outlier_detection_using_percentile.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c443f61e",
   "metadata": {},
   "source": [
    "# outlier_detection_using_percentile "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a96bfeca",
   "metadata": {},
   "source": [
    "**FORMULAS**\n",
    "* lower_limit = df[\"column_name\"].quantile(min_value)\n",
    "* upper_limit = df[\"column_name\"].quantile(max_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "6339d36c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "8ebc0ab0",
   "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>0</th>\n",
       "      <td>7.19</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.46</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.54</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.42</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.23</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>8.87</td>\n",
       "      <td>44.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>9.12</td>\n",
       "      <td>65.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>4.89</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>8.62</td>\n",
       "      <td>46.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>4.90</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     cgpa  placement_exam_marks  placed\n",
       "0    7.19                  26.0       1\n",
       "1    7.46                  38.0       1\n",
       "2    7.54                  40.0       1\n",
       "3    6.42                   8.0       1\n",
       "4    7.23                  17.0       0\n",
       "..    ...                   ...     ...\n",
       "995  8.87                  44.0       1\n",
       "996  9.12                  65.0       1\n",
       "997  4.89                  34.0       0\n",
       "998  8.62                  46.0       1\n",
       "999  4.90                  10.0       1\n",
       "\n",
       "[1000 rows x 3 columns]"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.read_csv(\"dataset/placement.csv\")\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "411a2c45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 3)"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "432b21aa",
   "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>count</th>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.961240</td>\n",
       "      <td>32.225000</td>\n",
       "      <td>0.489000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.615898</td>\n",
       "      <td>19.130822</td>\n",
       "      <td>0.500129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.890000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.550000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.960000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.370000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.120000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              cgpa  placement_exam_marks       placed\n",
       "count  1000.000000           1000.000000  1000.000000\n",
       "mean      6.961240             32.225000     0.489000\n",
       "std       0.615898             19.130822     0.500129\n",
       "min       4.890000              0.000000     0.000000\n",
       "25%       6.550000             17.000000     0.000000\n",
       "50%       6.960000             28.000000     0.000000\n",
       "75%       7.370000             44.000000     1.000000\n",
       "max       9.120000            100.000000     1.000000"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "eea7baa0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\CS-L2-PC09\\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": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8,4))\n",
