 36, 35, 48, 52, 63, 64]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "grade = np.array(data)\n",
    "print(data)\n",
    "\n",
    "# this is int datatype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9fbcb34b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "list is  <class 'list'>\n",
      "NP Array is  int32\n"
     ]
    }
   ],
   "source": [
    "print(\"list is \",type(data))\n",
    "print(\"NP Array is \",grade.dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "19f441cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[50, 50, 47, 97, 49, 3, 53, 42, 26, 74, 82, 62, 37, 15, 70, 27, 36, 35, 48, 52, 63, 64, 50, 50, 47, 97, 49, 3, 53, 42, 26, 74, 82, 62, 37, 15, 70, 27, 36, 35, 48, 52, 63, 64]\n"
     ]
    }
   ],
   "source": [
    "print(data*2)\n",
    "# Gives two times same list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "074a6e7d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[100 100  94 194  98   6 106  84  52 148 164 124  74  30 140  54  72  70\n",
      "  96 104 126 128]\n"
     ]
    }
   ],
   "source": [
    "print(grade*2)\n",
    "# simply multipliers twi times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e616f363",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "study_hours = [10.0,11.5,9.0,16.0,9.25,1.0,11.5,9.0,8.5,14.5,15.5,\n",
    "               13.75,9.0,8.0,15.5,8.0,9.0,6.0,10.0,12.0,12.5,12.0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6998e8c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[50, 50, 47, 97, 49, 3, 53, 42, 26, 74, 82, 62, 37, 15, 70, 27, 36, 35, 48, 52, 63, 64], [10.0, 11.5, 9.0, 16.0, 9.25, 1.0, 11.5, 9.0, 8.5, 14.5, 15.5, 13.75, 9.0, 8.0, 15.5, 8.0, 9.0, 6.0, 10.0, 12.0, 12.5, 12.0]]\n"
     ]
    }
   ],
   "source": [
    "data1 = [data,study_hours]\n",
    "print(data1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4ff48a48",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'tuple'>\n",
      "([10.0, 11.5, 9.0, 16.0, 9.25, 1.0, 11.5, 9.0, 8.5, 14.5, 15.5, 13.75, 9.0, 8.0, 15.5, 8.0, 9.0, 6.0, 10.0, 12.0, 12.5, 12.0], [50, 50, 47, 97, 49, 3, 53, 42, 26, 74, 82, 62, 37, 15, 70, 27, 36, 35, 48, 52, 63, 64])\n"
     ]
    }
   ],
   "source": [
    "'''<class 'tuple'>'''\n",
    "data2 = study_hours,data\n",
    "print(type(data2))\n",
    "print(data2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ddfb14cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[50.  , 50.  , 47.  , 97.  , 49.  ,  3.  , 53.  , 42.  , 26.  ,\n",
       "        74.  , 82.  , 62.  , 37.  , 15.  , 70.  , 27.  , 36.  , 35.  ,\n",
       "        48.  , 52.  , 63.  , 64.  ],\n",
       "       [10.  , 11.5 ,  9.  , 16.  ,  9.25,  1.  , 11.5 ,  9.  ,  8.5 ,\n",
       "        14.5 , 15.5 , 13.75,  9.  ,  8.  , 15.5 ,  8.  ,  9.  ,  6.  ,\n",
       "        10.  , 12.  , 12.5 , 12.  ]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "study_data = np.array([grade,study_hours])\n",
    "print(study_data.dtype)\n",
    "study_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "42aac115",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "22dbdae9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "study_data[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4a75b6b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>StudyHours</th>\n",
       "      <th>Grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dan</td>\n",
       "      <td>50.0</td>\n",
       "      <td>10.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Joann</td>\n",
       "      <td>50.0</td>\n",
       "      <td>11.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Pedro</td>\n",
       "      <td>47.0</td>\n",
       "      <td>9.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rosie</td>\n",
       "      <td>97.0</td>\n",
       "      <td>16.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ethan</td>\n",
       "      <td>49.0</td>\n",
       "      <td>9.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Vicky</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Frederic</td>\n",
       "      <td>53.0</td>\n",
       "      <td>11.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Jimmie</td>\n",
       "      <td>42.0</td>\n",
       "      <td>9.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Rhonda</td>\n",
       "      <td>26.0</td>\n",
       "      <td>8.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Giovanni</td>\n",
       "      <td>74.0</td>\n",
       "      <td>14.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Francesca</td>\n",
       "      <td>82.0</td>\n",
       "      <td>15.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Rajab</td>\n",
       "      <td>62.0</td>\n",
       "      <td>13.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Naiyana</td>\n",
       "      <td>37.0</td>\n",
       "      <td>9.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Kian</td>\n",
       "      <td>15.0</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Jenny</td>\n",
       "      <td>70.0</td>\n",
       "      <td>15.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Jakeem</td>\n",
       "      <td>27.0</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Helena</td>\n",
       "      <td>36.0</td>\n",
       "      <td>9.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Ismat</td>\n",
       "      <td>35.0</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Anila</td>\n",
       "      <td>48.0</td>\n",
       "      <td>10.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Skye</td>\n",
       "      <td>52.0</td>\n",
