day42.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 75,
   "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": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('placement.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 3)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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>689</th>\n",
       "      <td>8.02</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>6.48</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0</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>835</th>\n",
       "      <td>6.67</td>\n",
       "      <td>65.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>772</th>\n",
       "      <td>6.63</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     cgpa  placement_exam_marks  placed\n",
       "689  8.02                  67.0       0\n",
       "111  6.48                  33.0       0\n",
       "991  7.04                  57.0       0\n",
       "835  6.67                  65.0       1\n",
       "772  6.63                  26.0       0"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['placement_exam_marks'].skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean value of cgpa 6.96124000000001\n",
      "Std value of cgpa 0.6158978751323894\n",
      "Min value of cgpa 4.89\n",
      "Max value of cgpa 9.12\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean value of cgpa\",df['cgpa'].mean())\n",
    "print(\"Std value of cgpa\",df['cgpa'].std())\n",
    "print(\"Min value of cgpa\",df['cgpa'].min())\n",
    "print(\"Max value of cgpa\",df['cgpa'].max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Highest allowed 8.808933625397177\n",
      "Lowest allowed 5.113546374602842\n"
     ]
    }
   ],
   "source": [
    "# Finding the boundary values\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": 59,
   "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": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding the outliers\n",
    "df[(df['cgpa'] > 8.80) | (df['cgpa'] < 5.11)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Trimming"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>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": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Trimming\n",
    "\n",
    "new_df = df[(df['cgpa'] < 8.80) & (df['cgpa'] > 5.11)]\n",
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Approach 2\n",
    "\n",
    "# Calculating the Zscore\n",
    "\n",
    "df['cgpa_zscore'] = (df['cgpa'] - df['cgpa'].mean())/df['cgpa'].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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",
       "      <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",
       "  </tbody>\n",
       "</table>\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"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "  <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>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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     cgpa  placement_exam_marks  placed  cgpa_zscore\n",
       "995  8.87                  44.0       1     3.099150\n",
       "996  9.12                  65.0       1     3.505062"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['cgpa_zscore'] > 3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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",
       "      <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>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",
       "997  4.89                  34.0       0    -3.362960\n",
       "999  4.90                  10.0       1    -3.346724"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['cgpa_zscore'] < -3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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>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": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['cgpa_zscore'] > 3) | (df['cgpa_zscore'] < -3)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Trimming \n",
    "new_df = df[(df['cgpa_zscore'] < 3) & (df['cgpa_zscore'] > -3)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <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": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Capping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.113546374602842"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lower_limit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['cgpa'] = 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": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 4)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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, dtype: float64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['cgpa'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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