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Week5.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# The Battle of Neighborhoods\n",
"### _Opening a new shopping mall in Hyderabad city of Telangana state, India_\n",
"#### IBM Data Science Capstone Project, by Vamshi Krishna Gundu, June 2020\n",
"\n",
"***"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Table of Content:\n",
"1. Introduction/Business Understanding\n",
"2. Analytic approach\n",
"3. Data requirements\n",
"4. Data Preparation and pre-processing\n",
"5. Data Analysis\n",
"6. Conclusion"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![Namaste Hyderabad](https://camo.githubusercontent.com/6142076fab68a2e69083e1cd00f7419564a0541d/68747470733a2f2f6d69726f2e6d656469756d2e636f6d2f6d61782f313430302f302a2d307a6663716463324e6c6b414f6d66)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Introduction/Business Understanding\n",
"### 1.1 Introduction"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" A shopping center, is a group of shops built together, sometimes under one roof(wikipedia). Most of the shopping is done at the shopping malls by us because it is a place where you can buy what you want, have some snack or food in between and if you are tired you can watch a movie or have fun playing games or any activities, all at a single place. So, most of us prefer shopping at shopping malls instead of shopping any other store or outlet. It is a place where any startup or someone can showcase their product and let the market know about it. Now a days, flash mobs are being conducted in Malls, social media festivals are also celebrated by conducting flash mobs and shows. Real estate business, Restaurants, Groceries, Jeweleries, without leaving a corner each and every business is brought into these malls. \n",
"\n",
" As a result this is a really huge benifit for the Businesses as well as owners/real-estate, Businesses will get into huge sales and owners of the outlets get the income as rent, as well as real estate developers get the profit as well as fame by constructing large and beautiful shopping malls.\n",
"\n",
" However there are plenty number of malls which are located in the city of Hyderabad. So, selecting a suitable location for business as well as public is difficult. The location of the mall ultimately decides whether it will get into profits or loss."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.2 Business Problem\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" The objective of the project \" The Battle of Neighborhoods - Opening a Shopping Mall in Hyderabad , Telangana, India\" is to explore and analyze the neighborhoods and the shopping malls in hyderabad , Telangana and select the best locations in the city to open a new Shopping Mall. Using geocoder library to get the latitude and longitudes for neighborhoods. Foursquare api to explore neighborhoods, Data Science methodology and K means clustering this project will be a solution to the business problem : \n",
" In the city of Hyderabad , Telangana state of India, if someone is looking to open a Shopping Mall, where would you recommend that they open it so that Business man - investors, property developers and residents in the neighborhoods will get benifitted?\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.3 Target audience of this Project"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" Indian mall developers are looking to add over 65million square feets of new mall supply by 2022 end. Hyderabad one among the top cities comprise 11% share out of 7 cities. Hyderabad malls have average vacancy of 15%, lease rates of 100/- tp 160/- rupees per square feet, At present, Hyderabad has over four million sft of the area, says an article by Times of India. Hyderabad expected to get 6 million sq ft of shopping mall space in 3 years, says Economic Times report. With the increasing supply of shopping malls selecting a location which will meet the customer demand is difficult. So, this project helps in finding a good location to open a new shopping mall to Business stake holders, property developers, investors as well as customers ."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"___\n",
"## 2. Analytic Approach"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" First we are going to analyze the data using exploratory data analysis to uncover the hidden patterns in data and to provide useful insights to both the constructor, Business man and residents live in neighborhoods.\n",
" And secondly we will use prescriptive analysis to help decide a location to construct new shopping mall using K-means clustering."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Data Requirements"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3.1 Below is the required data:\n",
" We are going to analyze Hyderabad city shopping malls in this city.\n",
"- List of neighborhoods in the city of Hyderabad. This will give us the scope of the areas to open shopping malls near to the residential areas and meet the demand.\n",
"- Latitude and Longitudes of the extracted neighborhood data. This will act as the input parameters to the foursquare api to explore a neighborhood.\n",
"- Shopping malls data, this will be the output from the foursquare api and help us to create clusters on the neighborhoods depending on the frequency of malls in a given radius of a particular neighborhood."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3.2 Sources and methods to get the data:\n",
"- The list of neighborhoods in Hyderabad is taken from a wikipedia page (https://en.wikipedia.org/wiki/List_of_neighbourhoods_in_Hyderabad), contains 31 neighborhoods in total, combining all the zones. By using web scraping technique we will get the neighborhood data from the wikipedia page.\n",
"- We will then get the Latitude and Longitude of each neighborhood using geocoder library and attach these coordinates to our neighborhood data\n",
"- Then with the help of Foursquare api calls we will send the coordinates of each neighborhoods and get the venues details of shopping malls and create the shopping malls data of each neighborhood.(https://developer.foursquare.com/docs/api)\n",
"\n",
"- This project will be completed with the help of data science skills, data cleaning, exploring, analyzing, visualizing using folium maps in particular. Also takes help from foursquare api to get the shopping mall details which are near to each neighborhood. And finally using a machine learning technique 'K-means clustering' to cluster malls into different categories."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. Data Preparation and Pre-processing"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 4.1 Importing required libraries"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: geocoder in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (1.38.1)\n",
"Requirement already satisfied: click in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from geocoder) (7.1.2)\n",
"Requirement already satisfied: requests in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from geocoder) (2.23.0)\n",
"Requirement already satisfied: ratelim in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from geocoder) (0.1.6)\n",
"Requirement already satisfied: future in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from geocoder) (0.18.2)\n",
"Requirement already satisfied: six in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from geocoder) (1.15.0)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from requests->geocoder) (2020.4.5.2)\n",
"Requirement already satisfied: chardet<4,>=3.0.2 in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from requests->geocoder) (3.0.4)\n",
"Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from requests->geocoder) (1.25.9)\n",
"Requirement already satisfied: idna<3,>=2.5 in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from requests->geocoder) (2.9)\n",
"Requirement already satisfied: decorator in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from ratelim->geocoder) (4.4.2)\n",
"Requirement already satisfied: geopy in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (2.0.0)\n",
"Requirement already satisfied: geographiclib<2,>=1.49 in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from geopy) (1.50)\n",
"Requirement already satisfied: beautifulsoup4 in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (4.9.1)\n",
"Requirement already satisfied: soupsieve>1.2 in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from beautifulsoup4) (2.0.1)\n",
"Collecting geopandas\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/f8/dd/c0a6429cc7692efd5c99420c9df525c40f472b50705871a770449027e244/geopandas-0.8.0-py2.py3-none-any.whl (962kB)\n",
"\u001b[K |████████████████████████████████| 962kB 9.4MB/s eta 0:00:01\n",
"\u001b[?25hCollecting shapely (from geopandas)\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/20/fa/c96d3461fda99ed8e82ff0b219ac2c8384694b4e640a611a1a8390ecd415/Shapely-1.7.0-cp36-cp36m-manylinux1_x86_64.whl (1.8MB)\n",
"\u001b[K |████████████████████████████████| 1.8MB 30.3MB/s eta 0:00:01\n",
"\u001b[?25hCollecting fiona (from geopandas)\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/ec/20/4e63bc5c6e62df889297b382c3ccd4a7a488b00946aaaf81a118158c6f09/Fiona-1.8.13.post1-cp36-cp36m-manylinux1_x86_64.whl (14.7MB)\n",
"\u001b[K |████████████████████████████████| 14.7MB 6.8MB/s eta 0:00:011 |███▊ | 1.7MB 31.0MB/s eta 0:00:01\n",
"\u001b[?25hCollecting pyproj>=2.2.0 (from geopandas)\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/e5/c3/071e080230ac4b6c64f1a2e2f9161c9737a2bc7b683d2c90b024825000c0/pyproj-2.6.1.post1-cp36-cp36m-manylinux2010_x86_64.whl (10.9MB)\n",
"\u001b[K |████████████████████████████████| 10.9MB 1.2MB/s eta 0:00:01\n",
"\u001b[?25hRequirement already satisfied: pandas>=0.23.0 in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from geopandas) (1.0.4)\n",
"Requirement already satisfied: six>=1.7 in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from fiona->geopandas) (1.15.0)\n",
"Collecting cligj>=0.5 (from fiona->geopandas)\n",
" Downloading https://files.pythonhosted.org/packages/e4/be/30a58b4b0733850280d01f8bd132591b4668ed5c7046761098d665ac2174/cligj-0.5.0-py3-none-any.whl\n",
"Collecting click-plugins>=1.0 (from fiona->geopandas)\n",
" Downloading https://files.pythonhosted.org/packages/e9/da/824b92d9942f4e472702488857914bdd50f73021efea15b4cad9aca8ecef/click_plugins-1.1.1-py2.py3-none-any.whl\n",
"Collecting munch (from fiona->geopandas)\n",
" Downloading https://files.pythonhosted.org/packages/cc/ab/85d8da5c9a45e072301beb37ad7f833cd344e04c817d97e0cc75681d248f/munch-2.5.0-py2.py3-none-any.whl\n",
"Requirement already satisfied: click<8,>=4.0 in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from fiona->geopandas) (7.1.2)\n",
"Requirement already satisfied: attrs>=17 in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from fiona->geopandas) (19.3.0)\n",
"Requirement already satisfied: pytz>=2017.2 in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from pandas>=0.23.0->geopandas) (2020.1)\n",
"Requirement already satisfied: python-dateutil>=2.6.1 in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from pandas>=0.23.0->geopandas) (2.8.1)\n",
"Requirement already satisfied: numpy>=1.13.3 in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from pandas>=0.23.0->geopandas) (1.18.5)\n",
"Installing collected packages: shapely, cligj, click-plugins, munch, fiona, pyproj, geopandas\n",
" Found existing installation: pyproj 1.9.6\n",
" Uninstalling pyproj-1.9.6:\n",
" Successfully uninstalled pyproj-1.9.6\n",
"Successfully installed click-plugins-1.1.1 cligj-0.5.0 fiona-1.8.13.post1 geopandas-0.8.0 munch-2.5.0 pyproj-2.6.1.post1 shapely-1.7.0\n"
]
}
],
"source": [
"!pip install geocoder\n",
"!pip install geopy\n",
"!pip install beautifulsoup4\n",
"!pip install geopandas"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Libraries imported.\n"
]
}
],
"source": [
"import numpy as np # library to handle data in a vectorized manner\n",
"\n",
"import pandas as pd # library for data analsysis\n",
"pd.set_option(\"display.max_columns\", None)\n",
"pd.set_option(\"display.max_rows\", None)\n",
"\n",
"import json # library to handle JSON files\n",
"\n",
"#!conda install -c conda-forge geopy --yes # uncomment this line if you haven't completed the Foursquare API lab\n",
"from geopy.geocoders import Nominatim # convert an address into latitude and longitude values\n",
"#!conda install -c conda-forge geocoder --yes # uncomment this line if you haven't completed the Foursquare API lab\n",
"import geocoder # to get coordinates\n",
"\n",
"import requests # library to handle requests\n",
"\n",
"#!conda install -c anaconda beautifulsoup4 --yes\n",
"from bs4 import BeautifulSoup # library to parse HTML and XML documents\n",
"\n",
"from pandas.io.json import json_normalize # tranform JSON file into a pandas dataframe\n",
"\n",
"# Matplotlib and associated plotting modules\n",
"import matplotlib.cm as cm\n",
"import matplotlib.colors as colors\n",
"\n",
"# import k-means from clustering stage\n",
"from sklearn.cluster import KMeans\n",
"\n",
"import folium # map rendering library\n",
"\n",
"print(\"Libraries imported.\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 4.2 Data preparation :Scraping Neighborhood data from Wikipedia page"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" Lets scrape the Neighborhood data from wikipedia page using beautifulsoup library and get all the list elements and store in a list called hyd_neighborhood_list"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['1 Central\\n\\n1.1 Ameerpet\\n1.2 Sanathnagar\\n1.3 Khairatabad\\n1.4 Musheerabad\\n1.5 Amberpet\\n1.6 Nampally\\n1.7 Secunderabad\\n1.8 Secunderabad Cantt.\\n\\n', '1.1 Ameerpet', '1.2 Sanathnagar', '1.3 Khairatabad', '1.4 Musheerabad', '1.5 Amberpet', '1.6 Nampally', '1.7 Secunderabad', '1.8 Secunderabad Cantt.', '2 Old City', '3 Western\\n\\n3.1 HITEC City\\n3.2 Jubilee Hills\\n3.3 Gachibowli\\n\\n', '3.1 HITEC City', '3.2 Jubilee Hills', '3.3 Gachibowli', '4 North Western\\n\\n4.1 Serilingampally\\n4.2 Kukatpally\\n4.3 Patancheru\\n\\n', '4.1 Serilingampally', '4.2 Kukatpally', '4.3 Patancheru', '5 Northern\\n\\n5.1 Balanagar\\n5.2 Qutbullapur\\n5.3 Kompally\\n5.4 Alwal\\n\\n', '5.1 Balanagar', '5.2 Qutbullapur', '5.3 Kompally', '5.4 Alwal', '6 North Eastern\\n\\n6.1 Malkajgiri\\n6.2 Kapra\\n6.3 Keesara\\n\\n', '6.1 Malkajgiri', '6.2 Kapra', '6.3 Keesara', '7 Eastern\\n\\n7.1 Uppal\\n7.2 Ghatkesar\\n\\n', '7.1 Uppal', '7.2 Ghatkesar', '8 South Eastern\\n\\n8.1 Dilsukhnagar\\n8.2 LB Nagar\\n8.3 Saroornagar\\n8.4 Hayathnagar\\n\\n', '8.1 Dilsukhnagar', '8.2 LB Nagar', '8.3 Saroornagar', '8.4 Hayathnagar', '9 South Western\\n\\n9.1 Mehdipatnam\\n9.2 Rajendranagar\\n9.3 Shamshabad\\n\\n', '9.1 Mehdipatnam', '9.2 Rajendranagar', '9.3 Shamshabad', '10 References', '11 External links']\n"
]
}
],
"source": [
"page = \"https://en.wikipedia.org/wiki/List_of_neighbourhoods_in_Hyderabad\"\n",
"request = requests.get(page).text\n",
"soup = BeautifulSoup(request,'html.parser')\n",
"\n",
"hyd_neighborhood_list = []\n",
"[hyd_neighborhood_list.append(row.text) for row in soup.find_all(\"div\",class_=\"toc\")[0].findAll(\"li\")]\n",
"print(hyd_neighborhood_list)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" Using regular expression we will find all the list element that start with float values and store them in a list called float_elements"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['1.1 Ameerpet', '1.2 Sanathnagar', '1.3 Khairatabad', '1.4 Musheerabad', '1.5 Amberpet', '1.6 Nampally', '1.7 Secunderabad', '1.8 Secunderabad Cantt.', '1.1 Ameerpet', '1.2 Sanathnagar', '1.3 Khairatabad', '1.4 Musheerabad', '1.5 Amberpet', '1.6 Nampally', '1.7 Secunderabad', '1.8 Secunderabad Cantt.', '3.1 HITEC City', '3.2 Jubilee Hills', '3.3 Gachibowli', '3.1 HITEC City', '3.2 Jubilee Hills', '3.3 Gachibowli', '4.1 Serilingampally', '4.2 Kukatpally', '4.3 Patancheru', '4.1 Serilingampally', '4.2 Kukatpally', '4.3 Patancheru', '5.1 Balanagar', '5.2 Qutbullapur', '5.3 Kompally', '5.4 Alwal', '5.1 Balanagar', '5.2 Qutbullapur', '5.3 Kompally', '5.4 Alwal', '6.1 Malkajgiri', '6.2 Kapra', '6.3 Keesara', '6.1 Malkajgiri', '6.2 Kapra', '6.3 Keesara', '7.1 Uppal', '7.2 Ghatkesar', '7.1 Uppal', '7.2 Ghatkesar', '8.1 Dilsukhnagar', '8.2 LB Nagar', '8.3 Saroornagar', '8.4 Hayathnagar', '8.1 Dilsukhnagar', '8.2 LB Nagar', '8.3 Saroornagar', '8.4 Hayathnagar', '9.1 Mehdipatnam', '9.2 Rajendranagar', '9.3 Shamshabad', '9.1 Mehdipatnam', '9.2 Rajendranagar', '9.3 Shamshabad']\n"
]
}
],
"source": [
"import re\n",
"float_elements = []\n",
"[float_elements.extend(re.findall(\"\\d+[.]\\d*.*\", hyd_neighborhood_list[num])) for num in range(len(hyd_neighborhood_list))]\n",
"print(float_elements)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" From list object called float_elements we are going to get the unique neighborhoods and store with the help of set called hyd_neighborhood_set and print the length of the set of neighborhoods we scraped from wikipedia page."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of neighborhoods we have scraped: 30\n",
"{'Sanathnagar', 'Ghatkesar', 'Mehdipatnam', 'Balanagar', 'Malkajgiri', 'Secunderabad Cantt', 'Keesara', 'Serilingampally', 'Secunderabad', 'Patancheru', 'Kompally', 'Rajendranagar', 'Alwal', 'HITEC City', 'Shamshabad', 'Musheerabad', 'Saroornagar', 'Uppal', 'Ameerpet', 'Kapra', 'Jubilee Hills', 'Khairatabad', 'Hayathnagar', 'Dilsukhnagar', 'Nampally', 'Gachibowli', 'Amberpet', 'LB Nagar', 'Qutbullapur', 'Kukatpally'}\n"
]
}
],
"source": [
"hyd_neighborhood_set = set([])\n",
"[hyd_neighborhood_set.add(\" \".join(re.findall(\"[a-zA-Z]+\", float_elements[num]))) for num in range(len(float_elements))]\n",
"\n",
"print(\"Number of neighborhoods we have scraped: \",len(hyd_neighborhood_set))\n",
"print(hyd_neighborhood_set)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" We then finally convert our set of neighborhoods to list and store in a column called Neighborhood of hyd_df dataframe and print head of it and also see the shape of dataframe"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Neighborhood\n",
"0 Sanathnagar\n",
"1 Ghatkesar\n",
"2 Mehdipatnam\n",
"3 Balanagar\n",
"4 Malkajgiri\n",
"(30, 1)\n"
]
}
],
"source": [
"hyd_df = pd.DataFrame({\"Neighborhood\": list(hyd_neighborhood_set)})\n",
"print(hyd_df.head())\n",
"print(hyd_df.shape)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\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>Neighborhood</th>\n",
" </tr>\n",
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" <th>0</th>\n",
" <td>Sanathnagar</td>\n",
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" <tr>\n",