       "      <td>12.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Daniel</td>\n",
       "      <td>63.0</td>\n",
       "      <td>12.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Aisha</td>\n",
       "      <td>64.0</td>\n",
       "      <td>12.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Name  StudyHours  Grade\n",
       "0         Dan        50.0  10.00\n",
       "1       Joann        50.0  11.50\n",
       "2       Pedro        47.0   9.00\n",
       "3       Rosie        97.0  16.00\n",
       "4       Ethan        49.0   9.25\n",
       "5       Vicky         3.0   1.00\n",
       "6    Frederic        53.0  11.50\n",
       "7      Jimmie        42.0   9.00\n",
       "8      Rhonda        26.0   8.50\n",
       "9    Giovanni        74.0  14.50\n",
       "10  Francesca        82.0  15.50\n",
       "11      Rajab        62.0  13.75\n",
       "12    Naiyana        37.0   9.00\n",
       "13       Kian        15.0   8.00\n",
       "14      Jenny        70.0  15.50\n",
       "15     Jakeem        27.0   8.00\n",
       "16     Helena        36.0   9.00\n",
       "17      Ismat        35.0   6.00\n",
       "18      Anila        48.0  10.00\n",
       "19       Skye        52.0  12.00\n",
       "20     Daniel        63.0  12.50\n",
       "21      Aisha        64.0  12.00"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df_students = pd.DataFrame({'Name': ['Dan', 'Joann', 'Pedro', 'Rosie', 'Ethan', 'Vicky', 'Frederic', 'Jimmie', \n",
    "                                     'Rhonda', 'Giovanni', 'Francesca', 'Rajab', 'Naiyana', 'Kian', 'Jenny',\n",
    "                                     'Jakeem','Helena','Ismat','Anila','Skye','Daniel','Aisha'],\n",
    "                            'StudyHours':study_data[0],\n",
    "                            'Grade':study_data[1]})\n",
    "\n",
    "df_students "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bbd5ca53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name           Dan\n",
       "StudyHours    50.0\n",
       "Grade         10.0\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_students.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6024e2ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>Name</th>\n",
       "      <th>StudyHours</th>\n",
       "      <th>Grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dan</td>\n",
       "      <td>50.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Name  StudyHours  Grade\n",
       "0  Dan        50.0   10.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_students.iloc[[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "846ce353",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>StudyHours</th>\n",
       "      <th>Grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Daniel</td>\n",
       "      <td>63.0</td>\n",
       "      <td>12.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Anila</td>\n",
       "      <td>48.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name  StudyHours  Grade\n",
       "20  Daniel        63.0   12.5\n",
       "18   Anila        48.0   10.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_students.loc[[20,18]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d98030cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>Name</th>\n",
       "      <th>StudyHours</th>\n",
       "      <th>Grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dan</td>\n",
       "      <td>50.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Anila</td>\n",
       "      <td>48.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name  StudyHours  Grade\n",
       "0     Dan        50.0   10.0\n",
       "18  Anila        48.0   10.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df_students.iloc[21,['Name']]\n",
    "df_students.loc[lambda df: df_students['Grade'] == 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "104f671f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name          Pedro\n",
       "StudyHours     47.0\n",
       "Grade           9.0\n",
       "Name: 2, dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_students.iloc[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f2fa634c",
   "metadata": {},
   "outputs": [
    {
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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>Name</th>\n",
       "      <th>StudyHours</th>\n",
       "      <th>Grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Pedro</td>\n",
       "      <td>47.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Jimmie</td>\n",
       "      <td>42.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Naiyana</td>\n",
       "      <td>37.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Helena</td>\n",
       "      <td>36.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Name  StudyHours  Grade\n",
       "2     Pedro        47.0    9.0\n",
       "7    Jimmie        42.0    9.0\n",
       "12  Naiyana        37.0    9.0\n",
       "16   Helena        36.0    9.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_students.loc[df_students['Grade']==9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7bac54f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    10.00\n",
       "1    11.50\n",
       "2     9.00\n",
       "3    16.00\n",
       "4     9.25\n",
       "5     1.00\n",
       "Name: Grade, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_students.loc[:5,\"Grade\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2f221efe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>StudyHours</th>\n",
