08. Outliers.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "82109c8d",
   "metadata": {},
   "source": [
    "# Outliers\n",
    "An outlier is a data point lies far away from the distribution of data. An outlier may occur due error in measurements. Outliers can cause serious problems in stastical analysis hence need to be removed or should be taken care of."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "09ebfd1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing libraries\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a931902b",
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#loading the data\n",
    "df=pd.read_csv('titanic.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2c74c013",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "177"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#getting the missing values\n",
    "df['Age'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f25cf358",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "#distribution of 'Age'\n",
    "sns.distplot(df['Age']);\n",
    "#combines hist with KDE plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5286347c",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "sns.distplot(df['Age'].fillna(100));\n",
    "#fills missing values with 100 (for this plot only doesn't replace these in original dataframe)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1135d27",
   "metadata": {},
   "source": [
    "### Gaussian Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d0bb7ad5",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "#distribution of 'Age' using hisogram\n",
    "figure=df.Age.hist(bins=50)\n",
    "figure.set_title('Histogram')\n",
    "figure.set_xlabel('Age')\n",
    "figure.set_ylabel('No of passenger');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d167dd6b",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "#boxplot of 'Age' showing the distribution\n",
    "figure=df.boxplot(column=\"Age\");\n",
    "#it represents the data in terms of quartiles\n",
    "#the black dots outside the "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "540febd6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    714.000000\n",
       "mean      29.699118\n",
       "std       14.526497\n",
       "min        0.420000\n",
       "25%       20.125000\n",
       "50%       28.000000\n",
       "75%       38.000000\n",
       "max       80.000000\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#getting the stastical information\n",
    "df['Age'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "182d3385",
   "metadata": {},