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" <td>Ghatkesar</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Mehdipatnam</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Balanagar</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Malkajgiri</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Secunderabad Cantt</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Keesara</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>Serilingampally</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>Secunderabad</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>Patancheru</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>Kompally</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>Rajendranagar</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>Alwal</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>HITEC City</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>Shamshabad</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>Musheerabad</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>Saroornagar</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>Uppal</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>Ameerpet</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>Kapra</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>Jubilee Hills</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>Khairatabad</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>Hayathnagar</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>Dilsukhnagar</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>Nampally</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>Gachibowli</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>Amberpet</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>LB Nagar</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>Qutbullapur</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>Kukatpally</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Neighborhood\n",
"0 Sanathnagar\n",
"1 Ghatkesar\n",
"2 Mehdipatnam\n",
"3 Balanagar\n",
"4 Malkajgiri\n",
"5 Secunderabad Cantt\n",
"6 Keesara\n",
"7 Serilingampally\n",
"8 Secunderabad\n",
"9 Patancheru\n",
"10 Kompally\n",
"11 Rajendranagar\n",
"12 Alwal\n",
"13 HITEC City\n",
"14 Shamshabad\n",
"15 Musheerabad\n",
"16 Saroornagar\n",
"17 Uppal\n",
"18 Ameerpet\n",
"19 Kapra\n",
"20 Jubilee Hills\n",
"21 Khairatabad\n",
"22 Hayathnagar\n",
"23 Dilsukhnagar\n",
"24 Nampally\n",
"25 Gachibowli\n",
"26 Amberpet\n",
"27 LB Nagar\n",
"28 Qutbullapur\n",
"29 Kukatpally"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hyd_df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 4.3 Getting the geographical coordinates using geocoder library"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" We define a function called get_latlang to get the latitude and longitudes for a given neighborhood and loop over all the neighborhoods in our hyd_df dataframe to get respective coordinates, if any of the neighborhoods has no location information lets return and store as None"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"def get_latlang(neighborhood):\n",
" locator = Nominatim(user_agent=\"myGeocoder\")\n",
" location = locator.geocode(neighborhood+\", Hyderabad,Telangana\")\n",
" if(location==None):\n",
" return None\n",
" else:\n",
" return location.latitude,location.longitude"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" Sending one neighborhood at a time and storing the returned coordinates in a list called coordinates"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"coordinates = [ get_latlang(neighborhood) for neighborhood in hyd_df[\"Neighborhood\"] ]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" Lets temporarily store our coordinates in latitude and longitude columns of hyd_coords dataframe"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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" vertical-align: middle;\n",
" }\n",
"\n",
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" 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>Latitude</th>\n",
" <th>Longitude</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>17.456965</td>\n",
" <td>78.443478</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>17.394263</td>\n",
" <td>78.434251</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>17.476746</td>\n",
" <td>78.422108</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>17.448344</td>\n",
" <td>78.528973</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>17.465657</td>\n",
" <td>78.340672</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>17.433725</td>\n",
" <td>78.500683</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>17.528609</td>\n",
" <td>78.267425</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>17.544703</td>\n",
" <td>78.491842</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>17.311091</td>\n",
" <td>78.442585</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>17.502229</td>\n",
" <td>78.508858</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>17.449005</td>\n",
" <td>78.383138</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>17.419142</td>\n",
" <td>78.498573</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>17.361165</td>\n",
" <td>78.538756</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>17.402509</td>\n",
" <td>78.561256</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>17.437501</td>\n",
" <td>78.448251</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>17.484636</td>\n",
" <td>78.561009</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>17.430836</td>\n",
" <td>78.410288</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>17.411771</td>\n",
" <td>78.462200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>17.328115</td>\n",
" <td>78.604540</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>17.368431</td>\n",
" <td>78.523428</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>17.391852</td>\n",
" <td>78.466005</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>17.443622</td>\n",
" <td>78.351964</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>17.390263</td>\n",
" <td>78.516481</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>17.350162</td>\n",
" <td>78.551094</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>17.493084</td>\n",
" <td>78.405441</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Latitude Longitude\n",
"0 17.456965 78.443478\n",
"1 NaN NaN\n",
"2 17.394263 78.434251\n",
"3 17.476746 78.422108\n",
"4 17.448344 78.528973\n",
"5 NaN NaN\n",
"6 NaN NaN\n",
"7 17.465657 78.340672\n",
"8 17.433725 78.500683\n",
"9 17.528609 78.267425\n",
"10 17.544703 78.491842\n",
"11 17.311091 78.442585\n",
"12 17.502229 78.508858\n",
"13 17.449005 78.383138\n",
"14 NaN NaN\n",
"15 17.419142 78.498573\n",
"16 17.361165 78.538756\n",
"17 17.402509 78.561256\n",
"18 17.437501 78.448251\n",
"19 17.484636 78.561009\n",
"20 17.430836 78.410288\n",
"21 17.411771 78.462200\n",
"22 17.328115 78.604540\n",
"23 17.368431 78.523428\n",
"24 17.391852 78.466005\n",
"25 17.443622 78.351964\n",
"26 17.390263 78.516481\n",
"27 17.350162 78.551094\n",
"28 NaN NaN\n",
"29 17.493084 78.405441"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hyd_coords = pd.DataFrame(coordinates, columns=['Latitude', 'Longitude'])\n",
"hyd_coords"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" Lets add the coordinates to our dataframe hyd_df with Latitude and Longitude as column names from the temporarily created dataframe hyd_coords"
]
},
{
"cell_type": "code",
"execution_count": 11,
"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>Neighborhood</th>\n",
" <th>Latitude</th>\n",
" <th>Longitude</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Sanathnagar</td>\n",
" <td>17.456965</td>\n",
" <td>78.443478</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Ghatkesar</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Mehdipatnam</td>\n",
" <td>17.394263</td>\n",
" <td>78.434251</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Balanagar</td>\n",
" <td>17.476746</td>\n",
" <td>78.422108</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Malkajgiri</td>\n",
" <td>17.448344</td>\n",
" <td>78.528973</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Neighborhood Latitude Longitude\n",
"0 Sanathnagar 17.456965 78.443478\n",
"1 Ghatkesar NaN NaN\n",
"2 Mehdipatnam 17.394263 78.434251\n",
"3 Balanagar 17.476746 78.422108\n",
"4 Malkajgiri 17.448344 78.528973"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hyd_df['Latitude'] = hyd_coords['Latitude']\n",
"hyd_df['Longitude'] = hyd_coords['Longitude']\n",
"hyd_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" We can see some of the Latitude and Longitude values in rows of some Neighborhoods(Ghatkesar,Secunderabad Cantt,Keesara,Patancheru,Qutbullapur) which are in the out skirts of the city of Hyderabad as None, lets remove them from our dataframe hyd_df using dropna and reset index"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"scrolled": true
},
"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>Neighborhood</th>\n",
" <th>Latitude</th>\n",
" <th>Longitude</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Sanathnagar</td>\n",
" <td>17.456965</td>\n",
" <td>78.443478</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Mehdipatnam</td>\n",
" <td>17.394263</td>\n",
" <td>78.434251</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Balanagar</td>\n",
" <td>17.476746</td>\n",
" <td>78.422108</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Malkajgiri</td>\n",
" <td>17.448344</td>\n",
" <td>78.528973</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Serilingampally</td>\n",
" <td>17.465657</td>\n",
" <td>78.340672</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Secunderabad</td>\n",
" <td>17.433725</td>\n",
" <td>78.500683</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Patancheru</td>\n",
" <td>17.528609</td>\n",
" <td>78.267425</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>Kompally</td>\n",
" <td>17.544703</td>\n",
" <td>78.491842</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>Rajendranagar</td>\n",
" <td>17.311091</td>\n",
" <td>78.442585</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>Alwal</td>\n",
" <td>17.502229</td>\n",
" <td>78.508858</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>HITEC City</td>\n",
" <td>17.449005</td>\n",
" <td>78.383138</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>Musheerabad</td>\n",
" <td>17.419142</td>\n",
" <td>78.498573</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>Saroornagar</td>\n",
" <td>17.361165</td>\n",
" <td>78.538756</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>Uppal</td>\n",
" <td>17.402509</td>\n",
" <td>78.561256</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>Ameerpet</td>\n",
" <td>17.437501</td>\n",
" <td>78.448251</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>Kapra</td>\n",
" <td>17.484636</td>\n",
" <td>78.561009</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>Jubilee Hills</td>\n",
" <td>17.430836</td>\n",
" <td>78.410288</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>Khairatabad</td>\n",
" <td>17.411771</td>\n",
" <td>78.462200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>Hayathnagar</td>\n",
" <td>17.328115</td>\n",
" <td>78.604540</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>Dilsukhnagar</td>\n",
" <td>17.368431</td>\n",
" <td>78.523428</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>Nampally</td>\n",
" <td>17.391852</td>\n",
" <td>78.466005</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>Gachibowli</td>\n",
" <td>17.443622</td>\n",
" <td>78.351964</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>Amberpet</td>\n",
" <td>17.390263</td>\n",
" <td>78.516481</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>LB Nagar</td>\n",
" <td>17.350162</td>\n",
" <td>78.551094</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>Kukatpally</td>\n",
" <td>17.493084</td>\n",
" <td>78.405441</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Neighborhood Latitude Longitude\n",
"0 Sanathnagar 17.456965 78.443478\n",
"1 Mehdipatnam 17.394263 78.434251\n",
"2 Balanagar 17.476746 78.422108\n",
"3 Malkajgiri 17.448344 78.528973\n",
"4 Serilingampally 17.465657 78.340672\n",
"5 Secunderabad 17.433725 78.500683\n",
"6 Patancheru 17.528609 78.267425\n",
"7 Kompally 17.544703 78.491842\n",
"8 Rajendranagar 17.311091 78.442585\n",
"9 Alwal 17.502229 78.508858\n",
"10 HITEC City 17.449005 78.383138\n",
"11 Musheerabad 17.419142 78.498573\n",
"12 Saroornagar 17.361165 78.538756\n",
"13 Uppal 17.402509 78.561256\n",
"14 Ameerpet 17.437501 78.448251\n",
"15 Kapra 17.484636 78.561009\n",
"16 Jubilee Hills 17.430836 78.410288\n",
"17 Khairatabad 17.411771 78.462200\n",
"18 Hayathnagar 17.328115 78.604540\n",
"19 Dilsukhnagar 17.368431 78.523428\n",
"20 Nampally 17.391852 78.466005\n",
"21 Gachibowli 17.443622 78.351964\n",
"22 Amberpet 17.390263 78.516481\n",
"23 LB Nagar 17.350162 78.551094\n",
"24 Kukatpally 17.493084 78.405441"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hyd_df = hyd_df.dropna(axis = 0).reset_index(drop=True)\n",
"hyd_df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" Above is the final dataframe with neighborhood and location data as latitude and longitude on which we are going to work on now, before moving forward lets save the data in a csv file named hyd_df.csv"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"hyd_df.to_csv(\"hyd_df.csv\", index=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 4.4 Create the map of Hyderabad city with neighborhoods superimposed on top"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" Lets get the coordinates of Hyderabad, Telangana and store its value in hyd_location to create a map of hyderabad showing its neighborhoods using folium library"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The geograpical coordinates of Hyderabad,Telangana are, Latitude: 17.38878595 Longitude: 78.46106473453146\n"
]
}
],
"source": [
"address = 'Hyderabad, Telangana'\n",
"geolocator = Nominatim(user_agent=\"myGeocoder\")\n",
"hyd_location = geolocator.geocode(address)\n",
"print('The geograpical coordinates of Hyderabad,Telangana are, Latitude: ', hyd_location.latitude,' Longitude: ', hyd_location.longitude)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
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</script> onload=\"this.contentDocument.open();this.contentDocument.write(atob(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
],
"text/plain": [
"<folium.folium.Map at 0x7f2e75597b70>"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"map_hyd = folium.Map(location=[hyd_location.latitude, hyd_location.longitude], zoom_start=10)\n",
"\n",
"# add markers to map\n",
"for lat, lng, neighborhood in zip(hyd_df['Latitude'], hyd_df['Longitude'], hyd_df['Neighborhood']):\n",
" label = '{}'.format(neighborhood)\n",
" label = folium.Popup(label, parse_html=True)\n",
" folium.CircleMarker(\n",
" [lat, lng],\n",
" radius=5,\n",
" popup=label,\n",
" color='blue',\n",
" fill=True,\n",
" fill_color='#3186cc',\n",
" fill_opacity=0.7).add_to(map_hyd) \n",
"\n",
" \n",
"map_hyd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" The red color circle represents the Outer Ring Road of the city and blue dots represents the main neighborhoods of the city."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"***"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 5. Data Analysis:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 5.1 Exploring the Neighborhoods using Foursquare API"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" Here I save Foursquare credentials with client_id and client_secret in a hidden cell, so that no body can use them other than me;)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"# @hidden_cell\n",
"VERSION = '20180605' # Foursquare API version\n",
"\n",
"CLIENT_ID = '4JK4TLSKPCBG3A4UGFOCLGMY4AH0WW3RAXJKXPF5XRKPKMJ3'\n",
"CLIENT_SECRET = 'ARBBD03PGFCEY0TXMN45ZMEAIJ1QNZSZYNOFZ25MNC2TGCP4'\n",
"CODE = 'EOLCY1TCJ3UZWHV4ASDPP24RKA3NQ5Q0NKXE1FK4VGCERBPN#_=_'\n",
"ACCESS_TOKEN = 'TXW53PPKEYED1VEQV3TOBGYULVNEFW233BFMX3TSK11XSD2J'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" Now, let's get the top 100 venues that are within a radius of 2000 meters for each neighborhood using below code.\n",
" Below code first creates a url with 2000m as radius and venues limit to 100 for each neighborhood and then gets the results json using requests.get method, for each venue we get we will only take values like name, latitude,longitude and category."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"radius = 2000\n",
"LIMIT = 100\n",
"venues = []\n",
"for lat, long, neighborhood in zip(hyd_df['Latitude'], hyd_df['Longitude'], hyd_df['Neighborhood']):\n",
" \n",
" # create the API request URL\n",
" url = \"https://api.foursquare.com/v2/venues/explore?client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}\".format(\n",
" CLIENT_ID,\n",
" CLIENT_SECRET,\n",
" VERSION,\n",
" lat,\n",
" long,\n",
" radius, \n",
" LIMIT)\n",
" \n",
" # make the GET request\n",
" results = requests.get(url).json()[\"response\"]['groups'][0]['items']\n",
" \n",
" # return only relevant information for each nearby venue\n",
" for venue in results:\n",
" venues.append((\n",
" neighborhood,\n",
" lat, \n",
" long, \n",
" venue['venue']['name'], \n",
" venue['venue']['categories'][0]['name'],\n",
" venue['venue']['location']['lat'], \n",
" venue['venue']['location']['lng']))"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(883, 7)\n"
]
},
{
"data": {
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"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" 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>Neighborhood</th>\n",
" <th>Latitude</th>\n",
" <th>Longitude</th>\n",
" <th>Venue_name</th>\n",
" <th>Venue_category</th>\n",
" <th>Venue_latitude</th>\n",
" <th>Venue_longitude</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Sanathnagar</td>\n",
" <td>17.456965</td>\n",
" <td>78.443478</td>\n",
" <td>Sri Ramulu Theatre</td>\n",
" <td>Indie Movie Theater</td>\n",
" <td>17.467776</td>\n",
" <td>78.428811</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Sanathnagar</td>\n",
" <td>17.456965</td>\n",
" <td>78.443478</td>\n",
" <td>Domino's Pizza</td>\n",
" <td>Pizza Place</td>\n",
" <td>17.441819</td>\n",
" <td>78.446165</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Sanathnagar</td>\n",
" <td>17.456965</td>\n",
" <td>78.443478</td>\n",
" <td>Just Bake</td>\n",
" <td>Bakery</td>\n",
" <td>17.445300</td>\n",
" <td>78.446561</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Sanathnagar</td>\n",
" <td>17.456965</td>\n",
" <td>78.443478</td>\n",
" <td>Sanath Nagar Bus Station</td>\n",
" <td>Bus Station</td>\n",
" <td>17.454899</td>\n",
" <td>78.447747</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Sanathnagar</td>\n",
" <td>17.456965</td>\n",
" <td>78.443478</td>\n",
" <td>Greenland Restaurant</td>\n",
" <td>Indian Restaurant</td>\n",
" <td>17.443589</td>\n",