       "      <th>Grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dan</td>\n",
       "      <td>50.0</td>\n",
       "      <td>10.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Joann</td>\n",
       "      <td>50.0</td>\n",
       "      <td>11.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Pedro</td>\n",
       "      <td>47.0</td>\n",
       "      <td>9.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rosie</td>\n",
       "      <td>97.0</td>\n",
       "      <td>16.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ethan</td>\n",
       "      <td>49.0</td>\n",
       "      <td>9.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name  StudyHours  Grade\n",
       "0    Dan        50.0  10.00\n",
       "1  Joann        50.0  11.50\n",
       "2  Pedro        47.0   9.00\n",
       "3  Rosie        97.0  16.00\n",
       "4  Ethan        49.0   9.25"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_students.iloc[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5de6fc02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name      Dan\n",
       "Grade    10.0\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_students.iloc[0,[0,2]]\n",
    "# df_students.iloc[[row],[column]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a1c09514",
   "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",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>StudyHours</th>\n",
       "      <th>Grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dan</td>\n",
       "      <td>50.0</td>\n",
       "      <td>10.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Joann</td>\n",
       "      <td>50.0</td>\n",
       "      <td>11.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rosie</td>\n",
       "      <td>97.0</td>\n",
       "      <td>16.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ethan</td>\n",
       "      <td>49.0</td>\n",
       "      <td>9.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Frederic</td>\n",
       "      <td>53.0</td>\n",
       "      <td>11.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Giovanni</td>\n",
       "      <td>74.0</td>\n",
       "      <td>14.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Francesca</td>\n",
       "      <td>82.0</td>\n",
       "      <td>15.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Rajab</td>\n",
       "      <td>62.0</td>\n",
       "      <td>13.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Jenny</td>\n",
       "      <td>70.0</td>\n",
       "      <td>15.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Anila</td>\n",
       "      <td>48.0</td>\n",
       "      <td>10.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Skye</td>\n",
       "      <td>52.0</td>\n",
       "      <td>12.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Daniel</td>\n",
       "      <td>63.0</td>\n",
       "      <td>12.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Aisha</td>\n",
       "      <td>64.0</td>\n",
       "      <td>12.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Name  StudyHours  Grade\n",
       "0         Dan        50.0  10.00\n",
       "1       Joann        50.0  11.50\n",
       "3       Rosie        97.0  16.00\n",
       "4       Ethan        49.0   9.25\n",
       "6    Frederic        53.0  11.50\n",
       "9    Giovanni        74.0  14.50\n",
       "10  Francesca        82.0  15.50\n",
       "11      Rajab        62.0  13.75\n",
       "14      Jenny        70.0  15.50\n",
       "18      Anila        48.0  10.00\n",
       "19       Skye        52.0  12.00\n",
       "20     Daniel        63.0  12.50\n",
       "21      Aisha        64.0  12.00"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_students.query(\"Grade> 9\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0dad68ec",
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>StudyHours</th>\n",
       "      <th>Grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Vicky</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Rhonda</td>\n",
       "      <td>26.0</td>\n",
       "      <td>8.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Kian</td>\n",
       "      <td>15.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Jakeem</td>\n",
       "      <td>27.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name  StudyHours  Grade\n",
       "5    Vicky         3.0    1.0\n",
       "8   Rhonda        26.0    8.5\n",
       "13    Kian        15.0    8.0\n",
       "15  Jakeem        27.0    8.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_students.query(\"StudyHours<30\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6acbed95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>StudyHours</th>\n",
       "      <th>Grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dan</td>\n",
       "      <td>50.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Name  StudyHours  Grade\n",
       "0  Dan        50.0   10.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_students.query(\"Name=='Dan'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f3dd0c15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>StudyHours</th>\n",
       "      <th>Grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dan</td>\n",
       "      <td>50.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Name  StudyHours  Grade\n",
       "0  Dan        50.0   10.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_students[df_students.Name == 'Dan']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "9c65b251",
   "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",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>StudyHours</th>\n",