   "source": [
    "**If the data is normally distributed we use this:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "398efa23",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "73.27860964406095\n",
      "-13.88037434994331\n"
     ]
    }
   ],
   "source": [
    "#assuming 'Age' follows a gaussian distribution we will calculate the boundaries\n",
    "#Gaussian distribution is also known as normal distribution\n",
    "upper_boundary=df['Age'].mean()+3*df['Age'].std()\n",
    "lower_boundary=df['Age'].mean()-3*df['Age'].std()\n",
    "print(upper_boundary)\n",
    "print(lower_boundary)\n",
    "#it uses standard deviation to find the upper and lower boundaries\n",
    "#and whichever data points lie outside these boundaries are termed as outliers."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a644cec",
   "metadata": {},
   "source": [
    "If features are skewed to the left or to the right just like given below, we use following technique :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4da3fce4",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "#when the data doesn't show gaussian distribution\n",
    "#heret the datapoints seemed to be skewed towards right\n",
    "figure=df.Fare.hist(bins=50)\n",
    "figure.set_title('Fare')\n",
    "figure.set_xlabel('Fare')\n",
    "figure.set_ylabel('No of passenger');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "115e6812",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "#boxplot of 'Fare' showing the distribution\n",
    "figure=df.boxplot(column=\"Fare\");\n",
    "#you can see lot of outliers outiside the whiskers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c8671b00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    891.000000\n",
       "mean      32.204208\n",
       "std       49.693429\n",
       "min        0.000000\n",
       "25%        7.910400\n",
       "50%       14.454200\n",
       "75%       31.000000\n",
       "max      512.329200\n",
       "Name: Fare, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#getting the stastical information\n",
    "df['Fare'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "391551d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23.0896"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#lets compute the interquantile range (IQR) to calculate the boundaries\n",
    "IQR=df.Fare.quantile(0.75)-df.Fare.quantile(0.25)\n",
    "IQR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "66984266",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-26.724\n",
      "-3.6343999999999994\n"
     ]
    }
   ],
   "source": [