" <td>78.449170</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Neighborhood Latitude Longitude Venue_name \\\n",
"0 Sanathnagar 17.456965 78.443478 Sri Ramulu Theatre \n",
"1 Sanathnagar 17.456965 78.443478 Domino's Pizza \n",
"2 Sanathnagar 17.456965 78.443478 Just Bake \n",
"3 Sanathnagar 17.456965 78.443478 Sanath Nagar Bus Station \n",
"4 Sanathnagar 17.456965 78.443478 Greenland Restaurant \n",
"\n",
" Venue_category Venue_latitude Venue_longitude \n",
"0 Indie Movie Theater 17.467776 78.428811 \n",
"1 Pizza Place 17.441819 78.446165 \n",
"2 Bakery 17.445300 78.446561 \n",
"3 Bus Station 17.454899 78.447747 \n",
"4 Indian Restaurant 17.443589 78.449170 "
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# convert the venues list into a new DataFrame\n",
"hyd_venues_df = pd.DataFrame(venues)\n",
"\n",
"# define the column names\n",
"hyd_venues_df.columns = ['Neighborhood', 'Latitude', 'Longitude', 'Venue_name', 'Venue_category','Venue_latitude', 'Venue_longitude']\n",
"\n",
"print(hyd_venues_df.shape)\n",
"hyd_venues_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"scrolled": true
},
"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>Latitude</th>\n",
" <th>Longitude</th>\n",
" <th>Venue_name</th>\n",
" <th>Venue_category</th>\n",
" <th>Venue_latitude</th>\n",
" <th>Venue_longitude</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Neighborhood</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Alwal</th>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Amberpet</th>\n",
" <td>21</td>\n",
" <td>21</td>\n",
" <td>21</td>\n",
" <td>21</td>\n",
" <td>21</td>\n",
" <td>21</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Ameerpet</th>\n",
" <td>100</td>\n",
" <td>100</td>\n",
" <td>100</td>\n",
" <td>100</td>\n",
" <td>100</td>\n",
" <td>100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Balanagar</th>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Dilsukhnagar</th>\n",
" <td>13</td>\n",
" <td>13</td>\n",
" <td>13</td>\n",
" <td>13</td>\n",
" <td>13</td>\n",
" <td>13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Gachibowli</th>\n",
" <td>72</td>\n",
" <td>72</td>\n",
" <td>72</td>\n",
" <td>72</td>\n",
" <td>72</td>\n",
" <td>72</td>\n",
" </tr>\n",
" <tr>\n",
" <th>HITEC City</th>\n",
" <td>100</td>\n",
" <td>100</td>\n",
" <td>100</td>\n",
" <td>100</td>\n",
" <td>100</td>\n",
" <td>100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Hayathnagar</th>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Jubilee Hills</th>\n",
" <td>100</td>\n",
" <td>100</td>\n",
" <td>100</td>\n",
" <td>100</td>\n",
" <td>100</td>\n",
" <td>100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kapra</th>\n",
" <td>14</td>\n",
" <td>14</td>\n",
" <td>14</td>\n",
" <td>14</td>\n",
" <td>14</td>\n",
" <td>14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Khairatabad</th>\n",
" <td>100</td>\n",
" <td>100</td>\n",
" <td>100</td>\n",
" <td>100</td>\n",
" <td>100</td>\n",
" <td>100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kompally</th>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kukatpally</th>\n",
" <td>50</td>\n",
" <td>50</td>\n",
" <td>50</td>\n",
" <td>50</td>\n",
" <td>50</td>\n",
" <td>50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LB Nagar</th>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Malkajgiri</th>\n",
" <td>9</td>\n",
" <td>9</td>\n",
" <td>9</td>\n",
" <td>9</td>\n",
" <td>9</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mehdipatnam</th>\n",
" <td>48</td>\n",
" <td>48</td>\n",
" <td>48</td>\n",
" <td>48</td>\n",
" <td>48</td>\n",
" <td>48</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Musheerabad</th>\n",
" <td>24</td>\n",
" <td>24</td>\n",
" <td>24</td>\n",
" <td>24</td>\n",
" <td>24</td>\n",
" <td>24</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Nampally</th>\n",
" <td>73</td>\n",
" <td>73</td>\n",
" <td>73</td>\n",
" <td>73</td>\n",
" <td>73</td>\n",
" <td>73</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Patancheru</th>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Rajendranagar</th>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sanathnagar</th>\n",
" <td>17</td>\n",
" <td>17</td>\n",
" <td>17</td>\n",
" <td>17</td>\n",
" <td>17</td>\n",
" <td>17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Saroornagar</th>\n",
" <td>18</td>\n",
" <td>18</td>\n",
" <td>18</td>\n",
" <td>18</td>\n",
" <td>18</td>\n",
" <td>18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Secunderabad</th>\n",
" <td>65</td>\n",
" <td>65</td>\n",
" <td>65</td>\n",
" <td>65</td>\n",
" <td>65</td>\n",
" <td>65</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Serilingampally</th>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Uppal</th>\n",
" <td>10</td>\n",
" <td>10</td>\n",
" <td>10</td>\n",
" <td>10</td>\n",
" <td>10</td>\n",
" <td>10</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Latitude Longitude Venue_name Venue_category \\\n",
"Neighborhood \n",
"Alwal 4 4 4 4 \n",
"Amberpet 21 21 21 21 \n",
"Ameerpet 100 100 100 100 \n",
"Balanagar 7 7 7 7 \n",
"Dilsukhnagar 13 13 13 13 \n",
"Gachibowli 72 72 72 72 \n",
"HITEC City 100 100 100 100 \n",
"Hayathnagar 3 3 3 3 \n",
"Jubilee Hills 100 100 100 100 \n",
"Kapra 14 14 14 14 \n",
"Khairatabad 100 100 100 100 \n",
"Kompally 7 7 7 7 \n",
"Kukatpally 50 50 50 50 \n",
"LB Nagar 12 12 12 12 \n",
"Malkajgiri 9 9 9 9 \n",
"Mehdipatnam 48 48 48 48 \n",
"Musheerabad 24 24 24 24 \n",
"Nampally 73 73 73 73 \n",
"Patancheru 5 5 5 5 \n",
"Rajendranagar 4 4 4 4 \n",
"Sanathnagar 17 17 17 17 \n",
"Saroornagar 18 18 18 18 \n",
"Secunderabad 65 65 65 65 \n",
"Serilingampally 7 7 7 7 \n",
"Uppal 10 10 10 10 \n",
"\n",
" Venue_latitude Venue_longitude \n",
"Neighborhood \n",
"Alwal 4 4 \n",
"Amberpet 21 21 \n",
"Ameerpet 100 100 \n",
"Balanagar 7 7 \n",
"Dilsukhnagar 13 13 \n",
"Gachibowli 72 72 \n",
"HITEC City 100 100 \n",
"Hayathnagar 3 3 \n",
"Jubilee Hills 100 100 \n",
"Kapra 14 14 \n",
"Khairatabad 100 100 \n",
"Kompally 7 7 \n",
"Kukatpally 50 50 \n",
"LB Nagar 12 12 \n",
"Malkajgiri 9 9 \n",
"Mehdipatnam 48 48 \n",
"Musheerabad 24 24 \n",
"Nampally 73 73 \n",
"Patancheru 5 5 \n",
"Rajendranagar 4 4 \n",
"Sanathnagar 17 17 \n",
"Saroornagar 18 18 \n",
"Secunderabad 65 65 \n",
"Serilingampally 7 7 \n",
"Uppal 10 10 "
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hyd_venues_df.groupby([\"Neighborhood\"]).count()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"There are 131 uniques categories combining all the neighborhoods of the city\n"
]
}
],
"source": [
"print('There are {} uniques categories combining all the neighborhoods of the city'\n",
" .format(len(hyd_venues_df['Venue_category'].unique())))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" Lets see the list of venue categories that are present in the city neighborhoods"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"array(['Indie Movie Theater', 'Pizza Place', 'Bakery', 'Bus Station',\n",
" 'Indian Restaurant', 'Sandwich Place', 'Metro Station',\n",
" 'Department Store', 'Breakfast Spot', 'Train Station',\n",
" 'Restaurant', 'Asian Restaurant', 'Juice Bar', 'BBQ Joint',\n",
" 'Falafel Restaurant', 'Steakhouse', 'Café', 'Chinese Restaurant',\n",
" 'Park', 'Ice Cream Shop', 'Fast Food Restaurant',\n",
" 'Italian Restaurant', 'Hookah Bar', 'Snack Place',\n",
" 'Vegetarian / Vegan Restaurant', 'Bookstore', 'Hotel Bar',\n",
" 'Hyderabadi Restaurant', 'Electronics Store', 'Coffee Shop',\n",
" 'Grocery Store', 'Diner', 'Shopping Mall', 'Multiplex', 'Tea Room',\n",
" 'Cricket Ground', 'Light Rail Station', 'Indian Sweet Shop',\n",
" 'Arcade', 'Gym', 'Hotel', 'Sports Bar', 'Dessert Shop',\n",
" 'Flea Market', 'Smoke Shop', 'Convenience Store', 'Movie Theater',\n",
" 'Miscellaneous Shop', 'Hotel Pool', 'Harbor / Marina', 'ATM',\n",
" 'Shipping Store', 'Bridal Shop', 'Gaming Cafe', 'Golf Course',\n",
" 'Food Court', 'Business Service', 'Gastropub',\n",
" 'Athletics & Sports', 'Mexican Restaurant', 'Afghan Restaurant',\n",
" 'South Indian Restaurant', 'Dumpling Restaurant', 'Burger Joint',\n",
" 'Lounge', 'Nightclub', 'Pub', 'Bistro', 'Arts & Crafts Store',\n",
" 'Office', 'Multicuisine Indian Restaurant', 'Cocktail Bar',\n",
" 'Mediterranean Restaurant', 'Furniture / Home Store', 'Laser Tag',\n",
" 'Beer Garden', 'Bowling Alley', 'Supermarket', 'Bed & Breakfast',\n",
" 'Stadium', 'Platform', 'Gas Station', 'Food', 'Clothing Store',\n",
" 'Bar', 'Performing Arts Venue', 'Thai Restaurant',\n",
" 'Bengali Restaurant', 'Rajasthani Restaurant', 'Concert Hall',\n",
" 'Deli / Bodega', 'Middle Eastern Restaurant', 'Boutique',\n",
" 'Food Truck', 'Cupcake Shop', 'North Indian Restaurant', 'Brewery',\n",
" 'Frozen Yogurt Shop', 'Spa', 'Dance Studio', 'Liquor Store',\n",
" 'Parsi Restaurant', 'Social Club', 'Irish Pub',\n",
" 'Comfort Food Restaurant', 'New American Restaurant',\n",
" 'Scenic Lookout', 'Chaat Place', 'Science Museum',\n",
" 'American Restaurant', 'Donut Shop', 'Lake', 'Historic Site',\n",
" 'Garden', 'Mattress Store', 'Shoe Store', 'Neighborhood',\n",
" 'Mobile Phone Shop', 'Fried Chicken Joint', 'Farmers Market',\n",
" 'Gift Shop', \"Women's Store\", 'Soccer Field',\n",
" 'Japanese Restaurant', 'Gym / Fitness Center', 'Andhra Restaurant',\n",
" 'Food & Drink Shop', 'General Entertainment', 'Butcher',\n",
" 'Shop & Service', 'Food Stand'], dtype=object)"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hyd_venues_df['Venue_category'].unique()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" Lets check for mall categories if present any as the main aim of this project is on shopping mall category"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"'Shopping Mall' in hyd_venues_df['Venue_category'].unique()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It is true that Shopping Mall category is present in the fetched venues list"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 5.2 Analyze venues in each Neighborhood"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" Lets analyze each neighborhood venues by making venue category as column using get_dummies method and add city Neighborhood column at the front of the dataframe and renaming it to Neighborhoods to avoid confusion with the category 'Neighborhood'"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(883, 131)\n"
]
},
{
"data": {
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"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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" 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>Neighborhoods</th>\n",
" <th>ATM</th>\n",
" <th>Afghan Restaurant</th>\n",
" <th>American Restaurant</th>\n",
" <th>Andhra Restaurant</th>\n",
" <th>Arcade</th>\n",
" <th>Arts &amp; Crafts Store</th>\n",
" <th>Asian Restaurant</th>\n",
" <th>Athletics &amp; Sports</th>\n",
" <th>BBQ Joint</th>\n",
" <th>Bakery</th>\n",
" <th>Bar</th>\n",
" <th>Bed &amp; Breakfast</th>\n",
" <th>Beer Garden</th>\n",
" <th>Bengali Restaurant</th>\n",
" <th>Bistro</th>\n",
" <th>Bookstore</th>\n",
" <th>Boutique</th>\n",
" <th>Bowling Alley</th>\n",
" <th>Breakfast Spot</th>\n",
" <th>Brewery</th>\n",
" <th>Bridal Shop</th>\n",
" <th>Burger Joint</th>\n",
" <th>Bus Station</th>\n",
" <th>Business Service</th>\n",
" <th>Butcher</th>\n",
" <th>Café</th>\n",
" <th>Chaat Place</th>\n",
" <th>Chinese Restaurant</th>\n",
" <th>Clothing Store</th>\n",
" <th>Cocktail Bar</th>\n",
" <th>Coffee Shop</th>\n",
" <th>Comfort Food Restaurant</th>\n",
" <th>Concert Hall</th>\n",
" <th>Convenience Store</th>\n",
" <th>Cricket Ground</th>\n",
" <th>Cupcake Shop</th>\n",
" <th>Dance Studio</th>\n",
" <th>Deli / Bodega</th>\n",
" <th>Department Store</th>\n",
" <th>Dessert Shop</th>\n",
" <th>Diner</th>\n",
" <th>Donut Shop</th>\n",
" <th>Dumpling Restaurant</th>\n",
" <th>Electronics Store</th>\n",
" <th>Falafel Restaurant</th>\n",
" <th>Farmers Market</th>\n",
" <th>Fast Food Restaurant</th>\n",
" <th>Flea Market</th>\n",
" <th>Food</th>\n",
" <th>Food &amp; Drink Shop</th>\n",
" <th>Food Court</th>\n",
" <th>Food Stand</th>\n",
" <th>Food Truck</th>\n",
" <th>Fried Chicken Joint</th>\n",
" <th>Frozen Yogurt Shop</th>\n",
" <th>Furniture / Home Store</th>\n",
" <th>Gaming Cafe</th>\n",
" <th>Garden</th>\n",
" <th>Gas Station</th>\n",
" <th>Gastropub</th>\n",
" <th>General Entertainment</th>\n",
" <th>Gift Shop</th>\n",
" <th>Golf Course</th>\n",
" <th>Grocery Store</th>\n",
" <th>Gym</th>\n",
" <th>Gym / Fitness Center</th>\n",
" <th>Harbor / Marina</th>\n",
" <th>Historic Site</th>\n",
" <th>Hookah Bar</th>\n",
" <th>Hotel</th>\n",
" <th>Hotel Bar</th>\n",
" <th>Hotel Pool</th>\n",
" <th>Hyderabadi Restaurant</th>\n",
" <th>Ice Cream Shop</th>\n",
" <th>Indian Restaurant</th>\n",
" <th>Indian Sweet Shop</th>\n",
" <th>Indie Movie Theater</th>\n",
" <th>Irish Pub</th>\n",
" <th>Italian Restaurant</th>\n",
" <th>Japanese Restaurant</th>\n",
" <th>Juice Bar</th>\n",
" <th>Lake</th>\n",
" <th>Laser Tag</th>\n",
" <th>Light Rail Station</th>\n",
" <th>Liquor Store</th>\n",
" <th>Lounge</th>\n",
" <th>Mattress Store</th>\n",
" <th>Mediterranean Restaurant</th>\n",
" <th>Metro Station</th>\n",
" <th>Mexican Restaurant</th>\n",
" <th>Middle Eastern Restaurant</th>\n",
" <th>Miscellaneous Shop</th>\n",
" <th>Mobile Phone Shop</th>\n",
" <th>Movie Theater</th>\n",
" <th>Multicuisine Indian Restaurant</th>\n",
" <th>Multiplex</th>\n",
" <th>New American Restaurant</th>\n",
" <th>Nightclub</th>\n",
" <th>North Indian Restaurant</th>\n",
" <th>Office</th>\n",
" <th>Park</th>\n",
" <th>Parsi Restaurant</th>\n",
" <th>Performing Arts Venue</th>\n",
" <th>Pizza Place</th>\n",
" <th>Platform</th>\n",
" <th>Pub</th>\n",
" <th>Rajasthani Restaurant</th>\n",
" <th>Restaurant</th>\n",
" <th>Sandwich Place</th>\n",
" <th>Scenic Lookout</th>\n",
" <th>Science Museum</th>\n",
" <th>Shipping Store</th>\n",
" <th>Shoe Store</th>\n",
" <th>Shop &amp; Service</th>\n",
" <th>Shopping Mall</th>\n",
" <th>Smoke Shop</th>\n",
" <th>Snack Place</th>\n",
" <th>Soccer Field</th>\n",
" <th>Social Club</th>\n",
" <th>South Indian Restaurant</th>\n",
" <th>Spa</th>\n",
" <th>Sports Bar</th>\n",
" <th>Stadium</th>\n",
" <th>Steakhouse</th>\n",
" <th>Supermarket</th>\n",
" <th>Tea Room</th>\n",
" <th>Thai Restaurant</th>\n",
" <th>Train Station</th>\n",
" <th>Vegetarian / Vegan Restaurant</th>\n",
" <th>Women's Store</th>\n",
" </tr>\n",
" </thead>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Sanathnagar</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Sanathnagar</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Sanathnagar</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Sanathnagar</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
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" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Neighborhoods ATM Afghan Restaurant American Restaurant \\\n",
"0 Sanathnagar 0 0 0 \n",
"1 Sanathnagar 0 0 0 \n",
"2 Sanathnagar 0 0 0 \n",
"3 Sanathnagar 0 0 0 \n",
"4 Sanathnagar 0 0 0 \n",
"\n",
" Andhra Restaurant Arcade Arts & Crafts Store Asian Restaurant \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" Athletics & Sports BBQ Joint Bakery Bar Bed & Breakfast Beer Garden \\\n",
"0 0 0 0 0 0 0 \n",
"1 0 0 0 0 0 0 \n",
"2 0 0 1 0 0 0 \n",
"3 0 0 0 0 0 0 \n",
"4 0 0 0 0 0 0 \n",
"\n",
" Bengali Restaurant Bistro Bookstore Boutique Bowling Alley \\\n",
"0 0 0 0 0 0 \n",
"1 0 0 0 0 0 \n",
"2 0 0 0 0 0 \n",
"3 0 0 0 0 0 \n",
"4 0 0 0 0 0 \n",
"\n",
" Breakfast Spot Brewery Bridal Shop Burger Joint Bus Station \\\n",
"0 0 0 0 0 0 \n",
"1 0 0 0 0 0 \n",
"2 0 0 0 0 0 \n",
"3 0 0 0 0 1 \n",
"4 0 0 0 0 0 \n",
"\n",
" Business Service Butcher Café Chaat Place Chinese Restaurant \\\n",
"0 0 0 0 0 0 \n",
"1 0 0 0 0 0 \n",
"2 0 0 0 0 0 \n",
"3 0 0 0 0 0 \n",
"4 0 0 0 0 0 \n",
"\n",
" Clothing Store Cocktail Bar Coffee Shop Comfort Food Restaurant \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" Concert Hall Convenience Store Cricket Ground Cupcake Shop \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" Dance Studio Deli / Bodega Department Store Dessert Shop Diner \\\n",
"0 0 0 0 0 0 \n",
"1 0 0 0 0 0 \n",
"2 0 0 0 0 0 \n",
"3 0 0 0 0 0 \n",
"4 0 0 0 0 0 \n",
"\n",
" Donut Shop Dumpling Restaurant Electronics Store Falafel Restaurant \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" Farmers Market Fast Food Restaurant Flea Market Food Food & Drink Shop \\\n",
"0 0 0 0 0 0 \n",
"1 0 0 0 0 0 \n",
"2 0 0 0 0 0 \n",
"3 0 0 0 0 0 \n",
"4 0 0 0 0 0 \n",
"\n",
" Food Court Food Stand Food Truck Fried Chicken Joint \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" Frozen Yogurt Shop Furniture / Home Store Gaming Cafe Garden \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" Gas Station Gastropub General Entertainment Gift Shop Golf Course \\\n",
"0 0 0 0 0 0 \n",
"1 0 0 0 0 0 \n",
"2 0 0 0 0 0 \n",
"3 0 0 0 0 0 \n",
"4 0 0 0 0 0 \n",
"\n",
" Grocery Store Gym Gym / Fitness Center Harbor / Marina Historic Site \\\n",
"0 0 0 0 0 0 \n",
"1 0 0 0 0 0 \n",
"2 0 0 0 0 0 \n",
"3 0 0 0 0 0 \n",
"4 0 0 0 0 0 \n",
"\n",
" Hookah Bar Hotel Hotel Bar Hotel Pool Hyderabadi Restaurant \\\n",
"0 0 0 0 0 0 \n",
"1 0 0 0 0 0 \n",
"2 0 0 0 0 0 \n",
"3 0 0 0 0 0 \n",
"4 0 0 0 0 0 \n",
"\n",
" Ice Cream Shop Indian Restaurant Indian Sweet Shop Indie Movie Theater \\\n",
"0 0 0 0 1 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 1 0 0 \n",
"\n",
" Irish Pub Italian Restaurant Japanese Restaurant Juice Bar Lake \\\n",
"0 0 0 0 0 0 \n",
"1 0 0 0 0 0 \n",
"2 0 0 0 0 0 \n",
"3 0 0 0 0 0 \n",
"4 0 0 0 0 0 \n",
"\n",
" Laser Tag Light Rail Station Liquor Store Lounge Mattress Store \\\n",
"0 0 0 0 0 0 \n",
"1 0 0 0 0 0 \n",
"2 0 0 0 0 0 \n",
"3 0 0 0 0 0 \n",
"4 0 0 0 0 0 \n",
"\n",
" Mediterranean Restaurant Metro Station Mexican Restaurant \\\n",
"0 0 0 0 \n",
"1 0 0 0 \n",
"2 0 0 0 \n",
"3 0 0 0 \n",
"4 0 0 0 \n",
"\n",
" Middle Eastern Restaurant Miscellaneous Shop Mobile Phone Shop \\\n",
"0 0 0 0 \n",
"1 0 0 0 \n",
"2 0 0 0 \n",
"3 0 0 0 \n",
"4 0 0 0 \n",
"\n",
" Movie Theater Multicuisine Indian Restaurant Multiplex \\\n",
"0 0 0 0 \n",
"1 0 0 0 \n",
"2 0 0 0 \n",
"3 0 0 0 \n",
"4 0 0 0 \n",
"\n",
" New American Restaurant Nightclub North Indian Restaurant Office Park \\\n",
"0 0 0 0 0 0 \n",
"1 0 0 0 0 0 \n",
"2 0 0 0 0 0 \n",
"3 0 0 0 0 0 \n",
"4 0 0 0 0 0 \n",
"\n",
" Parsi Restaurant Performing Arts Venue Pizza Place Platform Pub \\\n",
"0 0 0 0 0 0 \n",
"1 0 0 1 0 0 \n",
"2 0 0 0 0 0 \n",
"3 0 0 0 0 0 \n",
"4 0 0 0 0 0 \n",
"\n",
" Rajasthani Restaurant Restaurant Sandwich Place Scenic Lookout \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" Science Museum Shipping Store Shoe Store Shop & Service Shopping Mall \\\n",
"0 0 0 0 0 0 \n",
"1 0 0 0 0 0 \n",
"2 0 0 0 0 0 \n",
"3 0 0 0 0 0 \n",
"4 0 0 0 0 0 \n",
"\n",
" Smoke Shop Snack Place Soccer Field Social Club \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" South Indian Restaurant Spa Sports Bar Stadium Steakhouse Supermarket \\\n",