    "sns.distplot(df1[\"cgpa\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "65bfada3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\CS-L2-PC09\\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": "iVBORw0KGgoAAAANSUhEUgAAAdAAAAEGCAYAAADVOsQyAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAL/klEQVR4nO3dX4yl9V3H8c+XHQx/IlGWSuJGXeom1VZNRESrhjQWDNsYG2uiNDEQvahCs2zBXpj0SoOJJt7AmmBIG7urFqMoF0TYFHrRWiPVBdryZ0kYcand2gqzLVVZhV1+XszsOixTWL7OznOG83olmz0z58w533ly5vee5zm756kxRgCAN+asqQcAgM1IQAGgQUABoEFAAaBBQAGgYeGN3Piiiy4a27dvP0OjAMBseeihh54bY7xlreveUEC3b9+eAwcOrM9UADDjquqZb3WdQ7gA0CCgANAgoADQIKAA0CCgANAgoADQIKAA0CCgANAgoADQIKAA0CCgANAgoADQIKAA0CCgANAgoADQIKAA0CCgANAgoADQIKAA0LAw9QCw0fbs2ZPFxcWpx/iWDh8+nCTZtm3bxJOsbceOHdm1a9fUY8DkBJS5s7i4mM8/djDHz7tw6lHWtOWF55MkX/2f2fvx3PLCkalHgJkxez+hsAGOn3dhjv7Ae6YeY03nPnlvkszkfCdmA7wGCgAtAgoADQIKAA0CCgANAgoADQIKAA0CCgANAgoADQIKAA0CCgANAgoADQIKAA0CCgANAgoADQIKAA0CCgANAgoADQIKAA0CCgANAgoADQIKAA0CCgANAgoADQIKAA0CCgANAgoADQIKAA0CCgANAgoADQIKAA0CCgANAgoADQIKAA0CCgANAgoADQIKAA0CCgANAgoADQIKAA0CCgANAgoADQK6Dvbs2ZM9e/ZMPQbAabFmrY+FqQd4M1hcXJx6BIDTZs1aH/ZAAaBBQAGgQUABoEFAAaBBQAGgQUABoEFAAaBBQAGgQUABoEFAAaBBQAGgQUABoEFAAaBBQAGgQUABoEFAAaBBQAGgQUABoEFAAaBBQAGgQUABoEFAAaBBQAGgQUABoEFAAaBBQAGgQUABoEFAAaBBQAGgQUABoEFAAaBBQAGgQUABoEFAAaBBQAGgQUABoEFAAaBBQAGgYbKALi0t5frrr88NN9yQxcXF3HjjjVlaWtrwGU487urLAPPoTK+Dm/3+TzVZQPfu3ZuDBw/miSeeyC233JJHH300+/bt2/AZTjzu6ssA8+hMr4Ob/f5PNUlAl5aWct999538+NChQxljZP/+/Rv2m8PS0lL279+fMUbuu+++k5c3cgaAWbF6TTwT6+Bmv/+1LJzxR1jD3r17c+zYsVd9/vjx49m3b19uuummDZnh5ZdfTpK89NJL/68ZDh8+nKNHj2b37t3rPifrb3FxMWe9OKYeY1M667+/mcXF//Bc3+QWFxdz7rnnvuJzq9fEM7EWb/b7X8vr7oFW1Qeq6kBVHXj22WfX5UEfeOCBjPHqBezYsWO5//771+UxTmeGExEfY5ycZyNnAJgVq9fEM7EObvb7X8vr7oGOMe5IckeSXHbZZevya/uVV16Ze+6551URXVhYyFVXXbUeD3FaM9x77705duxYqirJckg7M2zbti1Jcuutt677nKy/3bt356Gnvzb1GJvSy+dckB1vvdhzfZNb6wjC6jXxTKzFm/3+1zLJa6DXXXddFhZe3e4tW7bk2muv3bAZzjpr+ds/++yzc/bZZ2/4DACzYvWaeCbWwc1+/2uZJKBbt27Nzp07T368ffv2VFWuvvrqbN26dcNmuPrqq1NV2blz58nLGzkDwKxYvSaeiXVws9//Wib5R0TJ8m8LTz31VKoqN998c2677bYN3/O77rrrcujQoZOPu/oywLw5dU10/69tsoBu3bo1t99++8mPb7vttklmWP24U8wAMCtOXRPd/2vzVn4A0CCgANAgoADQIKAA0CCgANAgoADQIKAA0CCgANAgoADQIKAA0CCgANAgoADQIKAA0CCgANAgoADQIKAA0CCgANAgoADQIKAA0CCgANAgoADQIKAA0CCgANAgoADQIKAA0CCgANAgoADQIKAA0CCgANAgoADQIKAA0CCgANAgoADQIKAA0CCgANAgoADQIKAA0CCgANCwMPUAbwY7duyYegSA02bNWh8Cug527do19QgAp82atT4cwgWABgEFgAYBBYAGAQWABgEFgAYBBYAGAQWABgEFgAYBBYAGAQWABgEFgAYBBYAGAQWABgEFgAYBBYAGAQWABgEFgAYBBYAGAQWABgEFgAYBBYAGAQWABgEFgAYBBYAGAQWABgEFgAYBBYAGAQWABgEFgAYBBYAGAQWABgEFgAYBBYAGAQWABgEFgAYBBYAGAQWABgEFgAYBBYAGAQWAhoWpB4ApbHnhSM598t6px1jTlheWkmQm59vywpEkF089BswEAWXu7NixY+oRXtPhw8eSJNu2zWKoLp757QcbRUCZO7t27Zp6BOBNwGugANAgoADQIKAA0CCgANAgoADQIKAA0CCgANAgoADQIKAA0CCgANAgoADQIKAA0CCgANAgoADQIKAA0CCgANAgoADQIKAA0CCgANAgoADQUGOM079x1bNJnjlz47ymi5I8N9Fjzzvbflq2/7Rs/2lNvf2/b4zxlrWueEMBnVJVHRhjXDb1HPPItp+W7T8t239as7z9HcIFgAYBBYCGzRTQO6YeYI7Z9tOy/adl+09rZrf/pnkNFABmyWbaAwWAmSGgANAw8wGtqkNV9WhVfb6qDkw9z7ypqu+oqruq6smqOlhV75x6pnlRVW9bed6f+PPNqvrQ1HPNk6q6qaoer6rHqurOqjpn6pnmSVXtXtn2j8/ic3/mXwOtqkNJLhtj+I/ME6iqvUn+bozx0ar6tiTnjTG+MfFYc6eqtiQ5nOQnxhhTvZnJXKmqbUk+m+TtY4yjVfWXSe4dY3x82snmQ1X9UJK/SHJ5kheT7E9y/RjjqUkHW2Xm90CZTlVdkOSKJB9LkjHGi+I5mXcn+Wfx3HALSc6tqoUk5yX5ysTzzJMfTPLgGOOFMcaxJJ9O8osTz/QKmyGgI8knq+qhqvrA1MPMmbcmeTbJn1TVI1X10ao6f+qh5tQ1Se6ceoh5MsY4nOQPk3wpyb8leX6M8clpp5orjyW5oqq2VtV5Sd6T5HsmnukVNkNAf3qMcWmSnUk+WFVXTD3QHFlIcmmS28cYP5rkv5L89rQjzZ+VQ+e/kOSvpp5lnlTVdyZ5b5JLknx3kvOr6lennWp+jDEOJvmDJPdn+fDtF5Icm3SoU8x8QMcYX1n5+9+T3J3l4+FsjC8n+fIY43MrH9+V5aCysXYmeXiM8bWpB5kzVyb5lzHGs2OMl5L8TZKfmnimuTLG+NgY49IxxhVJjiSZmdc/kxkPaFWdX1XffuJykp/L8m49G2CM8dUk/1pVb1v51LuTPDHhSPPq/XH4dgpfSvKTVXVeVVWWn/8HJ55prlTVd638/b1J3pcZ+zlYmHqA13FxkruXn7tZSPKJMcb+aUeaO7uS/PnKYcSnk/zaxPPMlZXXfq5K8htTzzJvxhifq6q7kjyc5UOHj2SG31buTeqvq2prkpeSfHCM8fWpB1pt5v8bCwDMopk+hAsAs0pAAaBBQAGgQUABoEFAAaBBQAGgQUABoEFAYSJVdW1VfbGqvlBVf1pV319VD1bVP1XV71bVf67c7l1V9ZmquruqnqiqP66qs1auu72qDqycL/F3pv2OYL7M+jsRwZtSVb0jyUeyfLKE56rqwiT7ktw6xrizqn7zlC+5PMnbkzyT5TfWfl+W35v4I2OMIyvnC/1UVf3IGOOLG/edwPyyBwrT+Nkkd504UfwY40iSd+b/zrjyiVNu/49jjKfHGMez/H6gP7Py+V+uqoez/DZz78hyZIENYA8UplFZPtft6Tr1tqOqLkny4SQ/Psb4elV9PMk56zQf8DrsgcI0PpXlvcetSbJyCPfBJL+0cv01p9z+8qq6ZOW1z19J8tkkF2T5HK3PV9XFWT7tGbBB7IHCBMYYj1fV7yX5dFUdz/Ih2A8l+bOq+q0kf5vk+VVf8g9Jfj/JDyf5TJK7xxgvV9UjSR7P8ply/n4DvwWYe87GAjNi5dRlR8cYo6quSfL+McZ7q+pdST48xvj5SQcEXsEeKMyOH0vyRysnb/5Gkl+fdhzgtdgDBYAG/4gIABoEFAAaBBQAGgQUABoEFAAa/hdEzVQfOBjxsQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8,4))\n",
