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AllieUbisse/outlier-detection-percentile.ipynb

Created June 6, 2020 01:08
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{
"nbformat": 4,
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"metadata": {
"colab": {
"name": "Outlier-Detection-percentile.ipynb",
"provenance": [],
"authorship_tag": "ABX9TyNenuNRs4HbMshlwuh22YA8",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
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"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
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"source": [
"<a href=\"https://colab.research.google.com/gist/AllieUbisse/254b737213a16935918e8e59e44fabab/outlier-detection-percentile.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Y41mCGlZAk-s",
"colab_type": "text"
},
"source": [
"# **1. Library Imports**"
]
},
{
"cell_type": "code",
"metadata": {
"id": "ODlzYMLK_6kL",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 72
},
"outputId": "bc09812b-8980-4289-a46f-085a662121c3"
},
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"import seaborn as sns\n"
],
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
" import pandas.util.testing as tm\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "lqLrIC7RBVVc",
"colab_type": "text"
},
"source": [
"# **2. Import Dataset**\n",
"---\n",
"\n",
"source: [New York City Airbnb Open Data](https://www.kaggle.com/dgomonov/new-york-city-airbnb-open-data)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "npW-YRWCA9AW",
"colab_type": "code",
"colab": {}
},
"source": [
"bnb = pd.read_csv('/content/AB_NYC_2019.csv')"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "NQmEUGBABmwb",
"colab_type": "text"
},
"source": [
"# **3. Outlier Detection using percentile**\n",
"---\n",
"Suppose price is defined as price per night"
]
},
{
"cell_type": "code",
"metadata": {
"id": "ck-e-iC6BxsT",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 401
},
"outputId": "7be52dfb-d49f-40ba-9101-996d84a70834"
},
"source": [
"bnb.head()"
],
"execution_count": 4,
"outputs": [
{
"output_type": "execute_result",
"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>id</th>\n",
" <th>name</th>\n",
" <th>host_id</th>\n",
" <th>host_name</th>\n",
" <th>neighbourhood_group</th>\n",
" <th>neighbourhood</th>\n",
" <th>latitude</th>\n",
" <th>longitude</th>\n",
" <th>room_type</th>\n",
" <th>price</th>\n",
" <th>minimum_nights</th>\n",
" <th>number_of_reviews</th>\n",
" <th>last_review</th>\n",
" <th>reviews_per_month</th>\n",
" <th>calculated_host_listings_count</th>\n",
" <th>availability_365</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2539</td>\n",
" <td>Clean &amp; quiet apt home by the park</td>\n",
" <td>2787</td>\n",
" <td>John</td>\n",
" <td>Brooklyn</td>\n",
" <td>Kensington</td>\n",
" <td>40.64749</td>\n",
" <td>-73.97237</td>\n",
" <td>Private room</td>\n",
" <td>149</td>\n",
" <td>1</td>\n",
" <td>9</td>\n",
" <td>2018-10-19</td>\n",
" <td>0.21</td>\n",
" <td>6</td>\n",
" <td>365</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2595</td>\n",
" <td>Skylit Midtown Castle</td>\n",
" <td>2845</td>\n",
" <td>Jennifer</td>\n",
" <td>Manhattan</td>\n",
" <td>Midtown</td>\n",
" <td>40.75362</td>\n",
" <td>-73.98377</td>\n",
" <td>Entire home/apt</td>\n",
" <td>225</td>\n",
" <td>1</td>\n",
" <td>45</td>\n",
" <td>2019-05-21</td>\n",
" <td>0.38</td>\n",
" <td>2</td>\n",
" <td>355</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3647</td>\n",
" <td>THE VILLAGE OF HARLEM....NEW YORK !</td>\n",
" <td>4632</td>\n",
" <td>Elisabeth</td>\n",
" <td>Manhattan</td>\n",
" <td>Harlem</td>\n",
" <td>40.80902</td>\n",
" <td>-73.94190</td>\n",
" <td>Private room</td>\n",
" <td>150</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>365</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3831</td>\n",
" <td>Cozy Entire Floor of Brownstone</td>\n",
" <td>4869</td>\n",
" <td>LisaRoxanne</td>\n",
" <td>Brooklyn</td>\n",
" <td>Clinton Hill</td>\n",
" <td>40.68514</td>\n",
" <td>-73.95976</td>\n",
" <td>Entire home/apt</td>\n",
" <td>89</td>\n",
" <td>1</td>\n",
" <td>270</td>\n",
" <td>2019-07-05</td>\n",
" <td>4.64</td>\n",
" <td>1</td>\n",
" <td>194</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5022</td>\n",
" <td>Entire Apt: Spacious Studio/Loft by central park</td>\n",
" <td>7192</td>\n",
" <td>Laura</td>\n",
" <td>Manhattan</td>\n",
" <td>East Harlem</td>\n",
" <td>40.79851</td>\n",
" <td>-73.94399</td>\n",
" <td>Entire home/apt</td>\n",
" <td>80</td>\n",
" <td>10</td>\n",
" <td>9</td>\n",
" <td>2018-11-19</td>\n",
" <td>0.10</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id ... availability_365\n",
"0 2539 ... 365\n",
"1 2595 ... 355\n",
"2 3647 ... 365\n",
"3 3831 ... 194\n",
"4 5022 ... 0\n",
"\n",
"[5 rows x 16 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 4
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "ShT93U79HgPK",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 287
},
"outputId": "d1402857-e666-4975-a9dd-d59817420d2b"
},
"source": [
"bnb[['price', 'minimum_nights','number_of_reviews', 'reviews_per_month']].describe()"
],
"execution_count": 56,
"outputs": [
{
"output_type": "execute_result",