"0 0 0 0 0 0 0 \n",
"1 0 0 0 0 0 0 \n",
"2 0 0 0 0 0 0 \n",
"3 0 0 0 0 0 0 \n",
"4 0 0 0 0 0 0 \n",
"\n",
" Tea Room Thai Restaurant Train Station Vegetarian / Vegan Restaurant \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" Women's Store \n",
"0 0 \n",
"1 0 \n",
"2 0 \n",
"3 0 \n",
"4 0 "
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# one hot encoding\n",
"val = hyd_venues_df['Neighborhood'] \n",
"\n",
"hyd_data_onehot = pd.get_dummies(hyd_venues_df[['Venue_category']], prefix=\"\", prefix_sep=\"\")\n",
"\n",
"# dropping the existing Neighborhood column\n",
"hyd_data_onehot.drop(['Neighborhood'], inplace=True,axis=1)\n",
"\n",
"# add neighborhood column back to dataframe in the front column\n",
"hyd_data_onehot.insert(loc=0, column='Neighborhoods', value=val)\n",
"\n",
"print(hyd_data_onehot.shape)\n",
"hyd_data_onehot.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" Lets group the neighborhoods and calculate the mean of each category and reset the index"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(25, 131)\n"
]
},
{
"data": {
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"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"\n",
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" 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>Neighborhoods</th>\n",
" <th>ATM</th>\n",
" <th>Afghan Restaurant</th>\n",
" <th>American Restaurant</th>\n",
" <th>Andhra Restaurant</th>\n",
" <th>Arcade</th>\n",
" <th>Arts &amp; Crafts Store</th>\n",
" <th>Asian Restaurant</th>\n",
" <th>Athletics &amp; Sports</th>\n",
" <th>BBQ Joint</th>\n",
" <th>Bakery</th>\n",
" <th>Bar</th>\n",
" <th>Bed &amp; Breakfast</th>\n",
" <th>Beer Garden</th>\n",
" <th>Bengali Restaurant</th>\n",
" <th>Bistro</th>\n",
" <th>Bookstore</th>\n",
" <th>Boutique</th>\n",
" <th>Bowling Alley</th>\n",
" <th>Breakfast Spot</th>\n",
" <th>Brewery</th>\n",
" <th>Bridal Shop</th>\n",
" <th>Burger Joint</th>\n",
" <th>Bus Station</th>\n",
" <th>Business Service</th>\n",
" <th>Butcher</th>\n",
" <th>Café</th>\n",
" <th>Chaat Place</th>\n",
" <th>Chinese Restaurant</th>\n",
" <th>Clothing Store</th>\n",
" <th>Cocktail Bar</th>\n",
" <th>Coffee Shop</th>\n",
" <th>Comfort Food Restaurant</th>\n",
" <th>Concert Hall</th>\n",
" <th>Convenience Store</th>\n",
" <th>Cricket Ground</th>\n",
" <th>Cupcake Shop</th>\n",
" <th>Dance Studio</th>\n",
" <th>Deli / Bodega</th>\n",
" <th>Department Store</th>\n",
" <th>Dessert Shop</th>\n",
" <th>Diner</th>\n",
" <th>Donut Shop</th>\n",
" <th>Dumpling Restaurant</th>\n",
" <th>Electronics Store</th>\n",
" <th>Falafel Restaurant</th>\n",
" <th>Farmers Market</th>\n",
" <th>Fast Food Restaurant</th>\n",
" <th>Flea Market</th>\n",
" <th>Food</th>\n",
" <th>Food &amp; Drink Shop</th>\n",
" <th>Food Court</th>\n",
" <th>Food Stand</th>\n",
" <th>Food Truck</th>\n",
" <th>Fried Chicken Joint</th>\n",
" <th>Frozen Yogurt Shop</th>\n",
" <th>Furniture / Home Store</th>\n",
" <th>Gaming Cafe</th>\n",
" <th>Garden</th>\n",
" <th>Gas Station</th>\n",
" <th>Gastropub</th>\n",
" <th>General Entertainment</th>\n",
" <th>Gift Shop</th>\n",
" <th>Golf Course</th>\n",
" <th>Grocery Store</th>\n",
" <th>Gym</th>\n",
" <th>Gym / Fitness Center</th>\n",
" <th>Harbor / Marina</th>\n",
" <th>Historic Site</th>\n",
" <th>Hookah Bar</th>\n",
" <th>Hotel</th>\n",
" <th>Hotel Bar</th>\n",
" <th>Hotel Pool</th>\n",
" <th>Hyderabadi Restaurant</th>\n",
" <th>Ice Cream Shop</th>\n",
" <th>Indian Restaurant</th>\n",
" <th>Indian Sweet Shop</th>\n",
" <th>Indie Movie Theater</th>\n",
" <th>Irish Pub</th>\n",
" <th>Italian Restaurant</th>\n",
" <th>Japanese Restaurant</th>\n",
" <th>Juice Bar</th>\n",
" <th>Lake</th>\n",
" <th>Laser Tag</th>\n",
" <th>Light Rail Station</th>\n",
" <th>Liquor Store</th>\n",
" <th>Lounge</th>\n",
" <th>Mattress Store</th>\n",
" <th>Mediterranean Restaurant</th>\n",
" <th>Metro Station</th>\n",
" <th>Mexican Restaurant</th>\n",
" <th>Middle Eastern Restaurant</th>\n",
" <th>Miscellaneous Shop</th>\n",
" <th>Mobile Phone Shop</th>\n",
" <th>Movie Theater</th>\n",
" <th>Multicuisine Indian Restaurant</th>\n",
" <th>Multiplex</th>\n",
" <th>New American Restaurant</th>\n",
" <th>Nightclub</th>\n",
" <th>North Indian Restaurant</th>\n",
" <th>Office</th>\n",
" <th>Park</th>\n",
" <th>Parsi Restaurant</th>\n",
" <th>Performing Arts Venue</th>\n",
" <th>Pizza Place</th>\n",
" <th>Platform</th>\n",
" <th>Pub</th>\n",
" <th>Rajasthani Restaurant</th>\n",
" <th>Restaurant</th>\n",
" <th>Sandwich Place</th>\n",
" <th>Scenic Lookout</th>\n",
" <th>Science Museum</th>\n",
" <th>Shipping Store</th>\n",
" <th>Shoe Store</th>\n",
" <th>Shop &amp; Service</th>\n",
" <th>Shopping Mall</th>\n",
" <th>Smoke Shop</th>\n",
" <th>Snack Place</th>\n",
" <th>Soccer Field</th>\n",
" <th>Social Club</th>\n",
" <th>South Indian Restaurant</th>\n",
" <th>Spa</th>\n",
" <th>Sports Bar</th>\n",
" <th>Stadium</th>\n",
" <th>Steakhouse</th>\n",
" <th>Supermarket</th>\n",
" <th>Tea Room</th>\n",
" <th>Thai Restaurant</th>\n",
" <th>Train Station</th>\n",
" <th>Vegetarian / Vegan Restaurant</th>\n",
" <th>Women's Store</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
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" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Amberpet</td>\n",
" <td>0.000000</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Ameerpet</td>\n",
" <td>0.000000</td>\n",
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" <td>0.010000</td>\n",
" <td>0.030000</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.01</td>\n",
" <td>0.00</td>\n",
" <td>0.030000</td>\n",
" <td>0.01</td>\n",
" <td>0.000000</td>\n",
" <td>0.020000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.040000</td>\n",
" <td>0.000000</td>\n",
" <td>0.040000</td>\n",
" <td>0.030000</td>\n",
" <td>0.01</td>\n",
" <td>0.040000</td>\n",
" <td>0.00</td>\n",
" <td>0.01</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.02</td>\n",
" <td>0.020000</td>\n",
" <td>0.000000</td>\n",
" <td>0.020000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.030000</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.020000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.020000</td>\n",
" <td>0.050000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.020000</td>\n",
" <td>0.190000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.020000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.010000</td>\n",
" <td>0.040000</td>\n",
" <td>0.000000</td>\n",
" <td>0.01</td>\n",
" <td>0.01</td>\n",
" <td>0.010000</td>\n",
" <td>0.050000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.010000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.01</td>\n",
" <td>0.000000</td>\n",
" <td>0.050000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Balanagar</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.142857</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.285714</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.142857</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.142857</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.142857</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.142857</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Dilsukhnagar</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.076923</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.076923</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.153846</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.076923</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.076923</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.076923</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.076923</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.076923</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.076923</td>\n",
" <td>0.00</td>\n",
" <td>0.076923</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.076923</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.076923</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Gachibowli</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.013889</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.013889</td>\n",
" <td>0.041667</td>\n",
" <td>0.013889</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.013889</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.013889</td>\n",
" <td>0.000000</td>\n",
" <td>0.013889</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.027778</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.041667</td>\n",
" <td>0.013889</td>\n",
" <td>0.000000</td>\n",
" <td>0.013889</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.027778</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.013889</td>\n",
" <td>0.00</td>\n",
" <td>0.013889</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.013889</td>\n",
" <td>0.000000</td>\n",
" <td>0.013889</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.013889</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.013889</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.041667</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.013889</td>\n",
" <td>0.055556</td>\n",
" <td>0.194444</td>\n",
" <td>0.013889</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.013889</td>\n",
" <td>0.013889</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.013889</td>\n",
" <td>0.013889</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.013889</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.055556</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.041667</td>\n",
" <td>0.055556</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.027778</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.027778</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.027778</td>\n",
" <td>0.000000</td>\n",
" <td>0.013889</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.027778</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>HITEC City</td>\n",
" <td>0.000000</td>\n",
" <td>0.01</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.01</td>\n",
" <td>0.040000</td>\n",
" <td>0.01</td>\n",
" <td>0.020000</td>\n",
" <td>0.020000</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.01</td>\n",
" <td>0.00</td>\n",
" <td>0.01</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.010000</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.01</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.100000</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.01</td>\n",
" <td>0.040000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.020000</td>\n",
" <td>0.010000</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.01</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.040000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.020000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.01</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.050000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.130000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.030000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.01</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.020000</td>\n",
" <td>0.000000</td>\n",
" <td>0.01</td>\n",
" <td>0.000000</td>\n",
" <td>0.01</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.01</td>\n",
" <td>0.010000</td>\n",
" <td>0.00</td>\n",
" <td>0.01</td>\n",
" <td>0.00</td>\n",
" <td>0.01</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.020000</td>\n",
" <td>0.000000</td>\n",
" <td>0.01</td>\n",
" <td>0.00</td>\n",
" <td>0.060000</td>\n",
" <td>0.020000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.020000</td>\n",
" <td>0.000000</td>\n",
" <td>0.020000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.010000</td>\n",
" <td>0.00</td>\n",
" <td>0.020000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.020000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>Hayathnagar</td>\n",
" <td>0.666667</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.333333</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>Jubilee Hills</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.010000</td>\n",
" <td>0.00</td>\n",
" <td>0.020000</td>\n",
" <td>0.020000</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.01</td>\n",
" <td>0.00</td>\n",
" <td>0.01</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.010000</td>\n",
" <td>0.010000</td>\n",
" <td>0.040000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.110000</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.02</td>\n",
" <td>0.060000</td>\n",
" <td>0.01</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.01</td>\n",
" <td>0.01</td>\n",
" <td>0.01</td>\n",
" <td>0.000000</td>\n",
" <td>0.030000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.00</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.01</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.01</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.020000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.040000</td>\n",
" <td>0.030000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.040000</td>\n",
" <td>0.060000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.01</td>\n",
" <td>0.040000</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.050000</td>\n",
" <td>0.000000</td>\n",
" <td>0.01</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.03</td>\n",
" <td>0.000000</td>\n",
" <td>0.01</td>\n",
" <td>0.01</td>\n",
" <td>0.01</td>\n",
" <td>0.00</td>\n",
" <td>0.010000</td>\n",
" <td>0.01</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.020000</td>\n",
" <td>0.030000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.020000</td>\n",
" <td>0.000000</td>\n",
" <td>0.01</td>\n",
" <td>0.010000</td>\n",
" <td>0.02</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>Kapra</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.071429</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.142857</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.071429</td>\n",
" <td>0.000000</td>\n",
" <td>0.071429</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.071429</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.142857</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.071429</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.142857</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.071429</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.071429</td>\n",
" <td>0.000000</td>\n",
" <td>0.071429</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>Khairatabad</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.01</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.010000</td>\n",
" <td>0.00</td>\n",
" <td>0.010000</td>\n",
" <td>0.020000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.01</td>\n",
" <td>0.030000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.060000</td>\n",
" <td>0.010000</td>\n",
" <td>0.030000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.040000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.020000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.060000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.01</td>\n",
" <td>0.000000</td>\n",
" <td>0.060000</td>\n",
" <td>0.030000</td>\n",
" <td>0.000000</td>\n",
" <td>0.020000</td>\n",
" <td>0.030000</td>\n",
" <td>0.100000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.020000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.090000</td>\n",
" <td>0.00</td>\n",
" <td>0.01</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.010000</td>\n",
" <td>0.00</td>\n",
" <td>0.020000</td>\n",
" <td>0.040000</td>\n",
" <td>0.000000</td>\n",
" <td>0.02</td>\n",
" <td>0.00</td>\n",
" <td>0.030000</td>\n",
" <td>0.030000</td>\n",
" <td>0.01</td>\n",
" <td>0.010000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.030000</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.010000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>Kompally</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.142857</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.142857</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.142857</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.285714</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.285714</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>Kukatpally</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.02</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.02</td>\n",
" <td>0.000000</td>\n",
" <td>0.080000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.02</td>\n",
" <td>0.020000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.020000</td>\n",
" <td>0.00</td>\n",
" <td>0.020000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.160000</td>\n",
" <td>0.000000</td>\n",
" <td>0.020000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.02</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.020000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.020000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.060000</td>\n",
" <td>0.180000</td>\n",
" <td>0.000000</td>\n",
" <td>0.020000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.020000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.020000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.020000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.060000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.020000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.02</td>\n",
" <td>0.080000</td>\n",
" <td>0.000000</td>\n",
" <td>0.040000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.020000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>LB Nagar</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.083333</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.083333</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.083333</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.083333</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.083333</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.083333</td>\n",
" <td>0.000000</td>\n",
" <td>0.083333</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.083333</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.083333</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.166667</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.083333</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>Malkajgiri</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.111111</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.111111</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.111111</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.111111</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.111111</td>\n",
" <td>0.000000</td>\n",
" <td>0.111111</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.111111</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.111111</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.111111</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>Mehdipatnam</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.062500</td>\n",
" <td>0.00</td>\n",
" <td>0.020833</td>\n",