    "sns.boxplot(df1[\"cgpa\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "33c8a132",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.014529938929314918"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[\"cgpa\"].skew()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd16ecef",
   "metadata": {},
   "source": [
    "**Finding the upper limit**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "e94d7f83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.4201"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ul = df1[\"cgpa\"].quantile(0.99)\n",
    "ul"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5793c8f7",
   "metadata": {},
   "source": [
    "**Finding the lower limit**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "8d811023",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.5596"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ll = df1[\"cgpa\"].quantile(0.01)\n",
    "ll"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc662f78",
   "metadata": {},
   "source": [
    "**Finding the number of outlier**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "4629521e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cgpa                    20\n",
       "placement_exam_marks    20\n",
       "placed                  20\n",
       "dtype: int64"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[\"cgpa\"].count() - df1[(df1[\"cgpa\"] < ul) & (df1[\"cgpa\"] > ll)].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a38a124",
   "metadata": {},
   "source": [
    "**Trimming**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "766c7fd6",
   "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>0</th>\n",
       "      <td>7.19</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.46</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.54</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.42</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.23</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>990</th>\n",
       "      <td>6.17</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>991</th>\n",
       "      <td>7.04</td>\n",
       "      <td>57.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>992</th>\n",
       "      <td>6.26</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>993</th>\n",
       "      <td>6.73</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>994</th>\n",
       "      <td>6.48</td>\n",
       "      <td>63.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>980 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     cgpa  placement_exam_marks  placed\n",
       "0    7.19                  26.0       1\n",
       "1    7.46                  38.0       1\n",
       "2    7.54                  40.0       1\n",
       "3    6.42                   8.0       1\n",
       "4    7.23                  17.0       0\n",
       "..    ...                   ...     ...\n",
       "990  6.17                  33.0       1\n",
       "991  7.04                  57.0       0\n",
       "992  6.26                  12.0       0\n",
       "993  6.73                  21.0       1\n",
       "994  6.48                  63.0       0\n",
       "\n",
       "[980 rows x 3 columns]"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df1[(df1[\"cgpa\"] < ul) & (df1[\"cgpa\"] > ll)]\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "816b1b42",
   "metadata": {},
   "source": [
    "**capping**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "05e663f9",
   "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>0</th>\n",
       "      <td>7.19</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.46</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.54</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.42</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.23</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>8.87</td>\n",
       "      <td>44.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>9.12</td>\n",