"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>price</th>\n",
" <th>minimum_nights</th>\n",
" <th>number_of_reviews</th>\n",
" <th>reviews_per_month</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>48895.000000</td>\n",
" <td>48895.000000</td>\n",
" <td>48895.000000</td>\n",
" <td>38843.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>152.720687</td>\n",
" <td>7.029962</td>\n",
" <td>23.274466</td>\n",
" <td>1.373221</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>240.154170</td>\n",
" <td>20.510550</td>\n",
" <td>44.550582</td>\n",
" <td>1.680442</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>69.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.190000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>106.000000</td>\n",
" <td>3.000000</td>\n",
" <td>5.000000</td>\n",
" <td>0.720000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>175.000000</td>\n",
" <td>5.000000</td>\n",
" <td>24.000000</td>\n",
" <td>2.020000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>10000.000000</td>\n",
" <td>1250.000000</td>\n",
" <td>629.000000</td>\n",
" <td>58.500000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" price minimum_nights number_of_reviews reviews_per_month\n",
"count 48895.000000 48895.000000 48895.000000 38843.000000\n",
"mean 152.720687 7.029962 23.274466 1.373221\n",
"std 240.154170 20.510550 44.550582 1.680442\n",
"min 0.000000 1.000000 0.000000 0.010000\n",
"25% 69.000000 1.000000 1.000000 0.190000\n",
"50% 106.000000 3.000000 5.000000 0.720000\n",
"75% 175.000000 5.000000 24.000000 2.020000\n",
"max 10000.000000 1250.000000 629.000000 58.500000"
]
},
"metadata": {
"tags": []
},
"execution_count": 56
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "9m2kReN0DNzB",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 296
},
"outputId": "232ed44d-f219-4703-dfbf-081fc9f6c2dd"
},
"source": [
"sns.boxplot(x='price', data=bnb)"
],
"execution_count": 6,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7ff28eb262b0>"
]
},
"metadata": {
"tags": []
},
"execution_count": 6
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "74O8VjkXJf8T",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 296
},
"outputId": "871b0ff0-a8a3-451f-e045-e73d06db1eed"
},
"source": [
"sns.distplot(bnb.price, bins=10)"
],
"execution_count": 97,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7ff28dc05588>"
]
},
"metadata": {
"tags": []
},
"execution_count": 97
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "6YWWbaXfDrYx",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "11bedaf4-d910-4e35-bf99-b7f993a897cb"
},
"source": [
"# set threshold values\n",
"min_price_threshold, max_price_treshold = bnb.price.quantile([0.01, 0.90])\n",
"min_price_threshold, max_price_treshold"
],
"execution_count": 90,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(30.0, 269.0)"
]
},
"metadata": {
"tags": []
},
"execution_count": 90
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Vsbg4MN9ERnS",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "84c31a19-936b-4ca0-b117-72684d42ef2d"
},
"source": [
"# count the price occurance\n",
"bnb[bnb.price<min_price_threshold].price.value_counts().sum()"
],
"execution_count": 91,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"404"
]
},
"metadata": {
"tags": []
},
"execution_count": 91
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "8uMQU4srFSHf",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "53647172-8f60-46f8-c1cb-0fb53a5b966f"
},
"source": [
"# count the price occurance\n",
"bnb[bnb.price>max_price_treshold].price.value_counts().sum()"
],
"execution_count": 92,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"4878"
]
},
"metadata": {
"tags": []
},
"execution_count": 92
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "-PLMXlcBGFgO",
"colab_type": "code",
"colab": {}
},
"source": [
"bnb_no_outliers = bnb[(bnb.price>min_price_threshold) & (bnb.price<max_price_treshold) ]"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "gMKUAoMYGrr-",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "1fd82e55-3b9a-4041-f2f4-2ce558818a5c"
},
"source": [
"bnb.shape, bnb_no_outliers.shape"
],
"execution_count": 94,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"((48895, 16), (43325, 16))"
]
},
"metadata": {
"tags": []
},
"execution_count": 94
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "nPgHbSDUG3kI",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 296
},
"outputId": "d51e32ba-ebeb-496d-d0e6-b3ab0a9c0039"
},
"source": [
"sns.boxplot(x='price', data=bnb_no_outliers)"
],
"execution_count": 95,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7ff28d8a1a90>"
]
},
"metadata": {
"tags": []
},
"execution_count": 95
},
{
"output_type": "display_data",
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Km5wR5QtJrsj",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 296
},
"outputId": "39966afb-6ad3-45a0-9d6e-322acaf66197"
},
"source": [
"sns.distplot(bnb_no_outliers.price, bins=10)"
],
"execution_count": 96,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7ff28d7cedd8>"
]
},
"metadata": {
"tags": []
},
"execution_count": 96
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
}
]
}
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