" <td>0.020833</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.020833</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.083333</td>\n",
" <td>0.000000</td>\n",
" <td>0.020833</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.020833</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.020833</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.041667</td>\n",
" <td>0.020833</td>\n",
" <td>0.000000</td>\n",
" <td>0.083333</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.020833</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.020833</td>\n",
" <td>0.000000</td>\n",
" <td>0.020833</td>\n",
" <td>0.000000</td>\n",
" <td>0.020833</td>\n",
" <td>0.041667</td>\n",
" <td>0.187500</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.020833</td>\n",
" <td>0.000000</td>\n",
" <td>0.041667</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.020833</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.041667</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.062500</td>\n",
" <td>0.020833</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.020833</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.020833</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.020833</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>Musheerabad</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.083333</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.041667</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.041667</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.041667</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.041667</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.041667</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.083333</td>\n",
" <td>0.041667</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.250000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.125000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.125000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.041667</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.041667</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>Nampally</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.054795</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.027397</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.013699</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.054795</td>\n",
" <td>0.013699</td>\n",
" <td>0.041096</td>\n",
" <td>0.013699</td>\n",
" <td>0.00</td>\n",
" <td>0.013699</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.013699</td>\n",
" <td>0.013699</td>\n",
" <td>0.013699</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.013699</td>\n",
" <td>0.027397</td>\n",
" <td>0.000000</td>\n",
" <td>0.013699</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.013699</td>\n",
" <td>0.013699</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.013699</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.082192</td>\n",
" <td>0.027397</td>\n",
" <td>0.000000</td>\n",
" <td>0.027397</td>\n",
" <td>0.054795</td>\n",
" <td>0.123288</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.027397</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.013699</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.013699</td>\n",
" <td>0.000000</td>\n",
" <td>0.013699</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.013699</td>\n",
" <td>0.000000</td>\n",
" <td>0.013699</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.027397</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.013699</td>\n",
" <td>0.0</td>\n",
" <td>0.013699</td>\n",
" <td>0.00</td>\n",
" <td>0.013699</td>\n",
" <td>0.013699</td>\n",
" <td>0.027397</td>\n",
" <td>0.013699</td>\n",
" <td>0.00</td>\n",
" <td>0.041096</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.013699</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.013699</td>\n",
" <td>0.013699</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>Patancheru</td>\n",
" <td>0.600000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.2</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.2</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>Rajendranagar</td>\n",
" <td>0.250000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.250000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.25</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.250000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>Sanathnagar</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.058824</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.117647</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.058824</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.117647</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.058824</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.117647</td>\n",
" <td>0.000000</td>\n",
" <td>0.117647</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.058824</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.058824</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.058824</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.058824</td>\n",
" <td>0.058824</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.058824</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>Saroornagar</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.055556</td>\n",
" <td>0.000000</td>\n",
" <td>0.055556</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.055556</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.055556</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.055556</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.055556</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.055556</td>\n",
" <td>0.000000</td>\n",
" <td>0.166667</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.055556</td>\n",
" <td>0.00</td>\n",
" <td>0.055556</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.055556</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.055556</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.055556</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.055556</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.055556</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.055556</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>Secunderabad</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.030769</td>\n",
" <td>0.00</td>\n",
" <td>0.015385</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.076923</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.030769</td>\n",
" <td>0.000000</td>\n",
" <td>0.030769</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.061538</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.015385</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.015385</td>\n",
" <td>0.015385</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.030769</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.046154</td>\n",
" <td>0.030769</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.015385</td>\n",
" <td>0.000000</td>\n",
" <td>0.030769</td>\n",
" <td>0.00</td>\n",
" <td>0.015385</td>\n",
" <td>0.107692</td>\n",
" <td>0.015385</td>\n",
" <td>0.015385</td>\n",
" <td>0.015385</td>\n",
" <td>0.015385</td>\n",
" <td>0.123077</td>\n",
" <td>0.015385</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.015385</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.015385</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.015385</td>\n",
" <td>0.000000</td>\n",
" <td>0.015385</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.015385</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
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" <td>0.00</td>\n",
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" <td>0.015385</td>\n",
" <td>0.00</td>\n",
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" <td>0.015385</td>\n",
" <td>0.015385</td>\n",
" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.015385</td>\n",
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" <td>0.000000</td>\n",
" <td>0.015385</td>\n",
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" <td>0.030769</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>Serilingampally</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.142857</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
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" <td>0.00</td>\n",
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" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
" <td>0.142857</td>\n",
" <td>0.142857</td>\n",
" <td>0.142857</td>\n",
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" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.285714</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.142857</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
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" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>Uppal</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.100000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.100000</td>\n",
" <td>0.100000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.100000</td>\n",
" <td>0.100000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
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" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.1</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.200000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.100000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
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" <td>0.000000</td>\n",
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" <td>0.00</td>\n",
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" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
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" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Neighborhoods ATM Afghan Restaurant American Restaurant \\\n",
"0 Alwal 0.000000 0.00 0.00 \n",
"1 Amberpet 0.000000 0.00 0.00 \n",
"2 Ameerpet 0.000000 0.00 0.00 \n",
"3 Balanagar 0.000000 0.00 0.00 \n",
"4 Dilsukhnagar 0.000000 0.00 0.00 \n",
"5 Gachibowli 0.000000 0.00 0.00 \n",
"6 HITEC City 0.000000 0.01 0.00 \n",
"7 Hayathnagar 0.666667 0.00 0.00 \n",
"8 Jubilee Hills 0.000000 0.00 0.00 \n",
"9 Kapra 0.000000 0.00 0.00 \n",
"10 Khairatabad 0.000000 0.00 0.01 \n",
"11 Kompally 0.000000 0.00 0.00 \n",
"12 Kukatpally 0.000000 0.00 0.02 \n",
"13 LB Nagar 0.000000 0.00 0.00 \n",
"14 Malkajgiri 0.000000 0.00 0.00 \n",
"15 Mehdipatnam 0.000000 0.00 0.00 \n",
"16 Musheerabad 0.000000 0.00 0.00 \n",
"17 Nampally 0.000000 0.00 0.00 \n",
"18 Patancheru 0.600000 0.00 0.00 \n",
"19 Rajendranagar 0.250000 0.00 0.00 \n",
"20 Sanathnagar 0.000000 0.00 0.00 \n",
"21 Saroornagar 0.000000 0.00 0.00 \n",
"22 Secunderabad 0.000000 0.00 0.00 \n",
"23 Serilingampally 0.000000 0.00 0.00 \n",
"24 Uppal 0.000000 0.00 0.00 \n",
"\n",
" Andhra Restaurant Arcade Arts & Crafts Store Asian Restaurant \\\n",
"0 0.000000 0.000000 0.00 0.000000 \n",
"1 0.000000 0.000000 0.00 0.000000 \n",
"2 0.000000 0.000000 0.00 0.010000 \n",
"3 0.000000 0.000000 0.00 0.000000 \n",
"4 0.000000 0.000000 0.00 0.000000 \n",
"5 0.013889 0.000000 0.00 0.000000 \n",
"6 0.000000 0.000000 0.01 0.040000 \n",
"7 0.000000 0.000000 0.00 0.000000 \n",
"8 0.000000 0.000000 0.00 0.010000 \n",
"9 0.000000 0.000000 0.00 0.000000 \n",
"10 0.000000 0.000000 0.00 0.010000 \n",
"11 0.000000 0.142857 0.00 0.000000 \n",
"12 0.000000 0.000000 0.00 0.000000 \n",
"13 0.000000 0.000000 0.00 0.083333 \n",
"14 0.000000 0.000000 0.00 0.000000 \n",
"15 0.000000 0.000000 0.00 0.062500 \n",
"16 0.000000 0.000000 0.00 0.083333 \n",
"17 0.000000 0.000000 0.00 0.000000 \n",
"18 0.000000 0.000000 0.00 0.000000 \n",
"19 0.000000 0.000000 0.00 0.000000 \n",
"20 0.000000 0.000000 0.00 0.058824 \n",
"21 0.000000 0.000000 0.00 0.000000 \n",
"22 0.000000 0.030769 0.00 0.015385 \n",
"23 0.000000 0.142857 0.00 0.000000 \n",
"24 0.000000 0.000000 0.00 0.000000 \n",
"\n",
" Athletics & Sports BBQ Joint Bakery Bar Bed & Breakfast \\\n",
"0 0.00 0.000000 0.000000 0.000000 0.000000 \n",
"1 0.00 0.000000 0.000000 0.000000 0.000000 \n",
"2 0.00 0.010000 0.030000 0.010000 0.000000 \n",
"3 0.00 0.000000 0.142857 0.000000 0.000000 \n",
"4 0.00 0.000000 0.076923 0.000000 0.000000 \n",
"5 0.00 0.013889 0.041667 0.013889 0.000000 \n",
"6 0.01 0.020000 0.020000 0.000000 0.010000 \n",
"7 0.00 0.000000 0.000000 0.000000 0.000000 \n",
"8 0.00 0.020000 0.020000 0.010000 0.000000 \n",
"9 0.00 0.000000 0.071429 0.000000 0.000000 \n",
"10 0.00 0.010000 0.020000 0.000000 0.000000 \n",
"11 0.00 0.000000 0.000000 0.000000 0.000000 \n",
"12 0.02 0.000000 0.080000 0.000000 0.000000 \n",
"13 0.00 0.000000 0.000000 0.000000 0.083333 \n",
"14 0.00 0.000000 0.000000 0.000000 0.000000 \n",
"15 0.00 0.020833 0.020833 0.000000 0.000000 \n",
"16 0.00 0.000000 0.000000 0.000000 0.000000 \n",
"17 0.00 0.000000 0.054795 0.000000 0.000000 \n",
"18 0.00 0.000000 0.000000 0.000000 0.000000 \n",
"19 0.00 0.000000 0.000000 0.000000 0.000000 \n",
"20 0.00 0.000000 0.117647 0.000000 0.000000 \n",
"21 0.00 0.000000 0.055556 0.000000 0.055556 \n",
"22 0.00 0.000000 0.076923 0.000000 0.000000 \n",
"23 0.00 0.000000 0.000000 0.000000 0.000000 \n",
"24 0.00 0.000000 0.000000 0.000000 0.000000 \n",
"\n",
" Beer Garden Bengali Restaurant Bistro Bookstore Boutique \\\n",
"0 0.00 0.00 0.00 0.000000 0.00 \n",
"1 0.00 0.00 0.00 0.000000 0.00 \n",
"2 0.00 0.01 0.00 0.030000 0.01 \n",
"3 0.00 0.00 0.00 0.000000 0.00 \n",
"4 0.00 0.00 0.00 0.000000 0.00 \n",
"5 0.00 0.00 0.00 0.000000 0.00 \n",
"6 0.01 0.00 0.01 0.000000 0.00 \n",
"7 0.00 0.00 0.00 0.000000 0.00 \n",
"8 0.01 0.00 0.01 0.000000 0.00 \n",
"9 0.00 0.00 0.00 0.000000 0.00 \n",
"10 0.00 0.00 0.01 0.030000 0.00 \n",
"11 0.00 0.00 0.00 0.000000 0.00 \n",
"12 0.00 0.00 0.00 0.000000 0.00 \n",
"13 0.00 0.00 0.00 0.000000 0.00 \n",
"14 0.00 0.00 0.00 0.000000 0.00 \n",
"15 0.00 0.00 0.00 0.020833 0.00 \n",
"16 0.00 0.00 0.00 0.000000 0.00 \n",
"17 0.00 0.00 0.00 0.000000 0.00 \n",
"18 0.00 0.00 0.00 0.000000 0.00 \n",
"19 0.00 0.00 0.00 0.000000 0.00 \n",
"20 0.00 0.00 0.00 0.000000 0.00 \n",
"21 0.00 0.00 0.00 0.000000 0.00 \n",
"22 0.00 0.00 0.00 0.000000 0.00 \n",
"23 0.00 0.00 0.00 0.000000 0.00 \n",
"24 0.00 0.00 0.00 0.000000 0.00 \n",
"\n",
" Bowling Alley Breakfast Spot Brewery Bridal Shop Burger Joint \\\n",
"0 0.000000 0.000000 0.000000 0.0 0.00 \n",
"1 0.000000 0.000000 0.000000 0.0 0.00 \n",
"2 0.000000 0.020000 0.000000 0.0 0.00 \n",
"3 0.000000 0.000000 0.000000 0.0 0.00 \n",
"4 0.076923 0.000000 0.000000 0.0 0.00 \n",
"5 0.000000 0.000000 0.013889 0.0 0.00 \n",
"6 0.010000 0.010000 0.000000 0.0 0.01 \n",
"7 0.000000 0.000000 0.000000 0.0 0.00 \n",
"8 0.010000 0.010000 0.040000 0.0 0.00 \n",
"9 0.000000 0.000000 0.000000 0.0 0.00 \n",
"10 0.000000 0.000000 0.000000 0.0 0.00 \n",
"11 0.000000 0.000000 0.000000 0.0 0.00 \n",
"12 0.000000 0.000000 0.000000 0.0 0.00 \n",
"13 0.000000 0.000000 0.000000 0.0 0.00 \n",
"14 0.000000 0.111111 0.000000 0.0 0.00 \n",
"15 0.000000 0.000000 0.000000 0.0 0.00 \n",
"16 0.000000 0.000000 0.000000 0.0 0.00 \n",
"17 0.000000 0.027397 0.000000 0.0 0.00 \n",
"18 0.000000 0.000000 0.000000 0.2 0.00 \n",
"19 0.000000 0.250000 0.000000 0.0 0.00 \n",
"20 0.000000 0.058824 0.000000 0.0 0.00 \n",
"21 0.055556 0.000000 0.000000 0.0 0.00 \n",
"22 0.000000 0.000000 0.000000 0.0 0.00 \n",
"23 0.000000 0.000000 0.000000 0.0 0.00 \n",
"24 0.000000 0.000000 0.000000 0.0 0.00 \n",
"\n",
" Bus Station Business Service Butcher Café Chaat Place \\\n",
"0 0.250000 0.00 0.00 0.000000 0.000000 \n",
"1 0.000000 0.00 0.00 0.047619 0.000000 \n",
"2 0.000000 0.00 0.00 0.040000 0.000000 \n",
"3 0.000000 0.00 0.00 0.000000 0.000000 \n",
"4 0.000000 0.00 0.00 0.153846 0.000000 \n",
"5 0.000000 0.00 0.00 0.013889 0.000000 \n",
"6 0.000000 0.00 0.00 0.100000 0.000000 \n",
"7 0.000000 0.00 0.00 0.000000 0.000000 \n",
"8 0.000000 0.00 0.00 0.110000 0.000000 \n",
"9 0.000000 0.00 0.00 0.142857 0.000000 \n",
"10 0.000000 0.00 0.00 0.060000 0.010000 \n",
"11 0.000000 0.00 0.00 0.000000 0.000000 \n",
"12 0.000000 0.00 0.02 0.020000 0.000000 \n",
"13 0.000000 0.00 0.00 0.000000 0.000000 \n",
"14 0.000000 0.00 0.00 0.111111 0.000000 \n",
"15 0.000000 0.00 0.00 0.083333 0.000000 \n",
"16 0.000000 0.00 0.00 0.041667 0.000000 \n",
"17 0.013699 0.00 0.00 0.054795 0.013699 \n",
"18 0.000000 0.00 0.00 0.000000 0.000000 \n",
"19 0.000000 0.25 0.00 0.000000 0.000000 \n",
"20 0.117647 0.00 0.00 0.000000 0.000000 \n",
"21 0.000000 0.00 0.00 0.055556 0.000000 \n",
"22 0.000000 0.00 0.00 0.030769 0.000000 \n",
"23 0.000000 0.00 0.00 0.000000 0.000000 \n",
"24 0.100000 0.00 0.00 0.000000 0.000000 \n",
"\n",
" Chinese Restaurant Clothing Store Cocktail Bar Coffee Shop \\\n",
"0 0.000000 0.000000 0.00 0.000000 \n",
"1 0.000000 0.000000 0.00 0.095238 \n",
"2 0.040000 0.030000 0.01 0.040000 \n",
"3 0.000000 0.000000 0.00 0.000000 \n",
"4 0.000000 0.000000 0.00 0.000000 \n",
"5 0.013889 0.000000 0.00 0.027778 \n",
"6 0.010000 0.000000 0.01 0.040000 \n",
"7 0.000000 0.000000 0.00 0.000000 \n",
"8 0.010000 0.000000 0.02 0.060000 \n",
"9 0.000000 0.000000 0.00 0.000000 \n",
"10 0.030000 0.000000 0.00 0.040000 \n",
"11 0.000000 0.000000 0.00 0.000000 \n",
"12 0.000000 0.020000 0.00 0.020000 \n",
"13 0.000000 0.000000 0.00 0.000000 \n",
"14 0.000000 0.000000 0.00 0.000000 \n",
"15 0.020833 0.000000 0.00 0.020833 \n",
"16 0.000000 0.000000 0.00 0.041667 \n",
"17 0.041096 0.013699 0.00 0.013699 \n",
"18 0.000000 0.000000 0.00 0.000000 \n",
"19 0.000000 0.000000 0.00 0.000000 \n",
"20 0.000000 0.000000 0.00 0.000000 \n",
"21 0.000000 0.000000 0.00 0.055556 \n",
"22 0.030769 0.000000 0.00 0.061538 \n",
"23 0.000000 0.000000 0.00 0.000000 \n",
"24 0.000000 0.000000 0.00 0.000000 \n",
"\n",
" Comfort Food Restaurant Concert Hall Convenience Store Cricket Ground \\\n",
"0 0.00 0.00 0.000000 0.000000 \n",
"1 0.00 0.00 0.095238 0.000000 \n",
"2 0.00 0.01 0.010000 0.000000 \n",
"3 0.00 0.00 0.000000 0.000000 \n",
"4 0.00 0.00 0.076923 0.000000 \n",
"5 0.00 0.00 0.000000 0.000000 \n",
"6 0.00 0.00 0.000000 0.000000 \n",
"7 0.00 0.00 0.000000 0.000000 \n",
"8 0.01 0.00 0.000000 0.000000 \n",
"9 0.00 0.00 0.000000 0.000000 \n",
"10 0.00 0.00 0.010000 0.000000 \n",
"11 0.00 0.00 0.000000 0.000000 \n",
"12 0.00 0.00 0.000000 0.000000 \n",
"13 0.00 0.00 0.000000 0.000000 \n",
"14 0.00 0.00 0.000000 0.111111 \n",
"15 0.00 0.00 0.000000 0.000000 \n",
"16 0.00 0.00 0.041667 0.000000 \n",
"17 0.00 0.00 0.000000 0.000000 \n",
"18 0.00 0.00 0.000000 0.000000 \n",
"19 0.00 0.00 0.000000 0.000000 \n",
"20 0.00 0.00 0.000000 0.000000 \n",
"21 0.00 0.00 0.000000 0.000000 \n",
"22 0.00 0.00 0.015385 0.000000 \n",
"23 0.00 0.00 0.000000 0.000000 \n",
"24 0.00 0.00 0.100000 0.100000 \n",
"\n",
" Cupcake Shop Dance Studio Deli / Bodega Department Store Dessert Shop \\\n",
"0 0.00 0.00 0.00 0.250000 0.000000 \n",
"1 0.00 0.00 0.00 0.000000 0.000000 \n",
"2 0.00 0.00 0.02 0.020000 0.000000 \n",
"3 0.00 0.00 0.00 0.000000 0.000000 \n",
"4 0.00 0.00 0.00 0.076923 0.000000 \n",
"5 0.00 0.00 0.00 0.041667 0.013889 \n",