       "      <th>Grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dan</td>\n",
       "      <td>50.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Kian</td>\n",
       "      <td>15.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name  StudyHours  Grade\n",
       "0    Dan        50.0   10.0\n",
       "13  Kian        15.0    8.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_students.query(\"Name==['Kian','Dan']\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6792c9c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\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>Name</th>\n",
       "      <th>StudyHours</th>\n",
       "      <th>Grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dan</td>\n",
       "      <td>50.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Joann</td>\n",
       "      <td>50.0</td>\n",
       "      <td>11.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name  StudyHours  Grade\n",
       "0    Dan        50.0   10.0\n",
       "1  Joann        50.0   11.5"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_students.iloc[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b138c83a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>StudyHours</th>\n",
       "      <th>Grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dan</td>\n",
       "      <td>50.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Joann</td>\n",
       "      <td>50.0</td>\n",
       "      <td>11.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Pedro</td>\n",
       "      <td>47.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name  StudyHours  Grade\n",
       "0    Dan        50.0   10.0\n",
       "1  Joann        50.0   11.5\n",
       "2  Pedro        47.0    9.0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_students.loc[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3ab7d64d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df_students.copy(deep=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e38ea511",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\KIRAN Sonkamble\\AppData\\Local\\Temp\\ipykernel_22524\\3698961737.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  df.mean()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "StudyHours    49.181818\n",
       "Grade         10.522727\n",
       "dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "23951627",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49.18181818181818"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.StudyHours.mean()\n",
    "# Mean of studyHours"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acfce4dc",
   "metadata": {},
   "source": [
    "#### For example, let's find the average grade for students who undertook more than the average amount of study time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c19de65f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>StudyHours</th>\n",
       "      <th>Grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dan</td>\n",
       "      <td>50.0</td>\n",
       "      <td>10.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Joann</td>\n",
       "      <td>50.0</td>\n",
       "      <td>11.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rosie</td>\n",
       "      <td>97.0</td>\n",
       "      <td>16.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Frederic</td>\n",
       "      <td>53.0</td>\n",
       "      <td>11.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Giovanni</td>\n",
       "      <td>74.0</td>\n",
       "      <td>14.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Francesca</td>\n",
       "      <td>82.0</td>\n",
       "      <td>15.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Rajab</td>\n",
       "      <td>62.0</td>\n",
       "      <td>13.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Jenny</td>\n",
       "      <td>70.0</td>\n",
       "      <td>15.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Skye</td>\n",
       "      <td>52.0</td>\n",
       "      <td>12.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Daniel</td>\n",
       "      <td>63.0</td>\n",
       "      <td>12.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Aisha</td>\n",
       "      <td>64.0</td>\n",
       "      <td>12.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Name  StudyHours  Grade\n",
       "0         Dan        50.0  10.00\n",
       "1       Joann        50.0  11.50\n",
       "3       Rosie        97.0  16.00\n",
       "6    Frederic        53.0  11.50\n",
       "9    Giovanni        74.0  14.50\n",
       "10  Francesca        82.0  15.50\n",
       "11      Rajab        62.0  13.75\n",
       "14      Jenny        70.0  15.50\n",
       "19       Skye        52.0  12.00\n",
       "20     Daniel        63.0  12.50\n",
       "21      Aisha        64.0  12.00"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.StudyHours>df.StudyHours.mean()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "9d936452",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "65.18181818181819"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.StudyHours>df.StudyHours.mean()].StudyHours.mean()\n",
    "# study hour mean of mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "5fd4f127",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name          0\n",
       "StudyHours    0\n",
       "Grade         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae908916",
   "metadata": {},
   "source": [