       "      <td>65.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>4.89</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>8.62</td>\n",
       "      <td>46.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>4.90</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     cgpa  placement_exam_marks  placed\n",
       "0    7.19                  26.0       1\n",
       "1    7.46                  38.0       1\n",
       "2    7.54                  40.0       1\n",
       "3    6.42                   8.0       1\n",
       "4    7.23                  17.0       0\n",
       "..    ...                   ...     ...\n",
       "995  8.87                  44.0       1\n",
       "996  9.12                  65.0       1\n",
       "997  4.89                  34.0       0\n",
       "998  8.62                  46.0       1\n",
       "999  4.90                  10.0       1\n",
       "\n",
       "[1000 rows x 3 columns]"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = df1.copy()\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "5c22dac7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df3['cgpa'] = np.where(\n",
    "    df3['cgpa']>ul,\n",
    "    ul,\n",
    "    np.where(\n",
    "        df3['cgpa']<ll,\n",
    "        ll,\n",
    "        df3['cgpa']\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "5e78fa62",
   "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>0</th>\n",
       "      <td>7.1900</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.4600</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.5400</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.4200</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.2300</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>8.4201</td>\n",
       "      <td>44.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>8.4201</td>\n",
       "      <td>65.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>5.5596</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>8.4201</td>\n",
       "      <td>46.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>5.5596</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       cgpa  placement_exam_marks  placed\n",
       "0    7.1900                  26.0       1\n",
       "1    7.4600                  38.0       1\n",
       "2    7.5400                  40.0       1\n",
       "3    6.4200                   8.0       1\n",
       "4    7.2300                  17.0       0\n",
       "..      ...                   ...     ...\n",
       "995  8.4201                  44.0       1\n",
       "996  8.4201                  65.0       1\n",
       "997  5.5596                  34.0       0\n",
       "998  8.4201                  46.0       1\n",
       "999  5.5596                  10.0       1\n",
       "\n",
       "[1000 rows x 3 columns]"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "f32ee1d1",
   "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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [cgpa, placement_exam_marks, placed]\n",
       "Index: []"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3[(df3[\"cgpa\"] > ul) |  (df3[\"cgpa\"] < ll)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "d8884438",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\CS-L2-PC09\\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\\CS-L2-PC09\\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\\CS-L2-PC09\\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\\CS-L2-PC09\\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 720x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.subplot(2,2,1)\n",
    "sns.distplot(df1[\"cgpa\"])\n",
    "\n",
    "plt.subplot(2,2,2)\n",
    "sns.boxplot(df1[\"cgpa\"])\n",
    "\n",
    "#New\n",
    "plt.subplot(2,2,3)\n",
    "sns.distplot(df3[\"cgpa\"])\n",
    "\n",
    "plt.subplot(2,2,4)\n",
    "sns.boxplot(df3[\"cgpa\"])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ea4575d",
   "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
}