"6 0.00 0.00 0.00 0.020000 0.010000 \n",
"7 0.00 0.00 0.00 0.000000 0.000000 \n",
"8 0.01 0.01 0.01 0.000000 0.030000 \n",
"9 0.00 0.00 0.00 0.071429 0.000000 \n",
"10 0.00 0.00 0.00 0.010000 0.000000 \n",
"11 0.00 0.00 0.00 0.000000 0.000000 \n",
"12 0.00 0.00 0.00 0.000000 0.000000 \n",
"13 0.00 0.00 0.00 0.083333 0.000000 \n",
"14 0.00 0.00 0.00 0.111111 0.000000 \n",
"15 0.00 0.00 0.00 0.000000 0.000000 \n",
"16 0.00 0.00 0.00 0.000000 0.000000 \n",
"17 0.00 0.00 0.00 0.013699 0.013699 \n",
"18 0.00 0.00 0.00 0.000000 0.000000 \n",
"19 0.00 0.00 0.00 0.000000 0.000000 \n",
"20 0.00 0.00 0.00 0.058824 0.000000 \n",
"21 0.00 0.00 0.00 0.000000 0.000000 \n",
"22 0.00 0.00 0.00 0.015385 0.015385 \n",
"23 0.00 0.00 0.00 0.000000 0.000000 \n",
"24 0.00 0.00 0.00 0.000000 0.000000 \n",
"\n",
" Diner Donut Shop Dumpling Restaurant Electronics Store \\\n",
"0 0.000000 0.000000 0.00 0.000000 \n",
"1 0.000000 0.000000 0.00 0.047619 \n",
"2 0.020000 0.000000 0.00 0.010000 \n",
"3 0.000000 0.000000 0.00 0.000000 \n",
"4 0.000000 0.000000 0.00 0.000000 \n",
"5 0.000000 0.013889 0.00 0.000000 \n",
"6 0.010000 0.000000 0.01 0.000000 \n",
"7 0.000000 0.000000 0.00 0.000000 \n",
"8 0.000000 0.000000 0.00 0.000000 \n",
"9 0.071429 0.000000 0.00 0.071429 \n",
"10 0.000000 0.020000 0.00 0.000000 \n",
"11 0.000000 0.000000 0.00 0.000000 \n",
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"13 0.000000 0.000000 0.00 0.083333 \n",
"14 0.000000 0.000000 0.00 0.000000 \n",
"15 0.020833 0.000000 0.00 0.041667 \n",
"16 0.000000 0.000000 0.00 0.000000 \n",
"17 0.013699 0.000000 0.00 0.000000 \n",
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"23 0.000000 0.000000 0.00 0.000000 \n",
"24 0.000000 0.000000 0.00 0.000000 \n",
"\n",
" Falafel Restaurant Farmers Market Fast Food Restaurant Flea Market \\\n",
"0 0.000000 0.000000 0.000000 0.000000 \n",
"1 0.000000 0.000000 0.000000 0.000000 \n",
"2 0.000000 0.000000 0.030000 0.000000 \n",
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"13 0.000000 0.000000 0.00 0.000000 \n",
"14 0.000000 0.000000 0.00 0.000000 \n",
"15 0.062500 0.020833 0.00 0.000000 \n",
"16 0.041667 0.000000 0.00 0.000000 \n",
"17 0.027397 0.000000 0.00 0.013699 \n",
"18 0.000000 0.000000 0.00 0.000000 \n",
"19 0.000000 0.000000 0.00 0.000000 \n",
"20 0.058824 0.058824 0.00 0.000000 \n",
"21 0.055556 0.000000 0.00 0.000000 \n",
"22 0.015385 0.015385 0.00 0.000000 \n",
"23 0.142857 0.000000 0.00 0.000000 \n",
"24 0.000000 0.000000 0.00 0.000000 \n",
"\n",
" Shipping Store Shoe Store Shop & Service Shopping Mall Smoke Shop \\\n",
"0 0.0 0.000000 0.00 0.000000 0.000000 \n",
"1 0.0 0.000000 0.00 0.047619 0.000000 \n",
"2 0.0 0.000000 0.00 0.010000 0.000000 \n",
"3 0.0 0.000000 0.00 0.142857 0.000000 \n",
"4 0.0 0.000000 0.00 0.076923 0.000000 \n",
"5 0.0 0.000000 0.00 0.000000 0.000000 \n",
"6 0.0 0.000000 0.00 0.020000 0.000000 \n",
"7 0.0 0.000000 0.00 0.000000 0.000000 \n",
"8 0.0 0.000000 0.00 0.000000 0.000000 \n",
"9 0.0 0.000000 0.00 0.071429 0.000000 \n",
"10 0.0 0.000000 0.00 0.030000 0.010000 \n",
"11 0.0 0.000000 0.00 0.000000 0.000000 \n",
"12 0.0 0.000000 0.02 0.080000 0.000000 \n",
"13 0.0 0.000000 0.00 0.000000 0.000000 \n",
"14 0.0 0.000000 0.00 0.000000 0.000000 \n",
"15 0.0 0.000000 0.00 0.000000 0.000000 \n",
"16 0.0 0.000000 0.00 0.000000 0.000000 \n",
"17 0.0 0.013699 0.00 0.013699 0.013699 \n",
"18 0.2 0.000000 0.00 0.000000 0.000000 \n",
"19 0.0 0.000000 0.00 0.000000 0.000000 \n",
"20 0.0 0.000000 0.00 0.000000 0.000000 \n",
"21 0.0 0.000000 0.00 0.055556 0.000000 \n",
"22 0.0 0.000000 0.00 0.000000 0.015385 \n",
"23 0.0 0.000000 0.00 0.000000 0.000000 \n",
"24 0.0 0.000000 0.00 0.000000 0.000000 \n",
"\n",
" Snack Place Soccer Field Social Club South Indian Restaurant Spa \\\n",
"0 0.000000 0.000000 0.00 0.000000 0.00 \n",
"1 0.000000 0.000000 0.00 0.000000 0.00 \n",
"2 0.000000 0.000000 0.00 0.010000 0.00 \n",
"3 0.000000 0.000000 0.00 0.000000 0.00 \n",
"4 0.000000 0.000000 0.00 0.000000 0.00 \n",
"5 0.027778 0.000000 0.00 0.027778 0.00 \n",
"6 0.020000 0.000000 0.00 0.010000 0.00 \n",
"7 0.000000 0.000000 0.00 0.000000 0.00 \n",
"8 0.020000 0.000000 0.01 0.010000 0.02 \n",
"9 0.071429 0.000000 0.00 0.000000 0.00 \n",
"10 0.000000 0.000000 0.00 0.010000 0.00 \n",
"11 0.000000 0.000000 0.00 0.000000 0.00 \n",
"12 0.040000 0.000000 0.00 0.000000 0.00 \n",
"13 0.000000 0.000000 0.00 0.083333 0.00 \n",
"14 0.000000 0.000000 0.00 0.000000 0.00 \n",
"15 0.020833 0.000000 0.00 0.000000 0.00 \n",
"16 0.000000 0.000000 0.00 0.000000 0.00 \n",
"17 0.027397 0.013699 0.00 0.041096 0.00 \n",
"18 0.000000 0.000000 0.00 0.000000 0.00 \n",
"19 0.000000 0.000000 0.00 0.000000 0.00 \n",
"20 0.000000 0.000000 0.00 0.000000 0.00 \n",
"21 0.000000 0.000000 0.00 0.055556 0.00 \n",
"22 0.015385 0.000000 0.00 0.000000 0.00 \n",
"23 0.000000 0.000000 0.00 0.000000 0.00 \n",
"24 0.000000 0.000000 0.00 0.000000 0.00 \n",
"\n",
" Sports Bar Stadium Steakhouse Supermarket Tea Room Thai Restaurant \\\n",
"0 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 \n",
"1 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 \n",
"2 0.000000 0.000000 0.000000 0.000000 0.000000 0.01 \n",
"3 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 \n",
"4 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 \n",
"5 0.000000 0.027778 0.000000 0.013889 0.000000 0.00 \n",
"6 0.020000 0.000000 0.000000 0.010000 0.000000 0.00 \n",
"7 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 \n",
"8 0.010000 0.000000 0.000000 0.000000 0.000000 0.00 \n",
"9 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 \n",
"10 0.000000 0.010000 0.000000 0.000000 0.000000 0.00 \n",
"11 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 \n",
"12 0.000000 0.000000 0.000000 0.020000 0.000000 0.00 \n",
"13 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 \n",
"14 0.000000 0.000000 0.000000 0.000000 0.111111 0.00 \n",
"15 0.000000 0.000000 0.020833 0.000000 0.000000 0.00 \n",
"16 0.000000 0.041667 0.000000 0.000000 0.000000 0.00 \n",
"17 0.000000 0.013699 0.000000 0.000000 0.000000 0.00 \n",
"18 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 \n",
"19 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 \n",
"20 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 \n",
"21 0.000000 0.055556 0.000000 0.000000 0.000000 0.00 \n",
"22 0.015385 0.000000 0.000000 0.000000 0.015385 0.00 \n",
"23 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 \n",
"24 0.000000 0.100000 0.000000 0.000000 0.000000 0.00 \n",
"\n",
" Train Station Vegetarian / Vegan Restaurant Women's Store \n",
"0 0.000000 0.000000 0.000000 \n",
"1 0.047619 0.000000 0.000000 \n",
"2 0.000000 0.050000 0.000000 \n",
"3 0.142857 0.000000 0.000000 \n",
"4 0.000000 0.000000 0.000000 \n",
"5 0.000000 0.027778 0.000000 \n",
"6 0.000000 0.020000 0.000000 \n",
"7 0.000000 0.000000 0.000000 \n",
"8 0.000000 0.000000 0.000000 \n",
"9 0.000000 0.000000 0.000000 \n",
"10 0.000000 0.010000 0.000000 \n",
"11 0.000000 0.000000 0.000000 \n",
"12 0.000000 0.000000 0.000000 \n",
"13 0.000000 0.000000 0.000000 \n",
"14 0.000000 0.000000 0.000000 \n",
"15 0.000000 0.020833 0.000000 \n",
"16 0.000000 0.000000 0.000000 \n",
"17 0.000000 0.013699 0.013699 \n",
"18 0.000000 0.000000 0.000000 \n",
"19 0.000000 0.000000 0.000000 \n",
"20 0.058824 0.000000 0.000000 \n",
"21 0.055556 0.000000 0.000000 \n",
"22 0.000000 0.030769 0.000000 \n",
"23 0.000000 0.000000 0.000000 \n",
"24 0.000000 0.000000 0.000000 "
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hyd_grouped = hyd_data_onehot.groupby([\"Neighborhoods\"]).mean().reset_index()\n",
"\n",
"print(hyd_grouped.shape)\n",
"hyd_grouped"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" Lets now only grab Shopping Malls categories from all the neighborhoods"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"scrolled": true
},
"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>Neighborhoods</th>\n",
" <th>Shopping Mall</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Alwal</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Amberpet</td>\n",
" <td>0.047619</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Ameerpet</td>\n",
" <td>0.010000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Balanagar</td>\n",
" <td>0.142857</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Dilsukhnagar</td>\n",
" <td>0.076923</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Gachibowli</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>HITEC City</td>\n",
" <td>0.020000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>Hayathnagar</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>Jubilee Hills</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>Kapra</td>\n",
" <td>0.071429</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>Khairatabad</td>\n",
" <td>0.030000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>Kompally</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>Kukatpally</td>\n",
" <td>0.080000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>LB Nagar</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>Malkajgiri</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>Mehdipatnam</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>Musheerabad</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>Nampally</td>\n",
" <td>0.013699</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>Patancheru</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>Rajendranagar</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>Sanathnagar</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>Saroornagar</td>\n",
" <td>0.055556</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>Secunderabad</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>Serilingampally</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>Uppal</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Neighborhoods Shopping Mall\n",
"0 Alwal 0.000000\n",
"1 Amberpet 0.047619\n",
"2 Ameerpet 0.010000\n",
"3 Balanagar 0.142857\n",
"4 Dilsukhnagar 0.076923\n",
"5 Gachibowli 0.000000\n",
"6 HITEC City 0.020000\n",
"7 Hayathnagar 0.000000\n",
"8 Jubilee Hills 0.000000\n",
"9 Kapra 0.071429\n",
"10 Khairatabad 0.030000\n",
"11 Kompally 0.000000\n",
"12 Kukatpally 0.080000\n",
"13 LB Nagar 0.000000\n",
"14 Malkajgiri 0.000000\n",
"15 Mehdipatnam 0.000000\n",
"16 Musheerabad 0.000000\n",
"17 Nampally 0.013699\n",
"18 Patancheru 0.000000\n",
"19 Rajendranagar 0.000000\n",
"20 Sanathnagar 0.000000\n",
"21 Saroornagar 0.055556\n",
"22 Secunderabad 0.000000\n",
"23 Serilingampally 0.000000\n",
"24 Uppal 0.000000"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hyd_mall = hyd_grouped[[\"Neighborhoods\",\"Shopping Mall\"]]\n",
"hyd_mall"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f2ea73176a0>"
]
},
"execution_count": 26,
"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": [
"count, bin_edges = np.histogram(hyd_mall['Shopping Mall'])\n",
"hyd_mall.plot(kind='hist')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" If you can see most of the neighborhoods, almost half of them are lacking Shopping Malls and remaining neighborhoods are having malls moderately. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 5.3 Cluster the Neighborhoods by Venue Categories using K-means clustering"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" In this corner we are going to form clusters on Neighborhoods based on the Venues it has nearby using K-means clustering"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" Lets find the k value for K-means to form clusters using below function with the help of elbow method"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
"def get_inertia(n_clusters):\n",
" km = KMeans(n_clusters=n_clusters, init='k-means++', max_iter=15, random_state=8)\n",
" km.fit(X1)\n",
" return km.inertia_"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" Lets pass the venue categories as X value to the model"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
"X1 = hyd_grouped.drop([\"Neighborhoods\"], 1)"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0, 0.5, 'Error')"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"\n",
"scores = [get_inertia(x) for x in range(2, 25)]\n",
"plt.figure(figsize=[10, 8])\n",
"sns.lineplot(x=range(2, 25), y=scores)\n",
"plt.title(\"K vs Error\")\n",
"plt.xticks(range(2, 25))\n",
"plt.xlabel(\"K\")\n",
"plt.ylabel(\"Error\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" From above curve we can assume that k-value of 15 is the optimal one"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 4, 1, 1, 6, 14, 1, 1, 5, 1, 13], dtype=int32)"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"n_clusters1 = 15\n",
"hyd_venues_clustering = hyd_grouped.drop([\"Neighborhoods\"], 1)\n",
"kmeans1 = KMeans(n_clusters=n_clusters1, init='k-means++', max_iter=15, random_state=8).fit(X1)\n",
"kmeans1.labels_[0:10]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" Lets add cluster labels to a new copied data frame"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [],
"source": [
"hyd_venues_merged = hyd_grouped.copy()\n",
"hyd_venues_merged[\"Cluster_labels\"] = kmeans1.labels_"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" Add back our Neighborhood column to dataframe"
]
},
{
"cell_type": "code",
"execution_count": 48,
"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>Neighborhood</th>\n",
" <th>ATM</th>\n",
" <th>Afghan Restaurant</th>\n",
" <th>American Restaurant</th>\n",
" <th>Andhra Restaurant</th>\n",
" <th>Arcade</th>\n",
" <th>Arts &amp; Crafts Store</th>\n",
" <th>Asian Restaurant</th>\n",
" <th>Athletics &amp; Sports</th>\n",
" <th>BBQ Joint</th>\n",
" <th>Bakery</th>\n",
" <th>Bar</th>\n",
" <th>Bed &amp; Breakfast</th>\n",
" <th>Beer Garden</th>\n",
" <th>Bengali Restaurant</th>\n",
" <th>Bistro</th>\n",
" <th>Bookstore</th>\n",
" <th>Boutique</th>\n",
" <th>Bowling Alley</th>\n",
" <th>Breakfast Spot</th>\n",
" <th>Brewery</th>\n",
" <th>Bridal Shop</th>\n",
" <th>Burger Joint</th>\n",
" <th>Bus Station</th>\n",
" <th>Business Service</th>\n",
" <th>Butcher</th>\n",
" <th>Café</th>\n",
" <th>Chaat Place</th>\n",
" <th>Chinese Restaurant</th>\n",
" <th>Clothing Store</th>\n",
" <th>Cocktail Bar</th>\n",
" <th>Coffee Shop</th>\n",
" <th>Comfort Food Restaurant</th>\n",
" <th>Concert Hall</th>\n",
" <th>Convenience Store</th>\n",
" <th>Cricket Ground</th>\n",
" <th>Cupcake Shop</th>\n",
" <th>Dance Studio</th>\n",
" <th>Deli / Bodega</th>\n",
" <th>Department Store</th>\n",
" <th>Dessert Shop</th>\n",
" <th>Diner</th>\n",
" <th>Donut Shop</th>\n",
" <th>Dumpling Restaurant</th>\n",
" <th>Electronics Store</th>\n",
" <th>Falafel Restaurant</th>\n",
" <th>Farmers Market</th>\n",
" <th>Fast Food Restaurant</th>\n",
" <th>Flea Market</th>\n",
" <th>Food</th>\n",
" <th>Food &amp; Drink Shop</th>\n",
" <th>Food Court</th>\n",
" <th>Food Stand</th>\n",
" <th>Food Truck</th>\n",
" <th>Fried Chicken Joint</th>\n",
" <th>Frozen Yogurt Shop</th>\n",
" <th>Furniture / Home Store</th>\n",
" <th>Gaming Cafe</th>\n",
" <th>Garden</th>\n",
" <th>Gas Station</th>\n",
" <th>Gastropub</th>\n",
" <th>General Entertainment</th>\n",
" <th>Gift Shop</th>\n",
" <th>Golf Course</th>\n",
" <th>Grocery Store</th>\n",
" <th>Gym</th>\n",
" <th>Gym / Fitness Center</th>\n",
" <th>Harbor / Marina</th>\n",
" <th>Historic Site</th>\n",
" <th>Hookah Bar</th>\n",
" <th>Hotel</th>\n",
" <th>Hotel Bar</th>\n",
" <th>Hotel Pool</th>\n",
" <th>Hyderabadi Restaurant</th>\n",
" <th>Ice Cream Shop</th>\n",
" <th>Indian Restaurant</th>\n",
" <th>Indian Sweet Shop</th>\n",
" <th>Indie Movie Theater</th>\n",
" <th>Irish Pub</th>\n",
" <th>Italian Restaurant</th>\n",
" <th>Japanese Restaurant</th>\n",
" <th>Juice Bar</th>\n",
" <th>Lake</th>\n",
" <th>Laser Tag</th>\n",
" <th>Light Rail Station</th>\n",
" <th>Liquor Store</th>\n",
" <th>Lounge</th>\n",
" <th>Mattress Store</th>\n",
" <th>Mediterranean Restaurant</th>\n",
" <th>Metro Station</th>\n",
" <th>Mexican Restaurant</th>\n",
" <th>Middle Eastern Restaurant</th>\n",
" <th>Miscellaneous Shop</th>\n",
" <th>Mobile Phone Shop</th>\n",
" <th>Movie Theater</th>\n",
" <th>Multicuisine Indian Restaurant</th>\n",
" <th>Multiplex</th>\n",
" <th>New American Restaurant</th>\n",
" <th>Nightclub</th>\n",
" <th>North Indian Restaurant</th>\n",
" <th>Office</th>\n",
" <th>Park</th>\n",
" <th>Parsi Restaurant</th>\n",
" <th>Performing Arts Venue</th>\n",
" <th>Pizza Place</th>\n",
" <th>Platform</th>\n",
" <th>Pub</th>\n",
" <th>Rajasthani Restaurant</th>\n",
" <th>Restaurant</th>\n",
" <th>Sandwich Place</th>\n",
" <th>Scenic Lookout</th>\n",
" <th>Science Museum</th>\n",
" <th>Shipping Store</th>\n",
" <th>Shoe Store</th>\n",
" <th>Shop &amp; Service</th>\n",
" <th>Shopping Mall</th>\n",
" <th>Smoke Shop</th>\n",
" <th>Snack Place</th>\n",
" <th>Soccer Field</th>\n",
" <th>Social Club</th>\n",
" <th>South Indian Restaurant</th>\n",
" <th>Spa</th>\n",
" <th>Sports Bar</th>\n",
" <th>Stadium</th>\n",
" <th>Steakhouse</th>\n",
" <th>Supermarket</th>\n",
" <th>Tea Room</th>\n",
" <th>Thai Restaurant</th>\n",
" <th>Train Station</th>\n",
" <th>Vegetarian / Vegan Restaurant</th>\n",
" <th>Women's Store</th>\n",
" <th>Cluster_labels</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Alwal</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.25</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.250000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.25</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.25</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Amberpet</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.047619</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.095238</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.095238</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.047619</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.047619</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.047619</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.047619</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.047619</td>\n",
" <td>0.190476</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.047619</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.047619</td>\n",
" <td>0.047619</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.095238</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.047619</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.047619</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Ameerpet</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.01</td>\n",
" <td>0.0</td>\n",
" <td>0.01</td>\n",
" <td>0.030000</td>\n",
" <td>0.01</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.01</td>\n",
" <td>0.0</td>\n",
" <td>0.03</td>\n",
" <td>0.01</td>\n",
" <td>0.000000</td>\n",
" <td>0.02</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.040000</td>\n",
" <td>0.0</td>\n",
" <td>0.04</td>\n",
" <td>0.03</td>\n",
" <td>0.01</td>\n",
" <td>0.040000</td>\n",
" <td>0.0</td>\n",