    "### When the DataFrame is retrieved, the missing numeric values show up as NaN (not a number)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e7f7e923",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\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>Name</th>\n",
       "      <th>StudyHours</th>\n",
       "      <th>Grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Name, StudyHours, Grade]\n",
       "Index: []"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df.isna().any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "18e0cce5",
   "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",
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       "\n",
       "    .dataframe thead th {\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>Name</th>\n",
       "      <th>StudyHours</th>\n",
       "      <th>Grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Name, StudyHours, Grade]\n",
       "Index: []"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_students[df_students.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ceca1911",
   "metadata": {},
   "source": [
    "#### One common approach is to impute replacement values. For example, if the number of study hours is missing, we could just assume that the student studied for an average amount of time and replace the missing value with the mean study hours. To do this, we can use the fillna method, like this:\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "2359a1eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.Grade=df.Grade.fillna(df.Grade.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b44add30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th>0</th>\n",
       "      <td>Dan</td>\n",
       "      <td>50.0</td>\n",
       "      <td>10.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Joann</td>\n",
       "      <td>50.0</td>\n",
       "      <td>11.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Pedro</td>\n",
       "      <td>47.0</td>\n",
       "      <td>9.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rosie</td>\n",
       "      <td>97.0</td>\n",
       "      <td>16.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ethan</td>\n",
       "      <td>49.0</td>\n",
       "      <td>9.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Vicky</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Frederic</td>\n",
       "      <td>53.0</td>\n",
       "      <td>11.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Jimmie</td>\n",
       "      <td>42.0</td>\n",
       "      <td>9.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Rhonda</td>\n",
       "      <td>26.0</td>\n",
       "      <td>8.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Giovanni</td>\n",
       "      <td>74.0</td>\n",
       "      <td>14.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Francesca</td>\n",
       "      <td>82.0</td>\n",
       "      <td>15.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Rajab</td>\n",
       "      <td>62.0</td>\n",
       "      <td>13.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Naiyana</td>\n",
       "      <td>37.0</td>\n",
       "      <td>9.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Kian</td>\n",
       "      <td>15.0</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Jenny</td>\n",
       "      <td>70.0</td>\n",
       "      <td>15.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Jakeem</td>\n",
       "      <td>27.0</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Helena</td>\n",
       "      <td>36.0</td>\n",
       "      <td>9.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Ismat</td>\n",
       "      <td>35.0</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Anila</td>\n",
       "      <td>48.0</td>\n",
       "      <td>10.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Skye</td>\n",
       "      <td>52.0</td>\n",
       "      <td>12.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Daniel</td>\n",
       "      <td>63.0</td>\n",
       "      <td>12.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Aisha</td>\n",
       "      <td>64.0</td>\n",
       "      <td>12.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Name  StudyHours  Grade\n",
       "0         Dan        50.0  10.00\n",
       "1       Joann        50.0  11.50\n",
       "2       Pedro        47.0   9.00\n",
       "3       Rosie        97.0  16.00\n",
       "4       Ethan        49.0   9.25\n",
       "5       Vicky         3.0   1.00\n",
       "6    Frederic        53.0  11.50\n",
       "7      Jimmie        42.0   9.00\n",
       "8      Rhonda        26.0   8.50\n",
       "9    Giovanni        74.0  14.50\n",
       "10  Francesca        82.0  15.50\n",
       "11      Rajab        62.0  13.75\n",
       "12    Naiyana        37.0   9.00\n",
       "13       Kian        15.0   8.00\n",
       "14      Jenny        70.0  15.50\n",
       "15     Jakeem        27.0   8.00\n",
       "16     Helena        36.0   9.00\n",
       "17      Ismat        35.0   6.00\n",
       "18      Anila        48.0  10.00\n",
       "19       Skye        52.0  12.00\n",
       "20     Daniel        63.0  12.50\n",
       "21      Aisha        64.0  12.00"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b79bb996",
   "metadata": {},
   "source": [
    "#### One common approach is to impute replacement values. For example, if the number of study hours is missing, we could just assume that the student studied for an average amount of time and replace the missing value with the mean study hours. To do this, we can use the fillna method, like this:\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "15a4521c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.dropna(axis=0,how='any')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "9c5cbc14",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>Dan</td>\n",