    "#finding lower and upper boundaries using interquantile range (IQR)\n",
    "lower_bridge=df['Fare'].quantile(0.25)-(IQR*1.5)\n",
    "upper_bridge=df['Fare'].quantile(0.75)-(IQR*1.5)\n",
    "print(lower_bridge)\n",
    "print(upper_bridge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a89fabbc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-61.358399999999996\n",
      "100.2688\n"
     ]
    }
   ],
   "source": [
    "#Extreme Outliers using the IQR\n",
    "lower_bridge=df['Fare'].quantile(0.25)-(IQR*3)\n",
    "upper_bridge=df['Fare'].quantile(0.75)+(IQR*3)\n",
    "print(lower_bridge)\n",
    "print(upper_bridge)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "780994d2",
   "metadata": {},
   "source": [
    "### Dealing with missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5fc3ed8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#copying previous dataframe into new one\n",
    "#it will avoid changes in the original dataframe\n",
    "data=df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "50855b29",
   "metadata": {},
   "outputs": [],
   "source": [
    "#locating all the values greater than 73 and replacing them with 73\n",
    "data.loc[data['Age']>73,'Age']=73"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "be9829d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#locating all the values greater than 100 and replacing them with 100\n",
    "data.loc[data['Fare']>100,'Fare']=100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d577cba2",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "#distribution of 'Age' after dealing with outliers\n",
    "figure=data.Age.hist(bins=50)\n",
    "figure.set_title('Age')\n",
    "figure.set_xlabel('Age')\n",
    "figure.set_ylabel('No of Passanger');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5144c4ab",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "#boxplot of 'Age'\n",
    "figure=data.boxplot(column=\"Age\");\n",
    "#lesser points outside the whiskers compared to the previous boxplot of 'Age'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d66428e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'No of Passanger')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAWgUlEQVR4nO3dfZBldX3n8feHh6DOIM9O4YAOppAokhDoVaMpq0eNTCQKSSmFBRZEdmdTaxIT2bjDalYtll02WU221mCJorA+MEsQA0J8YFlGalNLdAYtAYFIZII8yDPIoEHA7/5xT5+9jt09t7vvQ/e971fVrdvnd8655/ubmb6fOU+/k6pCkiSA3UZdgCRp+TAUJEktQ0GS1DIUJEktQ0GS1DIUJEktQ0GS1DIUpB4l2Z7kx0l2dL2eP+q6pH7aY9QFSCvMm6rqfy10pSQBUlU/HUBNUt+4pyAtUpL9klyZ5IEkjzQ/H9I1f0uSc5L8HfAj4EVJfinJ1UkeTnJbkpNG1wPp5xkK0uLtBnwKeCHwAuDHwEd2WubtwEZgb+AB4Grgc8DzgLcB5yU5clgFS7vi4SNpYf4mydPNz1uq6sSZGUnOAa7dafkLq+rmZv4GYHtVfaqZd0OSzwNvAW4ebNlSbwwFaWFOnDmnkOQ5ST4GbAD2a+bvnWT3qnqmmf5+17ovBF6R5NGutj2ATw+4ZqlnhoK0eGcCRwCvqKofJDka+CaQrmW6hyH+PvC1qvqN4ZUoLYznFKTF25vOeYRHk+wPvH8Xy18JvDjJ25Ps2bz+RZKXDLxSqUeGgrR4fwk8G3gQuB748nwLV9XjwBuAk4F7gB8A/wXYa6BVSgsQH7IjSZrhnoIkqWUoSJJahoIkqWUoSJJaK/o+hQMPPLDWrVu36PWfeOIJVq1a1b+ClrlJ6y/Y50lhnxdm27ZtD1bVQbPNW9GhsG7dOrZu3bro9bds2cL09HT/ClrmJq2/YJ8nhX1emCT/NNc8Dx9JklqGgiSpZShIklqGgiSpZShIklqGgiSpZShIklqGgiSpZShIklor+o7m5WLdpqtmbd9+7vFDrkSSlsY9BUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUGFgpJDk1ybZJbktyc5F1N+/5Jrk7y3eZ9v651zkpye5Lbkhw3qNokSbMb5J7C08CZVfUS4JXAO5O8FNgEXFNVhwPXNNM0804GjgQ2AOcl2X2A9UmSdjKwUKiqe6vqhubnx4FbgLXACcBFzWIXASc2P58AbK6qJ6vqDuB24OWDqk+S9PNSVYPfSLIOuA54GXBnVe3bNe+RqtovyUeA66vqM037BcCXqurSnT5rI7ARYM2aNcdu3rx50XXt2LGD1atXL3r9GTfe/dis7Uet3WfJn91P/ervSmKfJ4N9Xpj169dvq6qp2ebtsaSqepBkNfB54I+q6odJ5lx0lrafS6yqOh84H2Bqaqqmp6cXXduWLVtYyvozTt901azt209Z+mf3U7/6u5LY58lgn/tnoFcfJdmTTiB8tqoua5rvS3JwM/9g4P6m/S7g0K7VDwHuGWR9kqSfNcirjwJcANxSVR/umnUFcFrz82nA5V3tJyfZK8lhwOHA1wdVnyTp5w3y8NGrgbcDNyb5VtP274FzgUuSnAHcCbwVoKpuTnIJ8B06Vy69s6qeGWB9kqSdDCwUqur/MPt5AoDXzbHOOcA5g6pJkjQ/72iWJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLXmDYUkuyf582EVI0karXlDoaqeAY5NkiHVI0kaoT16WOabwOVJ/hp4Yqaxqi4bWFWSpJHoJRT2Bx4CXtvVVoChIEljZpehUFW/O4xCJEmjt8urj5K8OMk1SW5qpn85yfsGX5okadh6uST148BZwFMAVfVt4ORdrZTkk0nunwmTpu0DSe5O8q3m9caueWcluT3JbUmOW3hXJElL1UsoPKeqvr5T29M9rHchsGGW9r+oqqOb198CJHkpnaA5slnnvCS797ANSVIf9RIKDyb5RTonl0nyFuDeXa1UVdcBD/dYxwnA5qp6sqruAG4HXt7jupKkPklVzb9A8iLgfOBVwCPAHcCpVbV9lx+erAOurKqXNdMfAE4HfghsBc6sqkeSfAS4vqo+0yx3AfClqrp0ls/cCGwEWLNmzbGbN2/upZ+z2rFjB6tXr170+jNuvPuxWduPWrvPkj+7n/rV35XEPk8G+7ww69ev31ZVU7PN6+Xqo+8Br0+yCtitqh5fVBUdHwXOprPXcTbwIeAdwGw3x82aVlV1Pp2QYmpqqqanpxddzJYtW1jK+jNO33TVrO3bT1n6Z/dTv/q7ktjnyWCf+2eXoZDk3TtNAzwGbKuqby1kY1V1X9fnfBy4spm8Czi0a9FDgHsW8tmSpKXr5ZzCFPB7wNrmtRGYBj6e5D0L2ViSg7smfxuYuTLpCuDkJHslOQw4HNj55LYkacB6uaP5AOCYqtoBkOT9wKXAa4BtwJ/NtlKSi+mEx4FJ7gLeD0wnOZrOoaHtwL8GqKqbk1wCfIfOlU3vbMZdkiQNUS+h8ALgJ13TTwEvrKofJ3lyrpWq6m2zNF8wz/LnAOf0UI8kaUB6CYXPAdcnubyZfhNwcXPi+TsDq0ySNHS9XH10dpIvAa+mc5XQ71XV1mb2KYMsTpI0XL3sKUBn+Ox7ZpZP8oKqunNgVUmSRqKXS1L/gM5J4vuAZ+jsLRTwy4MtTZI0bL3sKbwLOKKqHhp0MZKk0erlPoXv07lZTZI05nrZU/gesCXJVUB7CWpVfXhgVUmSRqKXULizef1C85IkjaleLkn94DAKkSSNXi9XHx0EvIfOA3CeNdNeVa8dYF2SpBHo5UTzZ4FbgcOAD9IZs+gbA6xJkjQivYTCAVV1AfBUVX2tqt4BvHLAdUmSRqCXE81PNe/3Jjmezp3NhwyuJEnSqPQSCv8xyT7AmcB/B54L/PFAq5IkjUQvVx/NPB3tMWD9YMuRJI3SLs8pJPmzJM9NsmeSa5I8mOTUYRQnSRquXk40v6Gqfgj8Fp1nKb8Y+JOBViVJGoleQmHP5v2NwMVV9fAA65EkjVAvJ5q/mORW4MfAv2luZvvnwZYlSRqFXe4pVNUm4NeAqap6CngCOGHQhUmShq+XE81vBZ6uqmeSvA/4DPD8gVcmSRq6Xs4p/GlVPZ7k14HjgIuAjw62LEnSKPQSCs8078cDH62qy3EIbUkaS72Ewt1JPgacBPxtkr16XE+StML08uV+EvAVYENVPQrsj/cpSNJY6uXqox9V1WXAY0leQOe+hVsHXpkkaeh6ufrozUm+C9wBfK15/9KgC5MkDV8vh4/OpvP8hH+oqsOA1wN/N9CqJEkj0UsoPFVVDwG7Jdmtqq4Fjh5sWZKkUehlmItHk6wGrgM+m+R+4OnBliVJGoVe9hTeDPyIzoN1vgz8I/CmQRYlSRqNOfcUkrwCOB/4ReBG4IyqumhYhUmShm++PYW/Av4tcADwYeAvhlKRJGlk5guF3arq6qp6sqr+GjhoWEVJkkZjvhPN+yb5nbmmmxvaJEljZL5Q+Bo/e0K5e7oAQ0GSxsycoVBVvzvMQiRJozew0U6TfDLJ/Ulu6mrbP8nVSb7bvO/XNe+sJLcnuS3JcYOqS5I0t0EOgX0hsGGntk3ANVV1OHBNM02SlwInA0c265yXZPcB1iZJmsWcodA8hpMkhy3mg6vqOuDhnZpPoPPkNpr3E7vaNzdXOt0B3A68fDHblSQtXqpq9hnJDVV1zMz7oj48WQdcWVUva6Yfrap9u+Y/UlX7JfkIcH1VfaZpvwD4UlVdOstnbgQ2AqxZs+bYzZs3L6Y0AHbs2MHq1asXvf6MG+9+bNb2o9bus+TP7qd+9Xclsc+TwT4vzPr167dV1dRs8+a7+uihJNcChyW5YueZVfXmRVUzu8zSNmtaVdX5dO60Zmpqqqanpxe90S1btrCU9WecvumqWdu3n7L0z+6nfvV3JbHPk8E+9898oXA8cAzwaeBDfdrefUkOrqp7kxwM3N+03wUc2rXcIcA9fdqmJKlH812S+hPg+iSvqqoHkuzdaa4dS9jeFcBpwLnN++Vd7Z9L8mHg+cDhwNeXsB1J0iL0MnT2miRfpfNs5iR5ADitqm6ab6UkFwPTwIFJ7gLeTycMLklyBnAn8FaAqro5ySXAd+gMy/3OqnpmkX2SJC1SL6FwPvDu5uE6JJlu2l4130pV9bY5Zr1ujuXPAc7poZ4VY91c5xrOPX7IlUhSb3q5T2HVTCAAVNUWYNXAKpIkjUwvewrfS/KndE44A5wK3DG4kiRJo9LLnsI76AybfVnzOhBwXCRJGkO73FOoqkeAPxxCLZKkERvk2EeSpBXGUJAktQwFSVJrl6GQ5JAkX0jyQJL7knw+ySHDKE6SNFy97Cl8is4wFAcDa4EvNm2SpDHTSygcVFWfqqqnm9eFdC5RlSSNmV5C4cEkpybZvXmdCjw06MIkScPX681rJwE/AO4F3tK0SZLGTC83r90J9POBOpKkZWrOUEjyH+ZZr6rq7AHUI0kaofn2FJ6YpW0VcAZwAGAoSNKYme/Ja+0jOJunrr2LzkB4m+nf4zklScvIvOcUkuwPvBs4BbgIOKYZIE+SNIbmO6fw58Dv0HnK2lFLfDazJGkFmG9P4UzgSeB9wHuTzLSHzonm5w64Ni1jPmpUGk/znVNwsDxJmjB+8UuSWoaCJKllKEiSWoaCJKllKEiSWoaCJKllKEiSWoaCJKllKEiSWrt8yI6Gx6EjJI2aewqSpJahIElqGQqSpJahIElqGQqSpJahIElqGQqSpNZI7lNIsh14HHgGeLqqppLsD/xPYB2wHTipqh4ZRX2SNKlGefPa+qp6sGt6E3BNVZ2bZFMz/e9GU5okLQ9z3dR64YZVA9necjp8dAJwUfPzRcCJoytFkibTqEKhgK8m2ZZkY9O2pqruBWjenzei2iRpYqWqhr/R5PlVdU+S5wFXA38AXFFV+3Yt80hV7TfLuhuBjQBr1qw5dvPmzYuuY8eOHaxevXrR68+48e7HFrT8UWv3WdDnzLX8QvWrvzD4Wvuln31eKezzeJnrd+2wfXZfdJ/Xr1+/raqmZps3klD4mQKSDwA7gH8FTFfVvUkOBrZU1RHzrTs1NVVbt25d9La3bNnC9PT0otefMdcxv7nMNcBdvz5nLv3qL6ycwfv62eeVwj6Pl/nOKSy2z0nmDIWhHz5KsirJ3jM/A28AbgKuAE5rFjsNuHzYtUnSpBvF1UdrgC8kmdn+56rqy0m+AVyS5AzgTuCtI6hNkiba0EOhqr4H/Mos7Q8Brxt2PZKk/285XZIqSRoxQ0GS1DIUJEktQ0GS1DIUJEktQ0GS1DIUJEmtUQ6dPbEWOpyFJA2LewqSpJahIElqGQqSpJahIElqGQqSpJahIElqeUmqhmKlPKlNmnTuKUiSWoaCJKllKEiSWoaCJKllKEiSWoaCJKllKEiSWt6nMEFuvPsxTvd+AUnzcE9BktRyT0Hz8oFA0mRxT0GS1HJPYQzN9b/7M48aciGSVpyJDoW5TryulJOuHtpZmRwcUMvZRIeCxsdcX7QXblg15Eqklc1QkJY59yw0TIaCgP4divKQlrSyGQpalvzfsWbjYcLBMxQkLdigQ9s9ztHxPgVJUss9BalHHtJaefw7WzhDQSPlYQKtZPP9+12pwWMoSAOy0MAb54D0f+wrh6GgFWWhX5wr/a51adiWXSgk2QD8N2B34BNVde6IS5Lmtdz+hz/KIBzVn8V8zwpZCPdollkoJNkd+CvgN4C7gG8kuaKqvjPayjqW2y+/1A/+u961xfwZrdSAWVahALwcuL2qvgeQZDNwArAsQkHjY6X+wnZb6Gi4y/HLfznWNOlSVaOuoZXkLcCGqvqXzfTbgVdU1e93LbMR2NhMHgHctoRNHgg8uIT1V5pJ6y/Y50lhnxfmhVV10GwzltueQmZp+5nUqqrzgfP7srFka1VN9eOzVoJJ6y/Y50lhn/tnud3RfBdwaNf0IcA9I6pFkibOcguFbwCHJzksyS8AJwNXjLgmSZoYy+rwUVU9neT3ga/QuST1k1V18wA32ZfDUCvIpPUX7POksM99sqxONEuSRmu5HT6SJI2QoSBJak1kKCTZkOS2JLcn2TTqegYhyaFJrk1yS5Kbk7yrad8/ydVJvtu87zfqWvspye5JvpnkymZ6rPsLkGTfJJcmubX5+/61ce53kj9u/k3flOTiJM8at/4m+WSS+5Pc1NU2Zx+TnNV8n92W5LilbHviQqFrKI3fBF4KvC3JS0db1UA8DZxZVS8BXgm8s+nnJuCaqjocuKaZHifvAm7pmh73/kJnrLAvV9UvAb9Cp/9j2e8ka4E/BKaq6mV0Lkg5mfHr74XAhp3aZu1j83t9MnBks855zffcokxcKNA1lEZV/QSYGUpjrFTVvVV1Q/Pz43S+KNbS6etFzWIXASeOpMABSHIIcDzwia7mse0vQJLnAq8BLgCoqp9U1aOMd7/3AJ6dZA/gOXTuZRqr/lbVdcDDOzXP1ccTgM1V9WRV3QHcTud7blEmMRTWAt/vmr6raRtbSdYBvwr8PbCmqu6FTnAAzxthaf32l8B7gJ92tY1zfwFeBDwAfKo5bPaJJKsY035X1d3AfwXuBO4FHquqrzKm/d3JXH3s63faJIbCLofSGCdJVgOfB/6oqn446noGJclvAfdX1bZR1zJkewDHAB+tql8FnmDlHzqZU3Mc/QTgMOD5wKokp462qpHr63faJIbCxAylkWRPOoHw2aq6rGm+L8nBzfyDgftHVV+fvRp4c5LtdA4JvjbJZxjf/s64C7irqv6+mb6UTkiMa79fD9xRVQ9U1VPAZcCrGN/+dpurj339TpvEUJiIoTSShM5x5luq6sNds64ATmt+Pg24fNi1DUJVnVVVh1TVOjp/p/+7qk5lTPs7o6p+AHw/yRFN0+voDDU/rv2+E3hlkuc0/8ZfR+d82bj2t9tcfbwCODnJXkkOAw4Hvr7orVTVxL2ANwL/APwj8N5R1zOgPv46nV3IbwPfal5vBA6gc+XCd5v3/Udd6wD6Pg1c2fw8Cf09Gtja/F3/DbDfOPcb+CBwK3AT8Glgr3HrL3AxnXMmT9HZEzhjvj4C722+z24DfnMp23aYC0lSaxIPH0mS5mAoSJJahoIkqWUoSJJahoIkqbWsnrwmrQRJngFu7Go6saq2j6gcqa+8JFVaoCQ7qmr1AtcJnd+3n+5yYWmEPHwkLVGS1UmuSXJDkhuTnNC0r2ueb3AecANwaJI/SfKNJN9O8sHRVi79PENBWrhnJ/lW8/oC8M/Ab1fVMcB64EPNngHAEcD/qM5gdUfQGYLg5XTuQj42yWuGX740N88pSAv346o6emaiGXjwPzVf8D+lM2zxmmb2P1XV9c3Pb2he32ymV9MJieuGUbTUC0NBWrpTgIOAY6vqqWak1mc1857oWi7Af66qjw25PqlnHj6Slm4fOs9yeCrJeuCFcyz3FeAdzTMuSLI2yTg+DEYrmHsK0tJ9Fvhikq10RqO9dbaFquqrSV4C/N/mlMMO4FTGc+x/rVBekipJann4SJLUMhQkSS1DQZLUMhQkSS1DQZLUMhQkSS1DQZLU+n8cS9Om6HtWwwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#distribution of 'Fare' after dealing with outliers\n",
    "figure=data.Fare.hist(bins=50)\n",
    "figure.set_title('Fare')\n",
    "figure.set_xlabel('Fare')\n",
    "figure.set_ylabel('No of Passanger')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "be71629c",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "#boxplot of 'Fare'\n",
    "figure=data.boxplot(column=\"Fare\");\n",
    "#lesser points outside the whiskers compared to previous boxplot of 'Fare'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69472278",
   "metadata": {},
   "source": [
    "The End"
   ]
  }
 ],
 "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}