Outlier Detection and Removal using the IQR Method .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "09868996",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "261e60c1",
   "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>cgpa</th>\n",
       "      <th>placement_exam_marks</th>\n",
       "      <th>placed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>374</th>\n",
       "      <td>7.34</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494</th>\n",
       "      <td>6.83</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>7.45</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>6.62</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "     cgpa  placement_exam_marks  placed\n",
       "374  7.34                  41.0       0\n",
       "494  6.83                  51.0       0\n",
       "26   7.45                   8.0       1\n",
       "280  6.62                  55.0       0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"placement.csv\")\n",
    "df.sample(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9812f227",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 3)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "36fb0baa",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\nagam\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\nagam\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,5))\n",
    "plt.subplot(1,2,1)\n",
    "sns.distplot(df['cgpa'])\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "sns.distplot(df['placement_exam_marks'])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e3c3a9eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculating skewness of placement_exam_marks : 0.8356419499466834\n",
      "calculating skewness of cgpa : -0.014529938929314918\n"
     ]
    }
   ],
   "source": [
    "print(\"calculating skewness of placement_exam_marks :\",df['placement_exam_marks'].skew())\n",
    "print(\"calculating skewness of cgpa :\",df['cgpa'].skew())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "52e1f777",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean value of pem 32.225\n",
      "std value of pem 19.13082233892108\n",
      "min value of pem 0.0\n",
      "max value of pem 100.0\n",
      "\u001b[1m\n",
      " \n",
      " describe values : \n",
      "\n",
      "\u001b[0m count    1000.000000\n",
      "mean       32.225000\n",
      "std        19.130822\n",
      "min         0.000000\n",
      "25%        17.000000\n",
      "50%        28.000000\n",
      "75%        44.000000\n",
      "max       100.000000\n",
      "Name: placement_exam_marks, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(\"mean value of pem\",df['placement_exam_marks'].mean())\n",
    "print(\"std value of pem\",df['placement_exam_marks'].std())\n",
    "print(\"min value of pem\",df['placement_exam_marks'].min())\n",
    "print(\"max value of pem\",df['placement_exam_marks'].max())\n",
    "# or\n",
    "print('\\033[1m' + '\\n \\n describe values : \\n\\n' + '\\033[0m',\n",
    "      df['placement_exam_marks'].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0e440e59",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\nagam\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='placement_exam_marks'>"
      ]
     },
     "execution_count": 7,
     "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": [
    "sns.boxplot(df['placement_exam_marks'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "065b4193",
   "metadata": {},
   "outputs": [],
   "source": [
    "# FINDING THE IQR\n",
    "percentile25 = df['placement_exam_marks'].quantile(0.25)\n",
    "percentile75 = df['placement_exam_marks'].quantile(0.75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "998d6ad0",
   "metadata": {},
   "outputs": [],
   "source": [
    "iqr = percentile75 - percentile25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2925b9d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "percentile25  is : 17.0\n",
      "percentile75  is : 44.0\n",
      "iqr is : 27.0\n"
     ]
    }
   ],
   "source": [
    "print(\"percentile25  is :\",percentile25)\n",
    "print(\"percentile75  is :\",percentile75)\n",
    "print(\"iqr is :\",iqr )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7e7b3a4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "upper_limit = percentile75 + 1.5 *iqr\n",
    "lower_limit = percentile25 - 1.5 *iqr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "295f4189",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lower_limit  is : -23.5\n",
      "upper_limit  is : 84.5\n"
     ]
    }
   ],
   "source": [
    "print(\"lower_limit  is :\",lower_limit)\n",
    "print(\"upper_limit  is :\",upper_limit)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9959b1a9",
   "metadata": {},
   "source": [
    "## Finding Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7aa305ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>cgpa</th>\n",
       "      <th>placement_exam_marks</th>\n",
       "      <th>placed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7.75</td>\n",
       "      <td>94.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>6.60</td>\n",
       "      <td>86.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>7.51</td>\n",
       "      <td>86.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>6.33</td>\n",
       "      <td>93.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>7.80</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>7.09</td>\n",
       "      <td>87.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>8.38</td>\n",
       "      <td>87.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311</th>\n",
       "      <td>6.97</td>\n",
       "      <td>87.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>6.64</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>630</th>\n",
