outlier-removal-using-zscore.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2a534c49",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bde98cd7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df= pd.read_csv(\"placement.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e59cc4e7",
   "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": "ce60e220",
   "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>750</th>\n",
       "      <td>6.43</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>468</th>\n",
       "      <td>6.73</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>6.58</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429</th>\n",
       "      <td>7.35</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>759</th>\n",
       "      <td>6.55</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>736</th>\n",
       "      <td>6.86</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     cgpa  placement_exam_marks  placed\n",
       "750  6.43                   8.0       1\n",
       "468  6.73                  70.0       0\n",
       "38   6.58                  20.0       1\n",
       "429  7.35                  55.0       0\n",
       "759  6.55                  22.0       1\n",
       "736  6.86                  24.0       1"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dd43a21d",
   "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\\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 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": "raw",
   "id": "95f170bc",
   "metadata": {},
   "source": [
    "df['placement_exam_marks'].skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2b4d5793",
   "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": 7,
   "id": "3c1d1416",
   "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": 8,
   "id": "5f68f6aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding the outliers\n",
    "df[(df['cgpa'] > 8.80) | (df['cgpa'] < 5.11)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9259719c",
   "metadata": {},
   "source": [
    "## Trimming"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5d67711f",
   "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>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": 9,
     "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": 10,
   "id": "690bddec",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Approach 2\n",
    "\n",
    "# Calculating the Zscore\n",
    "df['cgpa_zscore'] = (df['cgpa'] - df['cgpa'].mean())/df['cgpa'].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8a543f18",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>cgpa</th>\n",
       "      <th>placement_exam_marks</th>\n",
       "      <th>placed</th>\n",
       "      <th>cgpa_zscore</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>7.82</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.394322</td>\n",
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       "    <tr>\n",
       "      <th>838</th>\n",
       "      <td>6.84</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.196851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>6.70</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.424161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>6.92</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.066959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>508</th>\n",
       "      <td>6.41</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.895019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>6.48</td>\n",
       "      <td>63.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.781363</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     cgpa  placement_exam_marks  placed  cgpa_zscore\n",
       "264  7.82                  28.0       1     1.394322\n",
       "838  6.84                  32.0       0    -0.196851\n",
       "41   6.70                  38.0       0    -0.424161\n",
       "217  6.92                  16.0       0    -0.066959\n",
       "508  6.41                  14.0       1    -0.895019\n",
       "92   6.48                  63.0       0    -0.781363"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2647a993",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>cgpa</th>\n",
       "      <th>placement_exam_marks</th>\n",
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       "      <th>995</th>\n",
       "      <td>8.87</td>\n",
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       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>9.12</td>\n",
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      ],
      "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": 12,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df[df['cgpa_zscore'] > 3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "18f85326",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>cgpa</th>\n",
       "      <th>placement_exam_marks</th>\n",
       "      <th>placed</th>\n",
       "      <th>cgpa_zscore</th>\n",
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       "      <th>485</th>\n",
       "      <td>4.92</td>\n",
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       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>4.89</td>\n",
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       "      <td>0</td>\n",
       "      <td>-3.362960</td>\n",
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       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>4.90</td>\n",
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      ],
      "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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['cgpa_zscore'] < -3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "85e187ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>cgpa</th>\n",
       "      <th>placement_exam_marks</th>\n",
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       "      <th>cgpa_zscore</th>\n",
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       "    <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",
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       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>9.12</td>\n",
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       "    </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",
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       "    <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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['cgpa_zscore'] > 3) | (df['cgpa_zscore'] < -3)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ba05f167",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Trimming\n",
    "new_df = df[(df['cgpa_zscore'] < 3) & (df['cgpa_zscore'] > -3)] \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9af5cda8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>placed</th>\n",
       "      <th>cgpa_zscore</th>\n",
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       "      <th>0</th>\n",
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       "    <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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34e9e8c0",
   "metadata": {},
   "source": [
    "## Capping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1d882508",
   "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": 18,
   "id": "b48bb574",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lower limit is : 5.113546374602842\n",
      "upper limit is : 8.808933625397177\n"
     ]
    }
   ],
   "source": [
    "print(\"lower limit is :\",lower_limit)\n",
    "print(\"upper limit is :\",upper_limit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8e1c4c65",
   "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": 20,
   "id": "25ad0062",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 4)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4cd319a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "403    6.71\n",
       "509    7.83\n",
       "917    5.95\n",
       "653    6.93\n",
       "24     6.44\n",
       "Name: cgpa, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['cgpa'].sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7c677801",
   "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": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['cgpa'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7a9874d7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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