" <td>0.01</td>\n",
" <td>0.010000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.02</td>\n",
" <td>0.020000</td>\n",
" <td>0.0</td>\n",
" <td>0.02</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.010000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.030000</td>\n",
" <td>0.0</td>\n",
" <td>0.01</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.01</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.02</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.01</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.020000</td>\n",
" <td>0.05</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.01</td>\n",
" <td>0.020000</td>\n",
" <td>0.190000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.01</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.01</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.020000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.01</td>\n",
" <td>0.040000</td>\n",
" <td>0.000000</td>\n",
" <td>0.01</td>\n",
" <td>0.01</td>\n",
" <td>0.010000</td>\n",
" <td>0.050000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.010000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.01</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.01</td>\n",
" <td>0.000000</td>\n",
" <td>0.05</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Balanagar</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.142857</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.285714</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.142857</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
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" <td>0.0</td>\n",
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" <td>0.00</td>\n",
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" <td>14</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Neighborhood ATM Afghan Restaurant American Restaurant \\\n",
"0 Alwal 0.0 0.0 0.0 \n",
"1 Amberpet 0.0 0.0 0.0 \n",
"2 Ameerpet 0.0 0.0 0.0 \n",
"3 Balanagar 0.0 0.0 0.0 \n",
"4 Dilsukhnagar 0.0 0.0 0.0 \n",
"\n",
" Andhra Restaurant Arcade Arts & Crafts Store Asian Restaurant \\\n",
"0 0.0 0.0 0.0 0.00 \n",
"1 0.0 0.0 0.0 0.00 \n",
"2 0.0 0.0 0.0 0.01 \n",
"3 0.0 0.0 0.0 0.00 \n",
"4 0.0 0.0 0.0 0.00 \n",
"\n",
" Athletics & Sports BBQ Joint Bakery Bar Bed & Breakfast \\\n",
"0 0.0 0.00 0.000000 0.00 0.0 \n",
"1 0.0 0.00 0.000000 0.00 0.0 \n",
"2 0.0 0.01 0.030000 0.01 0.0 \n",
"3 0.0 0.00 0.142857 0.00 0.0 \n",
"4 0.0 0.00 0.076923 0.00 0.0 \n",
"\n",
" Beer Garden Bengali Restaurant Bistro Bookstore Boutique \\\n",
"0 0.0 0.00 0.0 0.00 0.00 \n",
"1 0.0 0.00 0.0 0.00 0.00 \n",
"2 0.0 0.01 0.0 0.03 0.01 \n",
"3 0.0 0.00 0.0 0.00 0.00 \n",
"4 0.0 0.00 0.0 0.00 0.00 \n",
"\n",
" Bowling Alley Breakfast Spot Brewery Bridal Shop Burger Joint \\\n",
"0 0.000000 0.00 0.0 0.0 0.0 \n",
"1 0.000000 0.00 0.0 0.0 0.0 \n",
"2 0.000000 0.02 0.0 0.0 0.0 \n",
"3 0.000000 0.00 0.0 0.0 0.0 \n",
"4 0.076923 0.00 0.0 0.0 0.0 \n",
"\n",
" Bus Station Business Service Butcher Café Chaat Place \\\n",
"0 0.25 0.0 0.0 0.000000 0.0 \n",
"1 0.00 0.0 0.0 0.047619 0.0 \n",
"2 0.00 0.0 0.0 0.040000 0.0 \n",
"3 0.00 0.0 0.0 0.000000 0.0 \n",
"4 0.00 0.0 0.0 0.153846 0.0 \n",
"\n",
" Chinese Restaurant Clothing Store Cocktail Bar Coffee Shop \\\n",
"0 0.00 0.00 0.00 0.000000 \n",
"1 0.00 0.00 0.00 0.095238 \n",
"2 0.04 0.03 0.01 0.040000 \n",
"3 0.00 0.00 0.00 0.000000 \n",
"4 0.00 0.00 0.00 0.000000 \n",
"\n",
" Comfort Food Restaurant Concert Hall Convenience Store Cricket Ground \\\n",
"0 0.0 0.00 0.000000 0.0 \n",
"1 0.0 0.00 0.095238 0.0 \n",
"2 0.0 0.01 0.010000 0.0 \n",
"3 0.0 0.00 0.000000 0.0 \n",
"4 0.0 0.00 0.076923 0.0 \n",
"\n",
" Cupcake Shop Dance Studio Deli / Bodega Department Store Dessert Shop \\\n",
"0 0.0 0.0 0.00 0.250000 0.0 \n",
"1 0.0 0.0 0.00 0.000000 0.0 \n",
"2 0.0 0.0 0.02 0.020000 0.0 \n",
"3 0.0 0.0 0.00 0.000000 0.0 \n",
"4 0.0 0.0 0.00 0.076923 0.0 \n",
"\n",
" Diner Donut Shop Dumpling Restaurant Electronics Store \\\n",
"0 0.00 0.0 0.0 0.000000 \n",
"1 0.00 0.0 0.0 0.047619 \n",
"2 0.02 0.0 0.0 0.010000 \n",
"3 0.00 0.0 0.0 0.000000 \n",
"4 0.00 0.0 0.0 0.000000 \n",
"\n",
" Falafel Restaurant Farmers Market Fast Food Restaurant Flea Market \\\n",
"0 0.0 0.0 0.000000 0.0 \n",
"1 0.0 0.0 0.000000 0.0 \n",
"2 0.0 0.0 0.030000 0.0 \n",
"3 0.0 0.0 0.000000 0.0 \n",
"4 0.0 0.0 0.076923 0.0 \n",
"\n",
" Food Food & Drink Shop Food Court Food Stand Food Truck \\\n",
"0 0.00 0.000000 0.0 0.0 0.00 \n",
"1 0.00 0.047619 0.0 0.0 0.00 \n",
"2 0.01 0.000000 0.0 0.0 0.01 \n",
"3 0.00 0.000000 0.0 0.0 0.00 \n",
"4 0.00 0.000000 0.0 0.0 0.00 \n",
"\n",
" Fried Chicken Joint Frozen Yogurt Shop Furniture / Home Store \\\n",
"0 0.0 0.0 0.00 \n",
"1 0.0 0.0 0.00 \n",
"2 0.0 0.0 0.02 \n",
"3 0.0 0.0 0.00 \n",
"4 0.0 0.0 0.00 \n",
"\n",
" Gaming Cafe Garden Gas Station Gastropub General Entertainment \\\n",
"0 0.0 0.0 0.0 0.0 0.000000 \n",
"1 0.0 0.0 0.0 0.0 0.047619 \n",
"2 0.0 0.0 0.0 0.0 0.000000 \n",
"3 0.0 0.0 0.0 0.0 0.000000 \n",
"4 0.0 0.0 0.0 0.0 0.000000 \n",
"\n",
" Gift Shop Golf Course Grocery Store Gym Gym / Fitness Center \\\n",
"0 0.0 0.0 0.000000 0.25 0.0 \n",
"1 0.0 0.0 0.047619 0.00 0.0 \n",
"2 0.0 0.0 0.000000 0.01 0.0 \n",
"3 0.0 0.0 0.000000 0.00 0.0 \n",
"4 0.0 0.0 0.000000 0.00 0.0 \n",
"\n",
" Harbor / Marina Historic Site Hookah Bar Hotel Hotel Bar Hotel Pool \\\n",
"0 0.0 0.0 0.000000 0.00 0.0 0.0 \n",
"1 0.0 0.0 0.000000 0.00 0.0 0.0 \n",
"2 0.0 0.0 0.020000 0.05 0.0 0.0 \n",
"3 0.0 0.0 0.000000 0.00 0.0 0.0 \n",
"4 0.0 0.0 0.076923 0.00 0.0 0.0 \n",
"\n",
" Hyderabadi Restaurant Ice Cream Shop Indian Restaurant \\\n",
"0 0.00 0.000000 0.000000 \n",
"1 0.00 0.047619 0.190476 \n",
"2 0.01 0.020000 0.190000 \n",
"3 0.00 0.000000 0.000000 \n",
"4 0.00 0.000000 0.076923 \n",
"\n",
" Indian Sweet Shop Indie Movie Theater Irish Pub Italian Restaurant \\\n",
"0 0.0 0.000000 0.0 0.0 \n",
"1 0.0 0.000000 0.0 0.0 \n",
"2 0.0 0.000000 0.0 0.0 \n",
"3 0.0 0.285714 0.0 0.0 \n",
"4 0.0 0.000000 0.0 0.0 \n",
"\n",
" Japanese Restaurant Juice Bar Lake Laser Tag Light Rail Station \\\n",
"0 0.0 0.0 0.0 0.0 0.0 \n",
"1 0.0 0.0 0.0 0.0 0.0 \n",
"2 0.0 0.0 0.0 0.0 0.0 \n",
"3 0.0 0.0 0.0 0.0 0.0 \n",
"4 0.0 0.0 0.0 0.0 0.0 \n",
"\n",
" Liquor Store Lounge Mattress Store Mediterranean Restaurant \\\n",
"0 0.0 0.00 0.0 0.0 \n",
"1 0.0 0.00 0.0 0.0 \n",
"2 0.0 0.01 0.0 0.0 \n",
"3 0.0 0.00 0.0 0.0 \n",
"4 0.0 0.00 0.0 0.0 \n",
"\n",
" Metro Station Mexican Restaurant Middle Eastern Restaurant \\\n",
"0 0.0 0.0 0.00 \n",
"1 0.0 0.0 0.00 \n",
"2 0.0 0.0 0.01 \n",
"3 0.0 0.0 0.00 \n",
"4 0.0 0.0 0.00 \n",
"\n",
" Miscellaneous Shop Mobile Phone Shop Movie Theater \\\n",
"0 0.0 0.0 0.000000 \n",
"1 0.0 0.0 0.047619 \n",
"2 0.0 0.0 0.000000 \n",
"3 0.0 0.0 0.000000 \n",
"4 0.0 0.0 0.076923 \n",
"\n",
" Multicuisine Indian Restaurant Multiplex New American Restaurant \\\n",
"0 0.0 0.000000 0.0 \n",
"1 0.0 0.000000 0.0 \n",
"2 0.0 0.020000 0.0 \n",
"3 0.0 0.000000 0.0 \n",
"4 0.0 0.076923 0.0 \n",
"\n",
" Nightclub North Indian Restaurant Office Park Parsi Restaurant \\\n",
"0 0.0 0.0 0.0 0.25 0.0 \n",
"1 0.0 0.0 0.0 0.00 0.0 \n",
"2 0.0 0.0 0.0 0.00 0.0 \n",
"3 0.0 0.0 0.0 0.00 0.0 \n",
"4 0.0 0.0 0.0 0.00 0.0 \n",
"\n",
" Performing Arts Venue Pizza Place Platform Pub Rajasthani Restaurant \\\n",
"0 0.00 0.000000 0.000000 0.00 0.00 \n",
"1 0.00 0.047619 0.047619 0.00 0.00 \n",
"2 0.01 0.040000 0.000000 0.01 0.01 \n",
"3 0.00 0.142857 0.000000 0.00 0.00 \n",
"4 0.00 0.076923 0.000000 0.00 0.00 \n",
"\n",
" Restaurant Sandwich Place Scenic Lookout Science Museum Shipping Store \\\n",
"0 0.000000 0.000000 0.0 0.0 0.0 \n",
"1 0.000000 0.095238 0.0 0.0 0.0 \n",
"2 0.010000 0.050000 0.0 0.0 0.0 \n",
"3 0.142857 0.000000 0.0 0.0 0.0 \n",
"4 0.000000 0.000000 0.0 0.0 0.0 \n",
"\n",
" Shoe Store Shop & Service Shopping Mall Smoke Shop Snack Place \\\n",
"0 0.0 0.0 0.000000 0.0 0.0 \n",
"1 0.0 0.0 0.047619 0.0 0.0 \n",
"2 0.0 0.0 0.010000 0.0 0.0 \n",
"3 0.0 0.0 0.142857 0.0 0.0 \n",
"4 0.0 0.0 0.076923 0.0 0.0 \n",
"\n",
" Soccer Field Social Club South Indian Restaurant Spa Sports Bar \\\n",
"0 0.0 0.0 0.00 0.0 0.0 \n",
"1 0.0 0.0 0.00 0.0 0.0 \n",
"2 0.0 0.0 0.01 0.0 0.0 \n",
"3 0.0 0.0 0.00 0.0 0.0 \n",
"4 0.0 0.0 0.00 0.0 0.0 \n",
"\n",
" Stadium Steakhouse Supermarket Tea Room Thai Restaurant Train Station \\\n",
"0 0.0 0.0 0.0 0.0 0.00 0.000000 \n",
"1 0.0 0.0 0.0 0.0 0.00 0.047619 \n",
"2 0.0 0.0 0.0 0.0 0.01 0.000000 \n",
"3 0.0 0.0 0.0 0.0 0.00 0.142857 \n",
"4 0.0 0.0 0.0 0.0 0.00 0.000000 \n",
"\n",
" Vegetarian / Vegan Restaurant Women's Store Cluster_labels \n",
"0 0.00 0.0 4 \n",
"1 0.00 0.0 1 \n",
"2 0.05 0.0 1 \n",
"3 0.00 0.0 6 \n",
"4 0.00 0.0 14 "
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hyd_venues_merged.rename(columns={\"Neighborhoods\": \"Neighborhood\"}, inplace=True)\n",
"hyd_venues_merged.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" Add coordinates, Latitude and Longitude to the above dataframe"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(25, 134)\n"
]
},
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Neighborhood</th>\n",
" <th>ATM</th>\n",
" <th>Afghan Restaurant</th>\n",
" <th>American Restaurant</th>\n",
" <th>Andhra Restaurant</th>\n",
" <th>Arcade</th>\n",
" <th>Arts &amp; Crafts Store</th>\n",
" <th>Asian Restaurant</th>\n",
" <th>Athletics &amp; Sports</th>\n",
" <th>BBQ Joint</th>\n",
" <th>Bakery</th>\n",
" <th>Bar</th>\n",
" <th>Bed &amp; Breakfast</th>\n",
" <th>Beer Garden</th>\n",
" <th>Bengali Restaurant</th>\n",
" <th>Bistro</th>\n",
" <th>Bookstore</th>\n",
" <th>Boutique</th>\n",
" <th>Bowling Alley</th>\n",
" <th>Breakfast Spot</th>\n",
" <th>Brewery</th>\n",
" <th>Bridal Shop</th>\n",
" <th>Burger Joint</th>\n",
" <th>Bus Station</th>\n",
" <th>Business Service</th>\n",
" <th>Butcher</th>\n",
" <th>Café</th>\n",
" <th>Chaat Place</th>\n",
" <th>Chinese Restaurant</th>\n",
" <th>Clothing Store</th>\n",
" <th>Cocktail Bar</th>\n",
" <th>Coffee Shop</th>\n",
" <th>Comfort Food Restaurant</th>\n",
" <th>Concert Hall</th>\n",
" <th>Convenience Store</th>\n",
" <th>Cricket Ground</th>\n",
" <th>Cupcake Shop</th>\n",
" <th>Dance Studio</th>\n",
" <th>Deli / Bodega</th>\n",
" <th>Department Store</th>\n",
" <th>Dessert Shop</th>\n",
" <th>Diner</th>\n",
" <th>Donut Shop</th>\n",
" <th>Dumpling Restaurant</th>\n",
" <th>Electronics Store</th>\n",
" <th>Falafel Restaurant</th>\n",
" <th>Farmers Market</th>\n",
" <th>Fast Food Restaurant</th>\n",
" <th>Flea Market</th>\n",
" <th>Food</th>\n",
" <th>Food &amp; Drink Shop</th>\n",
" <th>Food Court</th>\n",
" <th>Food Stand</th>\n",
" <th>Food Truck</th>\n",
" <th>Fried Chicken Joint</th>\n",
" <th>Frozen Yogurt Shop</th>\n",
" <th>Furniture / Home Store</th>\n",
" <th>Gaming Cafe</th>\n",
" <th>Garden</th>\n",
" <th>Gas Station</th>\n",
" <th>Gastropub</th>\n",
" <th>General Entertainment</th>\n",
" <th>Gift Shop</th>\n",
" <th>Golf Course</th>\n",
" <th>Grocery Store</th>\n",
" <th>Gym</th>\n",
" <th>Gym / Fitness Center</th>\n",
" <th>Harbor / Marina</th>\n",
" <th>Historic Site</th>\n",
" <th>Hookah Bar</th>\n",
" <th>Hotel</th>\n",
" <th>Hotel Bar</th>\n",
" <th>Hotel Pool</th>\n",
" <th>Hyderabadi Restaurant</th>\n",
" <th>Ice Cream Shop</th>\n",
" <th>Indian Restaurant</th>\n",
" <th>Indian Sweet Shop</th>\n",
" <th>Indie Movie Theater</th>\n",
" <th>Irish Pub</th>\n",
" <th>Italian Restaurant</th>\n",
" <th>Japanese Restaurant</th>\n",
" <th>Juice Bar</th>\n",
" <th>Lake</th>\n",
" <th>Laser Tag</th>\n",
" <th>Light Rail Station</th>\n",
" <th>Liquor Store</th>\n",
" <th>Lounge</th>\n",
" <th>Mattress Store</th>\n",
" <th>Mediterranean Restaurant</th>\n",
" <th>Metro Station</th>\n",
" <th>Mexican Restaurant</th>\n",
" <th>Middle Eastern Restaurant</th>\n",
" <th>Miscellaneous Shop</th>\n",
" <th>Mobile Phone Shop</th>\n",
" <th>Movie Theater</th>\n",
" <th>Multicuisine Indian Restaurant</th>\n",
" <th>Multiplex</th>\n",
" <th>New American Restaurant</th>\n",
" <th>Nightclub</th>\n",
" <th>North Indian Restaurant</th>\n",
" <th>Office</th>\n",
" <th>Park</th>\n",
" <th>Parsi Restaurant</th>\n",
" <th>Performing Arts Venue</th>\n",
" <th>Pizza Place</th>\n",
" <th>Platform</th>\n",
" <th>Pub</th>\n",
" <th>Rajasthani Restaurant</th>\n",
" <th>Restaurant</th>\n",
" <th>Sandwich Place</th>\n",
" <th>Scenic Lookout</th>\n",
" <th>Science Museum</th>\n",
" <th>Shipping Store</th>\n",
" <th>Shoe Store</th>\n",
" <th>Shop &amp; Service</th>\n",
" <th>Shopping Mall</th>\n",
" <th>Smoke Shop</th>\n",
" <th>Snack Place</th>\n",
" <th>Soccer Field</th>\n",
" <th>Social Club</th>\n",
" <th>South Indian Restaurant</th>\n",
" <th>Spa</th>\n",
" <th>Sports Bar</th>\n",
" <th>Stadium</th>\n",
" <th>Steakhouse</th>\n",
" <th>Supermarket</th>\n",
" <th>Tea Room</th>\n",
" <th>Thai Restaurant</th>\n",
" <th>Train Station</th>\n",
" <th>Vegetarian / Vegan Restaurant</th>\n",
" <th>Women's Store</th>\n",
" <th>Cluster_labels</th>\n",
" <th>Latitude</th>\n",
" <th>Longitude</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Alwal</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.25</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.250000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.25</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.25</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>4</td>\n",
" <td>17.502229</td>\n",
" <td>78.508858</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Amberpet</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.047619</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.095238</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.095238</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.047619</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.047619</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.047619</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.047619</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.047619</td>\n",
" <td>0.190476</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.047619</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.047619</td>\n",
" <td>0.047619</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.095238</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.047619</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.047619</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" <td>17.390263</td>\n",
" <td>78.516481</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Ameerpet</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.01</td>\n",
" <td>0.0</td>\n",
" <td>0.01</td>\n",
" <td>0.030000</td>\n",
" <td>0.01</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.01</td>\n",
" <td>0.0</td>\n",
" <td>0.03</td>\n",
" <td>0.01</td>\n",
" <td>0.000000</td>\n",
" <td>0.02</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.040000</td>\n",
" <td>0.0</td>\n",
" <td>0.04</td>\n",
" <td>0.03</td>\n",
" <td>0.01</td>\n",
" <td>0.040000</td>\n",
" <td>0.0</td>\n",
" <td>0.01</td>\n",
" <td>0.010000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.02</td>\n",
" <td>0.020000</td>\n",
" <td>0.0</td>\n",
" <td>0.02</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.010000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.030000</td>\n",
" <td>0.0</td>\n",
" <td>0.01</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.01</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.02</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.01</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.020000</td>\n",
" <td>0.05</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.01</td>\n",
" <td>0.020000</td>\n",
" <td>0.190000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.01</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.01</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.020000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.01</td>\n",
" <td>0.040000</td>\n",
" <td>0.000000</td>\n",
" <td>0.01</td>\n",
" <td>0.01</td>\n",
" <td>0.010000</td>\n",
" <td>0.050000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.010000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.01</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.01</td>\n",
" <td>0.000000</td>\n",
" <td>0.05</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" <td>17.437501</td>\n",
" <td>78.448251</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Balanagar</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.142857</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.285714</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.142857</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.142857</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.142857</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.142857</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>6</td>\n",
" <td>17.476746</td>\n",
" <td>78.422108</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Dilsukhnagar</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.076923</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
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" </tr>\n",
" </tbody>\n",
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],
"text/plain": [
" Neighborhood ATM Afghan Restaurant American Restaurant \\\n",
"0 Alwal 0.0 0.0 0.0 \n",
"1 Amberpet 0.0 0.0 0.0 \n",
"2 Ameerpet 0.0 0.0 0.0 \n",
"3 Balanagar 0.0 0.0 0.0 \n",
"4 Dilsukhnagar 0.0 0.0 0.0 \n",
"\n",
" Andhra Restaurant Arcade Arts & Crafts Store Asian Restaurant \\\n",
"0 0.0 0.0 0.0 0.00 \n",
"1 0.0 0.0 0.0 0.00 \n",
"2 0.0 0.0 0.0 0.01 \n",
"3 0.0 0.0 0.0 0.00 \n",
"4 0.0 0.0 0.0 0.00 \n",
"\n",
" Athletics & Sports BBQ Joint Bakery Bar Bed & Breakfast \\\n",
"0 0.0 0.00 0.000000 0.00 0.0 \n",
"1 0.0 0.00 0.000000 0.00 0.0 \n",
"2 0.0 0.01 0.030000 0.01 0.0 \n",
"3 0.0 0.00 0.142857 0.00 0.0 \n",
"4 0.0 0.00 0.076923 0.00 0.0 \n",
"\n",
" Beer Garden Bengali Restaurant Bistro Bookstore Boutique \\\n",
"0 0.0 0.00 0.0 0.00 0.00 \n",
"1 0.0 0.00 0.0 0.00 0.00 \n",
"2 0.0 0.01 0.0 0.03 0.01 \n",
"3 0.0 0.00 0.0 0.00 0.00 \n",
"4 0.0 0.00 0.0 0.00 0.00 \n",
"\n",
" Bowling Alley Breakfast Spot Brewery Bridal Shop Burger Joint \\\n",
"0 0.000000 0.00 0.0 0.0 0.0 \n",
"1 0.000000 0.00 0.0 0.0 0.0 \n",
"2 0.000000 0.02 0.0 0.0 0.0 \n",
"3 0.000000 0.00 0.0 0.0 0.0 \n",
"4 0.076923 0.00 0.0 0.0 0.0 \n",
"\n",
" Bus Station Business Service Butcher Café Chaat Place \\\n",
"0 0.25 0.0 0.0 0.000000 0.0 \n",
"1 0.00 0.0 0.0 0.047619 0.0 \n",
"2 0.00 0.0 0.0 0.040000 0.0 \n",
"3 0.00 0.0 0.0 0.000000 0.0 \n",
"4 0.00 0.0 0.0 0.153846 0.0 \n",
"\n",
" Chinese Restaurant Clothing Store Cocktail Bar Coffee Shop \\\n",
"0 0.00 0.00 0.00 0.000000 \n",
"1 0.00 0.00 0.00 0.095238 \n",
"2 0.04 0.03 0.01 0.040000 \n",
"3 0.00 0.00 0.00 0.000000 \n",
"4 0.00 0.00 0.00 0.000000 \n",
"\n",
" Comfort Food Restaurant Concert Hall Convenience Store Cricket Ground \\\n",
"0 0.0 0.00 0.000000 0.0 \n",
"1 0.0 0.00 0.095238 0.0 \n",
"2 0.0 0.01 0.010000 0.0 \n",
"3 0.0 0.00 0.000000 0.0 \n",