       "      <td>6.56</td>\n",
       "      <td>96.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>685</th>\n",
       "      <td>6.05</td>\n",
       "      <td>87.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>730</th>\n",
       "      <td>6.14</td>\n",
       "      <td>90.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>771</th>\n",
       "      <td>7.31</td>\n",
       "      <td>86.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>846</th>\n",
       "      <td>6.99</td>\n",
       "      <td>97.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>917</th>\n",
       "      <td>5.95</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     cgpa  placement_exam_marks  placed\n",
       "9    7.75                  94.0       1\n",
       "40   6.60                  86.0       1\n",
       "61   7.51                  86.0       0\n",
       "134  6.33                  93.0       0\n",
       "162  7.80                  90.0       0\n",
       "283  7.09                  87.0       0\n",
       "290  8.38                  87.0       0\n",
       "311  6.97                  87.0       1\n",
       "324  6.64                  90.0       0\n",
       "630  6.56                  96.0       1\n",
       "685  6.05                  87.0       1\n",
       "730  6.14                  90.0       1\n",
       "771  7.31                  86.0       1\n",
       "846  6.99                  97.0       0\n",
       "917  5.95                 100.0       0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['placement_exam_marks'] > upper_limit]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8325760f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cgpa</th>\n",
       "      <th>placement_exam_marks</th>\n",
       "      <th>placed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [cgpa, placement_exam_marks, placed]\n",
       "Index: []"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df[df['placement_exam_marks'] < lower_limit]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37841536",
   "metadata": {},
   "source": [
    "### Trimming"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a4f762bf",
   "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>cgpa</th>\n",
       "      <th>placement_exam_marks</th>\n",
       "      <th>placed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>811</th>\n",
       "      <td>8.04</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>808</th>\n",
       "      <td>7.68</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>7.70</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>7.28</td>\n",
       "      <td>34.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>830</th>\n",
       "      <td>6.46</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     cgpa  placement_exam_marks  placed\n",
       "811  8.04                  14.0       0\n",
       "808  7.68                  31.0       0\n",
       "59   7.70                  32.0       1\n",
       "196  7.28                  34.0       1\n",
       "830  6.46                  28.0       0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df = df[df['placement_exam_marks']<upper_limit]\n",
    "new_df.sample(5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "26f7144d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(985, 3)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "dac7de57",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\nagam\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\nagam\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "C:\\Users\\nagam\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\nagam\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#comparing\n",
    "\n",
    "plt.figure(figsize=(16,9))\n",
    "plt.subplot(2,2,1)\n",
    "sns.distplot(df['placement_exam_marks'])\n",
    "\n",
    "plt.subplot(2,2,2)\n",
    "sns.boxplot(df['placement_exam_marks'])\n",
    "\n",
    "plt.subplot(2,2,3)\n",
    "sns.distplot(new_df['placement_exam_marks'])\n",
    "\n",
    "plt.subplot(2,2,4)\n",
    "sns.boxplot(new_df['placement_exam_marks'])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3177807c",
   "metadata": {},
   "source": [
    "## Capping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "47786264",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df_cap = df.copy()\n",
    "\n",
    "new_df_cap['placement_exam_marks'] = np.where(\n",
    "    new_df_cap['placement_exam_marks']>upper_limit,\n",
    "    upper_limit,\n",
    "    np.where(\n",
    "        new_df_cap['placement_exam_marks']<lower_limit,\n",
    "        lower_limit,\n",
    "        new_df_cap['placement_exam_marks']\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "23b6adb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 3)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df_cap.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "118dcc67",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\nagam\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\nagam\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "C:\\Users\\nagam\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\nagam\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#comparing\n",
    "\n",
    "plt.figure(figsize=(16,9))\n",
    "plt.subplot(2,2,1)\n",
    "sns.distplot(df['placement_exam_marks'])\n",
    "\n",
    "plt.subplot(2,2,2)\n",
    "sns.boxplot(df['placement_exam_marks'])\n",
    "\n",
    "plt.subplot(2,2,3)\n",
    "sns.distplot(new_df_cap['placement_exam_marks'])\n",
    "\n",
    "plt.subplot(2,2,4)\n",
    "sns.boxplot(new_df_cap['placement_exam_marks'])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eb0b33f4",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}