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       "      <td>10.00</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Joann</td>\n",
       "      <td>50.0</td>\n",
       "      <td>11.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Pedro</td>\n",
       "      <td>47.0</td>\n",
       "      <td>9.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rosie</td>\n",
       "      <td>97.0</td>\n",
       "      <td>16.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ethan</td>\n",
       "      <td>49.0</td>\n",
       "      <td>9.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Vicky</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Frederic</td>\n",
       "      <td>53.0</td>\n",
       "      <td>11.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Jimmie</td>\n",
       "      <td>42.0</td>\n",
       "      <td>9.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Rhonda</td>\n",
       "      <td>26.0</td>\n",
       "      <td>8.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Giovanni</td>\n",
       "      <td>74.0</td>\n",
       "      <td>14.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Francesca</td>\n",
       "      <td>82.0</td>\n",
       "      <td>15.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Rajab</td>\n",
       "      <td>62.0</td>\n",
       "      <td>13.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Naiyana</td>\n",
       "      <td>37.0</td>\n",
       "      <td>9.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Kian</td>\n",
       "      <td>15.0</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Jenny</td>\n",
       "      <td>70.0</td>\n",
       "      <td>15.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Jakeem</td>\n",
       "      <td>27.0</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Helena</td>\n",
       "      <td>36.0</td>\n",
       "      <td>9.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Ismat</td>\n",
       "      <td>35.0</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Anila</td>\n",
       "      <td>48.0</td>\n",
       "      <td>10.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Skye</td>\n",
       "      <td>52.0</td>\n",
       "      <td>12.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Daniel</td>\n",
       "      <td>63.0</td>\n",
       "      <td>12.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Aisha</td>\n",
       "      <td>64.0</td>\n",
       "      <td>12.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Name  StudyHours  Grade\n",
       "0         Dan        50.0  10.00\n",
       "1       Joann        50.0  11.50\n",
       "2       Pedro        47.0   9.00\n",
       "3       Rosie        97.0  16.00\n",
       "4       Ethan        49.0   9.25\n",
       "5       Vicky         3.0   1.00\n",
       "6    Frederic        53.0  11.50\n",
       "7      Jimmie        42.0   9.00\n",
       "8      Rhonda        26.0   8.50\n",
       "9    Giovanni        74.0  14.50\n",
       "10  Francesca        82.0  15.50\n",
       "11      Rajab        62.0  13.75\n",
       "12    Naiyana        37.0   9.00\n",
       "13       Kian        15.0   8.00\n",
       "14      Jenny        70.0  15.50\n",
       "15     Jakeem        27.0   8.00\n",
       "16     Helena        36.0   9.00\n",
       "17      Ismat        35.0   6.00\n",
       "18      Anila        48.0  10.00\n",
       "19       Skye        52.0  12.00\n",
       "20     Daniel        63.0  12.50\n",
       "21      Aisha        64.0  12.00"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d9e5759",
   "metadata": {},
   "source": [
    "Let's assume that the passing grade for the course is 7.\n",
    "\n",
    "We can use that information to add a new column to the DataFrame, indicating whether or not each student passed.\n",
    "\n",
    "First, we'll create a Pandas Series containing the pass/fail indicator (True or False), and then we'll concatenate that series as a new column (axis 1) in the DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "2febdd80",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>StudyHours</th>\n",
       "      <th>Grade</th>\n",
       "      <th>pass</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dan</td>\n",
       "      <td>50.0</td>\n",
       "      <td>10.00</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Joann</td>\n",
       "      <td>50.0</td>\n",
       "      <td>11.50</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Pedro</td>\n",
       "      <td>47.0</td>\n",
       "      <td>9.00</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rosie</td>\n",
       "      <td>97.0</td>\n",
       "      <td>16.00</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ethan</td>\n",
       "      <td>49.0</td>\n",
       "      <td>9.25</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Vicky</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Frederic</td>\n",
       "      <td>53.0</td>\n",
       "      <td>11.50</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Jimmie</td>\n",
       "      <td>42.0</td>\n",
       "      <td>9.00</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Rhonda</td>\n",
       "      <td>26.0</td>\n",
       "      <td>8.50</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Giovanni</td>\n",
