Outlier Detection and Removal using Z-score .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "559ee2c1",
   "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": 2,
   "id": "37609986",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"https://raw.githubusercontent.com/campusx-official/100-days-of-machine-learning/main/day42-outlier-removal-using-zscore/placement.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e68478d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\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",
       "  </tbody>\n",
       "</table>\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"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fbd0fa4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='placement_exam_marks', ylabel='Count'>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,(ax1,ax2)=plt.subplots(ncols=2,figsize=(15,5))\n",
    "sns.histplot(df[\"cgpa\"],ax=ax1,kde=True)\n",
    "sns.histplot(df[\"placement_exam_marks\"],ax=ax2,kde=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "335920aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.figure(figsize=(15,5))\n",
    "# ax1=plt.subplots(121)\n",
    "# sns.histplot(df[\"cgpa\"],ax=ax1,kde=True)\n",
    "# ax2=plt.subplots(122)\n",
    "# sns.histplot(df[\"placement_exam_marks\"],ax=ax2,kde=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ceb54485",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Abdur rahim nishad\\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\\Abdur rahim nishad\\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": "code",
   "execution_count": 7,
   "id": "139093d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "mean=df[\"cgpa\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9fab2afe",
   "metadata": {},
   "outputs": [],
   "source": [
    "std=df[\"cgpa\"].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b108270b",
   "metadata": {},
   "outputs": [],
   "source": [
    "upper_limit=mean+3*std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "be31f19d",
   "metadata": {},
   "outputs": [],
   "source": [
    "lower_limit=mean-3*std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3d73d822",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8.808933625397177, 5.113546374602842)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "upper_limit,lower_limit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2e6d9ec9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>485</th>\n",
       "      <td>4.92</td>\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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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>4.89</td>\n",
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       "      <td>0</td>\n",
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       "      <th>999</th>\n",
       "      <td>4.90</td>\n",
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       "    </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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df[\"cgpa\"]>8.808933625397177) | (df[\"cgpa\"]<5.113546374602842)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eda8fdcf",
   "metadata": {},
   "source": [
    "# Trimming"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "890f525a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#approch 1\n",
    "new_df=df[(df[\"cgpa\"]<8.808933625397177) & (df[\"cgpa\"]>5.113546374602842)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c9a0ab6e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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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",
       "    </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",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.46</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.54</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.42</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.23</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>991</th>\n",
       "      <td>7.04</td>\n",
       "      <td>57.0</td>\n",
       "      <td>0</td>\n",
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       "    <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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2d8fadde",
   "metadata": {},
   "outputs": [],
   "source": [
    "#approch 2\n",
    "df[\"cgpa_zscore\"]=df[\"cgpa\"]-df[\"cgpa\"].mean()/df[\"cgpa\"].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0f201e24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <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>-4.112588</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>-3.842588</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>-3.762588</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>-4.882588</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>-4.072588</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    -4.112588\n",
       "1  7.46                  38.0       1    -3.842588\n",
       "2  7.54                  40.0       1    -3.762588\n",
       "3  6.42                   8.0       1    -4.882588\n",
       "4  7.23                  17.0       0    -4.072588"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "268c653a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>7.19</td>\n",
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       "      <td>1</td>\n",
       "      <td>-4.112588</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>-3.842588</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>-3.762588</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>-4.882588</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>-4.072588</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>992</th>\n",
       "      <td>6.26</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-5.042588</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>-4.572588</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>-4.822588</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>-6.412588</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>-6.402588</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>986 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     cgpa  placement_exam_marks  placed  cgpa_zscore\n",
       "0    7.19                  26.0       1    -4.112588\n",
       "1    7.46                  38.0       1    -3.842588\n",
       "2    7.54                  40.0       1    -3.762588\n",
       "3    6.42                   8.0       1    -4.882588\n",
       "4    7.23                  17.0       0    -4.072588\n",
       "..    ...                   ...     ...          ...\n",
       "992  6.26                  12.0       0    -5.042588\n",
       "993  6.73                  21.0       1    -4.572588\n",
       "994  6.48                  63.0       0    -4.822588\n",
       "997  4.89                  34.0       0    -6.412588\n",
       "999  4.90                  10.0       1    -6.402588\n",
       "\n",
       "[986 rows x 4 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df[\"cgpa_zscore\"]>3) | (df[\"cgpa_zscore\"]< -3)]\n",
    "# df[(df['cgpa_zscore'] > 3) | (df['cgpa_zscore'] < -3)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "73fde694",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df = df[(df['cgpa_zscore'] < 3) & (df['cgpa_zscore'] > -3)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2041784b",
   "metadata": {},
   "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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       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>8.51</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-2.792588</td>\n",
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       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>8.42</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>8.35</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-2.952588</td>\n",
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       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>8.53</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-2.772588</td>\n",
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       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>8.49</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-2.812588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>8.38</td>\n",
       "      <td>87.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-2.922588</td>\n",
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       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>8.56</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-2.742588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>492</th>\n",
       "      <td>8.50</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-2.802588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>596</th>\n",
       "      <td>8.48</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-2.822588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>850</th>\n",
       "      <td>8.39</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-2.912588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>975</th>\n",
       "      <td>8.43</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-2.872588</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>-2.432588</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>-2.182588</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>-2.682588</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     cgpa  placement_exam_marks  placed  cgpa_zscore\n",
       "37   8.51                   9.0       1    -2.792588\n",
       "54   8.42                  36.0       1    -2.882588\n",
       "76   8.35                  12.0       0    -2.952588\n",
       "150  8.53                  22.0       0    -2.772588\n",
       "279  8.49                  40.0       1    -2.812588\n",
       "290  8.38                  87.0       0    -2.922588\n",
       "440  8.56                  22.0       0    -2.742588\n",
       "492  8.50                  21.0       1    -2.802588\n",
       "596  8.48                  25.0       1    -2.822588\n",
       "850  8.39                  37.0       1    -2.912588\n",
       "975  8.43                  25.0       1    -2.872588\n",
       "995  8.87                  44.0       1    -2.432588\n",
       "996  9.12                  65.0       1    -2.182588\n",
       "998  8.62                  46.0       1    -2.682588"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe80bec2",
   "metadata": {},
   "source": [
    "# capping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "dc1b211b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.808933625397177"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "upper_limit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "99312399",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.113546374602842"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lower_limit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "352b6841",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"cgpa\"]=np.where(\n",
    "    df[\"cgpa\"]>upper_limit,upper_limit,\n",
    "    np.where(df[\"cgpa\"]<lower_limit,\n",
    "            lower_limit,\n",
    "            df[\"cgpa\"])\n",
    "\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7f7b42fe",
   "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>-4.112588</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>-3.842588</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>-3.762588</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>-4.882588</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>-4.072588</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    -4.112588\n",
       "1  7.46                  38.0       1    -3.842588\n",
       "2  7.54                  40.0       1    -3.762588\n",
       "3  6.42                   8.0       1    -4.882588\n",
       "4  7.23                  17.0       0    -4.072588"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b2f190ea",
   "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": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.cgpa.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "953cb65d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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