Outlier Removal using IQR and Cliping of Values.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 34,
   "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": 98,
   "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>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.098</td>\n",
       "      <td>25.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.9968</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.68</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.092</td>\n",
       "      <td>15.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.9970</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.65</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.2</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.075</td>\n",
       "      <td>17.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.9980</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.58</td>\n",
       "      <td>9.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0            7.4              0.70         0.00             1.9      0.076   \n",
       "1            7.8              0.88         0.00             2.6      0.098   \n",
       "2            7.8              0.76         0.04             2.3      0.092   \n",
       "3           11.2              0.28         0.56             1.9      0.075   \n",
       "4            7.4              0.70         0.00             1.9      0.076   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
       "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
       "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
       "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "\n",
       "   alcohol  quality  \n",
       "0      9.4        5  \n",
       "1      9.8        5  \n",
       "2      9.8        5  \n",
       "3      9.8        6  \n",
       "4      9.4        5  "
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"../Data/winequality-red.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1599, 12)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,7))\n",
    "df.boxplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def removeOutliers(data):\n",
    "    Q1=data.quantile(0.25)\n",
    "    Q3=data.quantile(0.75)\n",
    "    IQR=Q3-Q1\n",
    "    upper=np.where(data>=(Q3+1.5*IQR))\n",
    "    lower=np.where(data <= (Q1-1.5*IQR))\n",
    "    data.drop(upper[0],axis=\"index\",inplace=True)\n",
    "    #data.drop(lower[0],axis=\"index\",inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=df\n",
    "removeOutliers(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1196, 12)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.boxplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "d=pd.DataFrame({\"data\":[1,4,5,6,3,50,600]})\n",
    "d.boxplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "d.clip(2,4).boxplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZcFhGkhpkuEtSgwx3aXq7lroAabYcc5ekBtlzl6QGGe6S1CDDXYsiyUuT/NIM2g0m+bkZtjswD3XdkOTabvr7knw+yeeSfE+v++72+UiSdd30/57jPn4xyb+bZPm8/DdQmwx3LZaXAtOGOzAITBvuC+Ry4Laqem1V/c1MNkgy46+qrKofmUtRVfU7VfWhuWyrM5fhrsVyI/A9Xc/4poy7KcmBJH+V5GcntPvxrt27ut7pnyV5oPs5ZUAmOS/Jvd32B5L8eLf86IQ2b0nywZO2exPwK8C/T3LPyb3iJNcmuaGbHknyX5J8EnjnSfv5Z0nu6nr/vwtkwrqj3euk7z3JbyX5z930T3Xvo++kvy7+RZIvJPlL4OoJ+17R7fMzSf5Pkv8w7f8RNc0vyNZi2QZsqKrXACT5N8BrgFczfnv/Z5Lc27W7tqre3LV7EfCGqnoqyXpgD3Cqu0V/DrizqnYkWQG8aCbFVdX/TPI7wNGq2plkcJpNXlpVPzHJ8uuBP6+qX0tyCbB1kjb/mqnf+2eS/BnwW8CbqurESV/q/QfAO6rqk0lumrB8C/DNqnpdktXAXyS5q6q+PN17V5sMdy2VHwP2VNVxYLTrBb8OOHJSu1XAzUleAxwHXjXNfj8DfCDJKuB/VNXn57Xqf/SRKZa/nvHwpqr2JXlikjaTvveq2pvkKuBe4F0nDw0lOYfxXyqf7Bb9IXBxN/1G4J8neUs3fw6wHjDcz1CGu5ZKpm8CwLuAUcZ7uX3AU6dqXFX3Jnk9cAnwh0lu6sarJ97QsWYGx32GfzpsefI23z5VGdPs+1Tv/fuBrwHfOcV2U+07jPfo75zm2DpDOOauxfIt4CUT5u8FfrYbK34Z4z3eT0/S7hzgUFWdAH4BWHGqgyQ5HzhcVb8H3AL8QLdqNMkFSfqAn5lBvaPAud0Y+mrgzTPY5tn3dWVXy8XA2inaPO+9d7VfA7wWuDjJv5y4UVV9A/hmkh/rFl05YfWdwH/s/mIhyauSvHiGNatB9ty1KKrqa0n+ovuQ8nbgOuCHgS8w3hu9rqr+LsnXgGeSfAH4IPDbwB8nuQK4h1P3mAGGgf+UZAw4Cjx7CeE24BPA3wIHgLOmqXcsya8B9zE+tPHXM3yrvwrsSfIA8EngsUnafIyT3jvjv0zuZvzzhq8k2QJ8MMnrTtr27YwPO/0D44H+rN9n/EqjBzI+SP9Vxq/+0RnKxw9IUoMclpGkBhnuktQgw12SGmS4S1KDDHdJapDhLkkNMtwlqUH/H+rtOkdxuGGjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[[\"total sulfur dioxide\"]].boxplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lower=df[[\"total sulfur dioxide\"]].quantile(0.05)[0]\n",
    "upper=df[[\"total sulfur dioxide\"]].quantile(0.90)[0]\n",
    "\n",
    "df[[\"total sulfur dioxide\"]].clip(lower,upper).boxplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clipValues(df,lower,upper):\n",
    "    for col in df.columns:\n",
    "        l=df[col].quantile(lower) \n",
    "        u=df[col].quantile(upper)\n",
    "        df[col]=df[col].clip(l,u)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1599, 12)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fixed acidity\n",
      "volatile acidity\n",
      "citric acid\n",
      "residual sugar\n",
      "chlorides\n",
      "free sulfur dioxide\n",
      "total sulfur dioxide\n",
      "density\n",
      "pH\n",
      "sulphates\n",
      "alcohol\n",
      "quality\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data1=df\n",
    "data1=clipValues(data1,0.05,0.9)\n",
    "data1.boxplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1599, 12)"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.shape"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}