       "      <td>74.0</td>\n",
       "      <td>14.50</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Francesca</td>\n",
       "      <td>82.0</td>\n",
       "      <td>15.50</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Rajab</td>\n",
       "      <td>62.0</td>\n",
       "      <td>13.75</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Naiyana</td>\n",
       "      <td>37.0</td>\n",
       "      <td>9.00</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Kian</td>\n",
       "      <td>15.0</td>\n",
       "      <td>8.00</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Jenny</td>\n",
       "      <td>70.0</td>\n",
       "      <td>15.50</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Jakeem</td>\n",
       "      <td>27.0</td>\n",
       "      <td>8.00</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Helena</td>\n",
       "      <td>36.0</td>\n",
       "      <td>9.00</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Ismat</td>\n",
       "      <td>35.0</td>\n",
       "      <td>6.00</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Anila</td>\n",
       "      <td>48.0</td>\n",
       "      <td>10.00</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Skye</td>\n",
       "      <td>52.0</td>\n",
       "      <td>12.00</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Daniel</td>\n",
       "      <td>63.0</td>\n",
       "      <td>12.50</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Aisha</td>\n",
       "      <td>64.0</td>\n",
       "      <td>12.00</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Name  StudyHours  Grade   pass\n",
       "0         Dan        50.0  10.00   True\n",
       "1       Joann        50.0  11.50   True\n",
       "2       Pedro        47.0   9.00   True\n",
       "3       Rosie        97.0  16.00   True\n",
       "4       Ethan        49.0   9.25   True\n",
       "5       Vicky         3.0   1.00  False\n",
       "6    Frederic        53.0  11.50   True\n",
       "7      Jimmie        42.0   9.00   True\n",
       "8      Rhonda        26.0   8.50   True\n",
       "9    Giovanni        74.0  14.50   True\n",
       "10  Francesca        82.0  15.50   True\n",
       "11      Rajab        62.0  13.75   True\n",
       "12    Naiyana        37.0   9.00   True\n",
       "13       Kian        15.0   8.00   True\n",
       "14      Jenny        70.0  15.50   True\n",
       "15     Jakeem        27.0   8.00   True\n",
       "16     Helena        36.0   9.00   True\n",
       "17      Ismat        35.0   6.00  False\n",
       "18      Anila        48.0  10.00   True\n",
       "19       Skye        52.0  12.00   True\n",
       "20     Daniel        63.0  12.50   True\n",
       "21      Aisha        64.0  12.00   True"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "passing = pd.Series(df.Grade>7)\n",
    "df = pd.concat([df,passing.rename('pass')],axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "10f1e42b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True     20\n",
       "False     2\n",
       "Name: pass, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['pass'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "36b2d857",
   "metadata": {},
   "outputs": [],
   "source": [
    "passeed = df.groupby('pass').Name.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "06143a56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pass\n",
       "False     2\n",
       "True     20\n",
       "Name: Name, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "passeed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "2632a0ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\KIRAN Sonkamble\\AppData\\Local\\Temp\\ipykernel_22524\\4201441927.py:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  pssd = df.groupby('pass')['StudyHours',\"Grade\"].mean()\n"
     ]
    }
   ],
   "source": [
    "pssd = df.groupby('pass')['StudyHours',\"Grade\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "b9971232",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>StudyHours</th>\n",
       "      <th>Grade</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pass</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>19.0</td>\n",
       "      <td>3.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>52.2</td>\n",
       "      <td>11.225</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       StudyHours   Grade\n",
       "pass                     \n",
       "False        19.0   3.500\n",
       "True         52.2  11.225"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pssd.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "84b1911f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import warnings \n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "68c3c640",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "plt.bar(df.Name,df.Grade,color='green')\n",
    "plt.grid(color='#95a5a6', linestyle='--', linewidth=2, axis='y', alpha=0.7)\n",
    "plt.xticks(rotation=90);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3027517",
   "metadata": {},
   "source": [
    "### How to convert dataframe into list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "57d9e0b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10.0, 11.5, 9.0, 16.0, 9.25, 1.0, 11.5, 9.0, 8.5, 14.5, 15.5, 13.75, 9.0, 8.0, 15.5, 8.0, 9.0, 6.0, 10.0, 12.0, 12.5, 12.0]\n"
     ]
    }
   ],
   "source": [
    "print(df.Grade.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "10447d61",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21]\n"
     ]
    }
   ],
