Z_score.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('placement_2.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>908</th>\n",
       "      <td>6.06</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>788</th>\n",
       "      <td>6.54</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>7.24</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>6.63</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>731</th>\n",
       "      <td>6.74</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     cgpa  placement_exam_marks  placed\n",
       "908  6.06                  17.0       1\n",
       "788  6.54                  19.0       0\n",
       "504  7.24                  25.0       0\n",
       "135  6.63                  47.0       0\n",
       "731  6.74                   5.0       0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1400x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,4))\n",
    "plt.subplot(121)\n",
    "sns.kdeplot(df['cgpa'])\n",
    "\n",
    "plt.subplot(122)\n",
    "sns.kdeplot(df['placement_exam_marks'])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "since placement marks is skewed we can't apply the z_score treatment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean value :  6.96124\n",
      "std value :  0.6158978751323894\n",
      "Min value :  4.89\n",
      "Max value :  9.12\n"
     ]
    }
   ],
   "source": [
    "print('Mean value : ',df['cgpa'].mean())\n",
    "print('std value : ',df['cgpa'].std())\n",
    "print('Min value : ',df['cgpa'].min())\n",
    "print('Max value : ',df['cgpa'].max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Highest allowed :  8.808933625397168\n",
      "lowest allowed :  5.113546374602832\n"
     ]
    }
   ],
   "source": [
    "# boundary values\n",
    "\n",
    "print('Highest allowed : ', df['cgpa'].mean() + 3*df['cgpa'].std())\n",
    "print('lowest allowed : ', df['cgpa'].mean() - 3*df['cgpa'].std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>485</th>\n",
       "      <td>4.92</td>\n",
       "      <td>44.0</td>\n",
       "      <td>1</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>999</th>\n",
       "      <td>4.90</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     cgpa  placement_exam_marks  placed\n",
       "485  4.92                  44.0       1\n",
       "995  8.87                  44.0       1\n",
       "996  9.12                  65.0       1\n",
       "997  4.89                  34.0       0\n",
       "999  4.90                  10.0       1"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# finding outliers\n",
    "\n",
    "df[(df['cgpa']>8.80) | (df['cgpa']<5.11)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Trimming\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>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",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>8.62</td>\n",
       "      <td>46.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>995 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",
       "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",
       "998  8.62                  46.0       1\n",
       "\n",
       "[995 rows x 3 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdf =df[ (df['cgpa']<8.80) & (df['cgpa']>5.11)]\n",
    "newdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Approach by Zscore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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",
       "      <th>cgpa_Zscore</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",
       "      <td>0.371425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.46</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.809810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.54</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.939701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.42</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.878782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.23</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.436371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\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",
       "      <td>3.099150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>9.12</td>\n",
       "      <td>65.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.505062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>4.89</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-3.362960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>8.62</td>\n",
       "      <td>46.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.693239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>4.90</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-3.346724</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     cgpa  placement_exam_marks  placed  cgpa_Zscore\n",
       "0    7.19                  26.0       1     0.371425\n",
       "1    7.46                  38.0       1     0.809810\n",
       "2    7.54                  40.0       1     0.939701\n",
       "3    6.42                   8.0       1    -0.878782\n",
       "4    7.23                  17.0       0     0.436371\n",
       "..    ...                   ...     ...          ...\n",
       "995  8.87                  44.0       1     3.099150\n",
       "996  9.12                  65.0       1     3.505062\n",
       "997  4.89                  34.0       0    -3.362960\n",
       "998  8.62                  46.0       1     2.693239\n",
       "999  4.90                  10.0       1    -3.346724\n",
       "\n",
       "[1000 rows x 4 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['cgpa_Zscore'] = (df['cgpa'] - df['cgpa'].mean())/df['cgpa'].std()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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",
       "      <th>cgpa_Zscore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>485</th>\n",
       "      <td>4.92</td>\n",
       "      <td>44.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-3.314251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>8.87</td>\n",
       "      <td>44.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.099150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>9.12</td>\n",
       "      <td>65.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.505062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>4.89</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-3.362960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>4.90</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-3.346724</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     cgpa  placement_exam_marks  placed  cgpa_Zscore\n",
       "485  4.92                  44.0       1    -3.314251\n",
       "995  8.87                  44.0       1     3.099150\n",
       "996  9.12                  65.0       1     3.505062\n",
       "997  4.89                  34.0       0    -3.362960\n",
       "999  4.90                  10.0       1    -3.346724"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['cgpa_Zscore']>3) | (df['cgpa_Zscore']<-3)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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",
       "      <th>cgpa_Zscore</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",
       "      <td>0.371425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.46</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.809810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.54</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.939701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.42</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.878782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.23</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.436371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>991</th>\n",
       "      <td>7.04</td>\n",
       "      <td>57.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.127878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>992</th>\n",
       "      <td>6.26</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.138565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>993</th>\n",
       "      <td>6.73</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.375452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>994</th>\n",
       "      <td>6.48</td>\n",
       "      <td>63.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.781363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>8.62</td>\n",
       "      <td>46.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.693239</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>995 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     cgpa  placement_exam_marks  placed  cgpa_Zscore\n",
       "0    7.19                  26.0       1     0.371425\n",
       "1    7.46                  38.0       1     0.809810\n",
       "2    7.54                  40.0       1     0.939701\n",
       "3    6.42                   8.0       1    -0.878782\n",
       "4    7.23                  17.0       0     0.436371\n",
       "..    ...                   ...     ...          ...\n",
       "991  7.04                  57.0       0     0.127878\n",
       "992  6.26                  12.0       0    -1.138565\n",
       "993  6.73                  21.0       1    -0.375452\n",
       "994  6.48                  63.0       0    -0.781363\n",
       "998  8.62                  46.0       1     2.693239\n",
       "\n",
       "[995 rows x 4 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdf2 =df[ (df['cgpa_Zscore']<3) & (df['cgpa_Zscore']>-3)]\n",
    "newdf2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Capping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "upper_limit= df['cgpa'].mean() + 3*df['cgpa'].std()\n",
    "lower_limit= df['cgpa'].mean() - 3*df['cgpa'].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['cgpa_capped'] = np.where(\n",
    "    df['cgpa']>upper_limit,\n",
    "    upper_limit,\n",
    "    np.where(\n",
    "        df['cgpa']<lower_limit,\n",
    "        lower_limit,\n",
    "        df['cgpa']\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1000.000000\n",
       "mean        6.961499\n",
       "std         0.612688\n",
       "min         5.113546\n",
       "25%         6.550000\n",
       "50%         6.960000\n",
       "75%         7.370000\n",
       "max         8.808934\n",
       "Name: cgpa_capped, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['cgpa_capped'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ML",
   "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": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}