Outlier_Percentile.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9a9ca99a-9567-4c79-8932-aeea53fadf02",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Temp\\ipykernel_9212\\2660915427.py:2: DeprecationWarning: \n",
      "Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),\n",
      "(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)\n",
      "but was not found to be installed on your system.\n",
      "If this would cause problems for you,\n",
      "please provide us feedback at https://github.com/pandas-dev/pandas/issues/54466\n",
      "        \n",
      "  import pandas as pd\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "ec0726a3-e63f-4ede-bdcf-20f08565f857",
   "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>Gender</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3630</th>\n",
       "      <td>Male</td>\n",
       "      <td>67.290283</td>\n",
       "      <td>187.001020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1083</th>\n",
       "      <td>Male</td>\n",
       "      <td>71.092956</td>\n",
       "      <td>198.829117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2290</th>\n",
       "      <td>Male</td>\n",
       "      <td>63.273151</td>\n",
       "      <td>143.248672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3034</th>\n",
       "      <td>Male</td>\n",
       "      <td>71.363595</td>\n",
       "      <td>212.594865</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Gender     Height      Weight\n",
       "3630   Male  67.290283  187.001020\n",
       "1083   Male  71.092956  198.829117\n",
       "2290   Male  63.273151  143.248672\n",
       "3034   Male  71.363595  212.594865"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"weight-height.csv\")\n",
    "df.sample(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb7e21ee-9d6d-4079-a792-2f4df266054f",
   "metadata": {},
   "source": [
    "#using percentile to detetct outliers.\n",
    "#we set the amouunt of duistribution on the oth side like if  say more then 90% are outlier and less then 5% are outlier.\n",
    "1:Detected the outlier remove them\n",
    "2: capping -->it is called winserization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "97ac5703-141d-47c3-aef1-75c28262de26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender    0.0\n",
       "Height    0.0\n",
       "Weight    0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "0356373e-c0b2-40b9-9a3c-66e6abe41ea8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    10000.000000\n",
       "mean        66.367560\n",
       "std          3.847528\n",
       "min         54.263133\n",
       "25%         63.505620\n",
       "50%         66.318070\n",
       "75%         69.174262\n",
       "max         78.998742\n",
       "Name: Height, dtype: float64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Height\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b407b40d-a820-4fab-a55b-d499a22d1116",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Temp\\ipykernel_9212\\1870637031.py:1: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(df[\"Height\"])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Height', ylabel='Density'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df[\"Height\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "2e232198-b029-4dda-bcf9-1815eb84cdbd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Temp\\ipykernel_9212\\3250964047.py:1: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(df[\"Weight\"])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Weight')"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df[\"Weight\"])\n",
    "plt.title(\"Weight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f8571fe9-e3b3-4e1d-ae2b-5ba991397991",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Height')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df[\"Height\"])\n",
    "plt.title(\"Height\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "14e76709-9315-422f-82f6-66e33a945c82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Weight')"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df[\"Weight\"])\n",
    "plt.title(\"Weight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "bfde1410-f9db-435b-8356-48ac6b103ecb",
   "metadata": {},
   "outputs": [],
   "source": [
    "upper_limit=df[\"Height\"].quantile(0.99)\n",
    "lower_limit=df[\"Height\"].quantile(0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "080ca518-a2cf-47aa-95c1-d02806804e71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "74.7857900583366"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lower_limit\n",
    "upper_limit\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "6ed818b3-26aa-4bb8-bed5-530c8019d2dc",
   "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>Gender</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Male</td>\n",
       "      <td>75.205974</td>\n",
       "      <td>228.761781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>Male</td>\n",
       "      <td>76.709835</td>\n",
       "      <td>235.035419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>Male</td>\n",
       "      <td>75.944460</td>\n",
       "      <td>231.924749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>Male</td>\n",
       "      <td>75.140821</td>\n",
       "      <td>224.124271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>Male</td>\n",
       "      <td>74.795375</td>\n",
       "      <td>232.635403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9761</th>\n",
       "      <td>Female</td>\n",
       "      <td>56.975279</td>\n",
       "      <td>90.341784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9825</th>\n",
       "      <td>Female</td>\n",
       "      <td>55.979198</td>\n",
       "      <td>85.417534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9895</th>\n",
       "      <td>Female</td>\n",
       "      <td>57.740192</td>\n",
       "      <td>93.652957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9904</th>\n",
       "      <td>Female</td>\n",
       "      <td>57.028857</td>\n",
       "      <td>101.202551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9978</th>\n",
       "      <td>Female</td>\n",
       "      <td>57.375759</td>\n",
       "      <td>114.192209</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Gender     Height      Weight\n",
       "23      Male  75.205974  228.761781\n",
       "190     Male  76.709835  235.035419\n",
       "197     Male  75.944460  231.924749\n",
       "202     Male  75.140821  224.124271\n",
       "215     Male  74.795375  232.635403\n",
       "...      ...        ...         ...\n",
       "9761  Female  56.975279   90.341784\n",
       "9825  Female  55.979198   85.417534\n",
       "9895  Female  57.740192   93.652957\n",
       "9904  Female  57.028857  101.202551\n",
       "9978  Female  57.375759  114.192209\n",
       "\n",
       "[200 rows x 3 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df[\"Height\"] >=upper_limit) | (df[\"Height\"] <=lower_limit)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "b9fc023f-f054-4f4e-bd11-45ec41b76e55",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Height\"]=np.where(\n",
    "    df[\"Height\"]>=upper_limit,\n",
    "    upper_limit,\n",
    "    np.where(df[\"Height\"]<=lower_limit,\n",
    "    lower_limit,\n",
    "    df[\"Height\"])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "5b0ba6cf-66f3-4e48-a6ab-9743b1bc1148",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Height'>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df[\"Height\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "6d700be9-2151-4909-8e75-c3409d33bd6e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Temp\\ipykernel_9212\\1870637031.py:1: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(df[\"Height\"])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Height', ylabel='Density'>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df[\"Height\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "fa9f37b1-589a-4a21-936e-eeeb38830ba2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    10000.000000\n",
       "mean        66.366281\n",
       "std          3.795717\n",
       "min         58.134412\n",
       "25%         63.505620\n",
       "50%         66.318070\n",
       "75%         69.174262\n",
       "max         74.785790\n",
       "Name: Height, dtype: float64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Height\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "30178e8e-4c49-4a04-a7f1-b7499195302d",
   "metadata": {},
   "outputs": [],
   "source": [
    "count    10000.000000\n",
    "mean        66.367560\n",
    "std          3.847528\n",
    "min         54.263133\n",
    "25%         63.505620\n",
    "50%         66.318070\n",
    "75%         69.174262\n",
    "max         78.998742"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.0"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}