   "source": [
    "print(df.Grade.keys().tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "5107ff86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Grade.value_counts().tolist()\n",
    "# convert Grade values into list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "f7a9307f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Dan', 'Joann', 'Daniel', 'Skye']"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Name.value_counts().keys().tolist()[:4]\n",
    "# take Name column's keys ie., names and convert into list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "fd62f4f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Grade.value_counts().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "4e66721c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[True, False]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['pass'].value_counts().keys().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "19377b69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 22 artists>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(df.Name,df.Grade,color='orange')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "ac0b8484",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'jt' is not recognized as an internal or external command,\n",
      "operable program or batch file.\n"
     ]
    }
   ],
   "source": [
    "!jt -t chesterish"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "3221e9ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(df['pass'].value_counts(),labels=df['pass'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68f44797",
   "metadata": {},
   "source": [
    "### How to make a lis from DF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "f16261bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    10.0\n",
       "Name: Grade, dtype: float64"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Grade.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "472a1935",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10.0, 11.5, 9.0, 16.0, 9.25, 1.0, 11.5, 9.0, 8.5, 14.5, 15.5, 13.75, 9.0, 8.0, 15.5, 8.0, 9.0, 6.0, 10.0, 12.0, 12.5, 12.0]\n"
     ]
    }
   ],
   "source": [
    "print(df.Grade.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "2ca2e391",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.0     4\n",
       "10.0    2\n",
       "11.5    2\n",
       "15.5    2\n",
       "8.0     2\n",
       "Name: Grade, dtype: int64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Grade.value_counts().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "f89dbe65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Float64Index([9.0, 10.0, 11.5, 15.5, 8.0, 12.0, 16.0, 9.25, 1.0, 8.5, 14.5,\n",
       "              13.75, 6.0, 12.5],\n",
       "             dtype='float64')"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Grade.value_counts().keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "e3c7bf1c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[9.0, 10.0, 11.5, 15.5, 8.0, 12.0, 16.0, 9.25, 1.0, 8.5, 14.5, 13.75, 6.0, 12.5]\n"
     ]
    }
   ],
   "source": [
    "print(df.Grade.value_counts().keys().tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "72d86391",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[10.0,\n",
       " 11.5,\n",
       " 9.0,\n",
       " 16.0,\n",
       " 9.25,\n",
       " 1.0,\n",
       " 11.5,\n",
       " 9.0,\n",
       " 8.5,\n",
       " 14.5,\n",
       " 15.5,\n",
       " 13.75,\n",
       " 9.0,\n",
       " 8.0,\n",
       " 15.5,\n",
       " 8.0,\n",
       " 9.0,\n",
       " 6.0,\n",
       " 10.0,\n",
       " 12.0,\n",
       " 12.5,\n",
       " 12.0]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Grade.values.tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a80c329",
   "metadata": {},
   "source": [
    "### How ro draw multiple graphs "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "7305026e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax =plt.subplots(1,2,figsize=(18,5))\n",
    "ax[0].bar(df.Name,df.Grade,color='orange')\n",
    "ax[0].set_xticklabels(df.Name,rotation=90)\n",
    "ax[0].set_title('Grade')\n",
    "ax[0].legend(df.Grade.keys().tolist())\n",
    "\n",
    "ax[1].pie(df['pass'].value_counts(),labels=df['pass'].value_counts())\n",
    "ax[1].set_title('Passed')\n",
    "ax[1].legend(df['pass'].value_counts().keys().tolist());"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7640cfcd",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_students.plot.bar(x='Name', y='StudyHours', color='teal', figsize=(6,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "e6e7d0e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "e0674ac1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Name'>"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.bar(x='Name',figsize=(5,6),stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "768a5695",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Name'>"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.bar(x='Name',y=['Grade','StudyHours'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "111d0e3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1., 1., 2., 3., 4., 4., 3., 2., 1., 1.]),\n",
       " array([ 3. , 12.4, 21.8, 31.2, 40.6, 50. , 59.4, 68.8, 78.2, 87.6, 97. ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df.StudyHours,bins=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "077e8beb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "669eb969",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "823a4254",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}