"4 0.0 0.00 0.076923 0.0 \n",
"\n",
" Cupcake Shop Dance Studio Deli / Bodega Department Store Dessert Shop \\\n",
"0 0.0 0.0 0.00 0.250000 0.0 \n",
"1 0.0 0.0 0.00 0.000000 0.0 \n",
"2 0.0 0.0 0.02 0.020000 0.0 \n",
"3 0.0 0.0 0.00 0.000000 0.0 \n",
"4 0.0 0.0 0.00 0.076923 0.0 \n",
"\n",
" Diner Donut Shop Dumpling Restaurant Electronics Store \\\n",
"0 0.00 0.0 0.0 0.000000 \n",
"1 0.00 0.0 0.0 0.047619 \n",
"2 0.02 0.0 0.0 0.010000 \n",
"3 0.00 0.0 0.0 0.000000 \n",
"4 0.00 0.0 0.0 0.000000 \n",
"\n",
" Falafel Restaurant Farmers Market Fast Food Restaurant Flea Market \\\n",
"0 0.0 0.0 0.000000 0.0 \n",
"1 0.0 0.0 0.000000 0.0 \n",
"2 0.0 0.0 0.030000 0.0 \n",
"3 0.0 0.0 0.000000 0.0 \n",
"4 0.0 0.0 0.076923 0.0 \n",
"\n",
" Food Food & Drink Shop Food Court Food Stand Food Truck \\\n",
"0 0.00 0.000000 0.0 0.0 0.00 \n",
"1 0.00 0.047619 0.0 0.0 0.00 \n",
"2 0.01 0.000000 0.0 0.0 0.01 \n",
"3 0.00 0.000000 0.0 0.0 0.00 \n",
"4 0.00 0.000000 0.0 0.0 0.00 \n",
"\n",
" Fried Chicken Joint Frozen Yogurt Shop Furniture / Home Store \\\n",
"0 0.0 0.0 0.00 \n",
"1 0.0 0.0 0.00 \n",
"2 0.0 0.0 0.02 \n",
"3 0.0 0.0 0.00 \n",
"4 0.0 0.0 0.00 \n",
"\n",
" Gaming Cafe Garden Gas Station Gastropub General Entertainment \\\n",
"0 0.0 0.0 0.0 0.0 0.000000 \n",
"1 0.0 0.0 0.0 0.0 0.047619 \n",
"2 0.0 0.0 0.0 0.0 0.000000 \n",
"3 0.0 0.0 0.0 0.0 0.000000 \n",
"4 0.0 0.0 0.0 0.0 0.000000 \n",
"\n",
" Gift Shop Golf Course Grocery Store Gym Gym / Fitness Center \\\n",
"0 0.0 0.0 0.000000 0.25 0.0 \n",
"1 0.0 0.0 0.047619 0.00 0.0 \n",
"2 0.0 0.0 0.000000 0.01 0.0 \n",
"3 0.0 0.0 0.000000 0.00 0.0 \n",
"4 0.0 0.0 0.000000 0.00 0.0 \n",
"\n",
" Harbor / Marina Historic Site Hookah Bar Hotel Hotel Bar Hotel Pool \\\n",
"0 0.0 0.0 0.000000 0.00 0.0 0.0 \n",
"1 0.0 0.0 0.000000 0.00 0.0 0.0 \n",
"2 0.0 0.0 0.020000 0.05 0.0 0.0 \n",
"3 0.0 0.0 0.000000 0.00 0.0 0.0 \n",
"4 0.0 0.0 0.076923 0.00 0.0 0.0 \n",
"\n",
" Hyderabadi Restaurant Ice Cream Shop Indian Restaurant \\\n",
"0 0.00 0.000000 0.000000 \n",
"1 0.00 0.047619 0.190476 \n",
"2 0.01 0.020000 0.190000 \n",
"3 0.00 0.000000 0.000000 \n",
"4 0.00 0.000000 0.076923 \n",
"\n",
" Indian Sweet Shop Indie Movie Theater Irish Pub Italian Restaurant \\\n",
"0 0.0 0.000000 0.0 0.0 \n",
"1 0.0 0.000000 0.0 0.0 \n",
"2 0.0 0.000000 0.0 0.0 \n",
"3 0.0 0.285714 0.0 0.0 \n",
"4 0.0 0.000000 0.0 0.0 \n",
"\n",
" Japanese Restaurant Juice Bar Lake Laser Tag Light Rail Station \\\n",
"0 0.0 0.0 0.0 0.0 0.0 \n",
"1 0.0 0.0 0.0 0.0 0.0 \n",
"2 0.0 0.0 0.0 0.0 0.0 \n",
"3 0.0 0.0 0.0 0.0 0.0 \n",
"4 0.0 0.0 0.0 0.0 0.0 \n",
"\n",
" Liquor Store Lounge Mattress Store Mediterranean Restaurant \\\n",
"0 0.0 0.00 0.0 0.0 \n",
"1 0.0 0.00 0.0 0.0 \n",
"2 0.0 0.01 0.0 0.0 \n",
"3 0.0 0.00 0.0 0.0 \n",
"4 0.0 0.00 0.0 0.0 \n",
"\n",
" Metro Station Mexican Restaurant Middle Eastern Restaurant \\\n",
"0 0.0 0.0 0.00 \n",
"1 0.0 0.0 0.00 \n",
"2 0.0 0.0 0.01 \n",
"3 0.0 0.0 0.00 \n",
"4 0.0 0.0 0.00 \n",
"\n",
" Miscellaneous Shop Mobile Phone Shop Movie Theater \\\n",
"0 0.0 0.0 0.000000 \n",
"1 0.0 0.0 0.047619 \n",
"2 0.0 0.0 0.000000 \n",
"3 0.0 0.0 0.000000 \n",
"4 0.0 0.0 0.076923 \n",
"\n",
" Multicuisine Indian Restaurant Multiplex New American Restaurant \\\n",
"0 0.0 0.000000 0.0 \n",
"1 0.0 0.000000 0.0 \n",
"2 0.0 0.020000 0.0 \n",
"3 0.0 0.000000 0.0 \n",
"4 0.0 0.076923 0.0 \n",
"\n",
" Nightclub North Indian Restaurant Office Park Parsi Restaurant \\\n",
"0 0.0 0.0 0.0 0.25 0.0 \n",
"1 0.0 0.0 0.0 0.00 0.0 \n",
"2 0.0 0.0 0.0 0.00 0.0 \n",
"3 0.0 0.0 0.0 0.00 0.0 \n",
"4 0.0 0.0 0.0 0.00 0.0 \n",
"\n",
" Performing Arts Venue Pizza Place Platform Pub Rajasthani Restaurant \\\n",
"0 0.00 0.000000 0.000000 0.00 0.00 \n",
"1 0.00 0.047619 0.047619 0.00 0.00 \n",
"2 0.01 0.040000 0.000000 0.01 0.01 \n",
"3 0.00 0.142857 0.000000 0.00 0.00 \n",
"4 0.00 0.076923 0.000000 0.00 0.00 \n",
"\n",
" Restaurant Sandwich Place Scenic Lookout Science Museum Shipping Store \\\n",
"0 0.000000 0.000000 0.0 0.0 0.0 \n",
"1 0.000000 0.095238 0.0 0.0 0.0 \n",
"2 0.010000 0.050000 0.0 0.0 0.0 \n",
"3 0.142857 0.000000 0.0 0.0 0.0 \n",
"4 0.000000 0.000000 0.0 0.0 0.0 \n",
"\n",
" Shoe Store Shop & Service Shopping Mall Smoke Shop Snack Place \\\n",
"0 0.0 0.0 0.000000 0.0 0.0 \n",
"1 0.0 0.0 0.047619 0.0 0.0 \n",
"2 0.0 0.0 0.010000 0.0 0.0 \n",
"3 0.0 0.0 0.142857 0.0 0.0 \n",
"4 0.0 0.0 0.076923 0.0 0.0 \n",
"\n",
" Soccer Field Social Club South Indian Restaurant Spa Sports Bar \\\n",
"0 0.0 0.0 0.00 0.0 0.0 \n",
"1 0.0 0.0 0.00 0.0 0.0 \n",
"2 0.0 0.0 0.01 0.0 0.0 \n",
"3 0.0 0.0 0.00 0.0 0.0 \n",
"4 0.0 0.0 0.00 0.0 0.0 \n",
"\n",
" Stadium Steakhouse Supermarket Tea Room Thai Restaurant Train Station \\\n",
"0 0.0 0.0 0.0 0.0 0.00 0.000000 \n",
"1 0.0 0.0 0.0 0.0 0.00 0.047619 \n",
"2 0.0 0.0 0.0 0.0 0.01 0.000000 \n",
"3 0.0 0.0 0.0 0.0 0.00 0.142857 \n",
"4 0.0 0.0 0.0 0.0 0.00 0.000000 \n",
"\n",
" Vegetarian / Vegan Restaurant Women's Store Cluster_labels Latitude \\\n",
"0 0.00 0.0 4 17.502229 \n",
"1 0.00 0.0 1 17.390263 \n",
"2 0.05 0.0 1 17.437501 \n",
"3 0.00 0.0 6 17.476746 \n",
"4 0.00 0.0 14 17.368431 \n",
"\n",
" Longitude \n",
"0 78.508858 \n",
"1 78.516481 \n",
"2 78.448251 \n",
"3 78.422108 \n",
"4 78.523428 "
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hyd_venues_merged = hyd_venues_merged.join(hyd_df.set_index(\"Neighborhood\"), on=\"Neighborhood\")\n",
"print(hyd_venues_merged.shape)\n",
"hyd_venues_merged.head() # check the last columns!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" Sort above dataframe by cluster labels"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Neighborhood</th>\n",
" <th>ATM</th>\n",
" <th>Afghan Restaurant</th>\n",
" <th>American Restaurant</th>\n",
" <th>Andhra Restaurant</th>\n",
" <th>Arcade</th>\n",
" <th>Arts &amp; Crafts Store</th>\n",
" <th>Asian Restaurant</th>\n",
" <th>Athletics &amp; Sports</th>\n",
" <th>BBQ Joint</th>\n",
" <th>Bakery</th>\n",
" <th>Bar</th>\n",
" <th>Bed &amp; Breakfast</th>\n",
" <th>Beer Garden</th>\n",
" <th>Bengali Restaurant</th>\n",
" <th>Bistro</th>\n",
" <th>Bookstore</th>\n",
" <th>Boutique</th>\n",
" <th>Bowling Alley</th>\n",
" <th>Breakfast Spot</th>\n",
" <th>Brewery</th>\n",
" <th>Bridal Shop</th>\n",
" <th>Burger Joint</th>\n",
" <th>Bus Station</th>\n",
" <th>Business Service</th>\n",
" <th>Butcher</th>\n",
" <th>Café</th>\n",
" <th>Chaat Place</th>\n",
" <th>Chinese Restaurant</th>\n",
" <th>Clothing Store</th>\n",
" <th>Cocktail Bar</th>\n",
" <th>Coffee Shop</th>\n",
" <th>Comfort Food Restaurant</th>\n",
" <th>Concert Hall</th>\n",
" <th>Convenience Store</th>\n",
" <th>Cricket Ground</th>\n",
" <th>Cupcake Shop</th>\n",
" <th>Dance Studio</th>\n",
" <th>Deli / Bodega</th>\n",
" <th>Department Store</th>\n",
" <th>Dessert Shop</th>\n",
" <th>Diner</th>\n",
" <th>Donut Shop</th>\n",
" <th>Dumpling Restaurant</th>\n",
" <th>Electronics Store</th>\n",
" <th>Falafel Restaurant</th>\n",
" <th>Farmers Market</th>\n",
" <th>Fast Food Restaurant</th>\n",
" <th>Flea Market</th>\n",
" <th>Food</th>\n",
" <th>Food &amp; Drink Shop</th>\n",
" <th>Food Court</th>\n",
" <th>Food Stand</th>\n",
" <th>Food Truck</th>\n",
" <th>Fried Chicken Joint</th>\n",
" <th>Frozen Yogurt Shop</th>\n",
" <th>Furniture / Home Store</th>\n",
" <th>Gaming Cafe</th>\n",
" <th>Garden</th>\n",
" <th>Gas Station</th>\n",
" <th>Gastropub</th>\n",
" <th>General Entertainment</th>\n",
" <th>Gift Shop</th>\n",
" <th>Golf Course</th>\n",
" <th>Grocery Store</th>\n",
" <th>Gym</th>\n",
" <th>Gym / Fitness Center</th>\n",
" <th>Harbor / Marina</th>\n",
" <th>Historic Site</th>\n",
" <th>Hookah Bar</th>\n",
" <th>Hotel</th>\n",
" <th>Hotel Bar</th>\n",
" <th>Hotel Pool</th>\n",
" <th>Hyderabadi Restaurant</th>\n",
" <th>Ice Cream Shop</th>\n",
" <th>Indian Restaurant</th>\n",
" <th>Indian Sweet Shop</th>\n",
" <th>Indie Movie Theater</th>\n",
" <th>Irish Pub</th>\n",
" <th>Italian Restaurant</th>\n",
" <th>Japanese Restaurant</th>\n",
" <th>Juice Bar</th>\n",
" <th>Lake</th>\n",
" <th>Laser Tag</th>\n",
" <th>Light Rail Station</th>\n",
" <th>Liquor Store</th>\n",
" <th>Lounge</th>\n",
" <th>Mattress Store</th>\n",
" <th>Mediterranean Restaurant</th>\n",
" <th>Metro Station</th>\n",
" <th>Mexican Restaurant</th>\n",
" <th>Middle Eastern Restaurant</th>\n",
" <th>Miscellaneous Shop</th>\n",
" <th>Mobile Phone Shop</th>\n",
" <th>Movie Theater</th>\n",
" <th>Multicuisine Indian Restaurant</th>\n",
" <th>Multiplex</th>\n",
" <th>New American Restaurant</th>\n",
" <th>Nightclub</th>\n",
" <th>North Indian Restaurant</th>\n",
" <th>Office</th>\n",
" <th>Park</th>\n",
" <th>Parsi Restaurant</th>\n",
" <th>Performing Arts Venue</th>\n",
" <th>Pizza Place</th>\n",
" <th>Platform</th>\n",
" <th>Pub</th>\n",
" <th>Rajasthani Restaurant</th>\n",
" <th>Restaurant</th>\n",
" <th>Sandwich Place</th>\n",
" <th>Scenic Lookout</th>\n",
" <th>Science Museum</th>\n",
" <th>Shipping Store</th>\n",
" <th>Shoe Store</th>\n",
" <th>Shop &amp; Service</th>\n",
" <th>Shopping Mall</th>\n",
" <th>Smoke Shop</th>\n",
" <th>Snack Place</th>\n",
" <th>Soccer Field</th>\n",
" <th>Social Club</th>\n",
" <th>South Indian Restaurant</th>\n",
" <th>Spa</th>\n",
" <th>Sports Bar</th>\n",
" <th>Stadium</th>\n",
" <th>Steakhouse</th>\n",
" <th>Supermarket</th>\n",
" <th>Tea Room</th>\n",
" <th>Thai Restaurant</th>\n",
" <th>Train Station</th>\n",
" <th>Vegetarian / Vegan Restaurant</th>\n",
" <th>Women's Store</th>\n",
" <th>Cluster_labels</th>\n",
" <th>Latitude</th>\n",
" <th>Longitude</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>Serilingampally</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.142857</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
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" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.142857</td>\n",
" <td>0.142857</td>\n",
" <td>0.142857</td>\n",
" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.285714</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.142857</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0</td>\n",
" <td>17.465657</td>\n",
" <td>78.340672</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>Saroornagar</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.055556</td>\n",
" <td>0.000000</td>\n",
" <td>0.055556</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.055556</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.055556</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.055556</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.055556</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
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" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.055556</td>\n",
" <td>0.000000</td>\n",
" <td>0.166667</td>\n",
" <td>0.000000</td>\n",
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" <td>0.00</td>\n",
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" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.055556</td>\n",
" <td>0.00</td>\n",
" <td>0.055556</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.055556</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.055556</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.055556</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.055556</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.055556</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
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" <td>0.00</td>\n",
" <td>0.055556</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1</td>\n",
" <td>17.361165</td>\n",
" <td>78.538756</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Amberpet</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.047619</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.095238</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.095238</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.047619</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.047619</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.047619</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.047619</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.047619</td>\n",
" <td>0.190476</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.047619</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.047619</td>\n",
" <td>0.047619</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.095238</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.047619</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.047619</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1</td>\n",
" <td>17.390263</td>\n",
" <td>78.516481</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Ameerpet</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.010000</td>\n",
" <td>0.00</td>\n",
" <td>0.010000</td>\n",
" <td>0.030000</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.01</td>\n",
" <td>0.00</td>\n",
" <td>0.030000</td>\n",
" <td>0.01</td>\n",
" <td>0.000000</td>\n",
" <td>0.020000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.040000</td>\n",
" <td>0.000000</td>\n",
" <td>0.040000</td>\n",
" <td>0.030000</td>\n",
" <td>0.01</td>\n",
" <td>0.040000</td>\n",
" <td>0.00</td>\n",
" <td>0.01</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.02</td>\n",
" <td>0.020000</td>\n",
" <td>0.000000</td>\n",
" <td>0.020000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.030000</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.020000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.020000</td>\n",
" <td>0.050000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.020000</td>\n",
" <td>0.190000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.020000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.010000</td>\n",
" <td>0.040000</td>\n",
" <td>0.000000</td>\n",
" <td>0.01</td>\n",
" <td>0.01</td>\n",
" <td>0.010000</td>\n",
" <td>0.050000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.010000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.01</td>\n",
" <td>0.000000</td>\n",
" <td>0.050000</td>\n",
" <td>0.000000</td>\n",
" <td>1</td>\n",
" <td>17.437501</td>\n",
" <td>78.448251</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>Mehdipatnam</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.062500</td>\n",
" <td>0.00</td>\n",
" <td>0.020833</td>\n",
" <td>0.020833</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.020833</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.083333</td>\n",
" <td>0.000000</td>\n",
" <td>0.020833</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.020833</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.020833</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.041667</td>\n",
" <td>0.020833</td>\n",
" <td>0.000000</td>\n",
" <td>0.083333</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.020833</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.020833</td>\n",
" <td>0.000000</td>\n",
" <td>0.020833</td>\n",
" <td>0.000000</td>\n",
" <td>0.020833</td>\n",
" <td>0.041667</td>\n",
" <td>0.187500</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.020833</td>\n",
" <td>0.000000</td>\n",
" <td>0.041667</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.020833</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.041667</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.062500</td>\n",
" <td>0.020833</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.020833</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.020833</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.020833</td>\n",
" <td>0.000000</td>\n",
" <td>1</td>\n",
" <td>17.394263</td>\n",
" <td>78.434251</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>Secunderabad</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.030769</td>\n",
" <td>0.00</td>\n",
" <td>0.015385</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.076923</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.030769</td>\n",
" <td>0.000000</td>\n",
" <td>0.030769</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.061538</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.015385</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.015385</td>\n",
" <td>0.015385</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.030769</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.046154</td>\n",
" <td>0.030769</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.015385</td>\n",
" <td>0.000000</td>\n",
" <td>0.030769</td>\n",
" <td>0.00</td>\n",
" <td>0.015385</td>\n",
" <td>0.107692</td>\n",
" <td>0.015385</td>\n",
" <td>0.015385</td>\n",
" <td>0.015385</td>\n",
" <td>0.015385</td>\n",
" <td>0.123077</td>\n",
" <td>0.015385</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.015385</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.015385</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.015385</td>\n",
" <td>0.000000</td>\n",
" <td>0.015385</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.015385</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.030769</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.015385</td>\n",
" <td>0.015385</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.015385</td>\n",
" <td>0.015385</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.015385</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.015385</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.030769</td>\n",
" <td>0.000000</td>\n",
" <td>1</td>\n",
" <td>17.433725</td>\n",
" <td>78.500683</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Gachibowli</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.013889</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.013889</td>\n",
" <td>0.041667</td>\n",
" <td>0.013889</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.013889</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.013889</td>\n",
" <td>0.000000</td>\n",
" <td>0.013889</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.027778</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.041667</td>\n",
" <td>0.013889</td>\n",
" <td>0.000000</td>\n",
" <td>0.013889</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.027778</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.013889</td>\n",
" <td>0.00</td>\n",
" <td>0.013889</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.013889</td>\n",
" <td>0.000000</td>\n",
" <td>0.013889</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.013889</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.013889</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.041667</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.013889</td>\n",
" <td>0.055556</td>\n",
" <td>0.194444</td>\n",
" <td>0.013889</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.013889</td>\n",
" <td>0.013889</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.013889</td>\n",
" <td>0.013889</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.013889</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.055556</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.041667</td>\n",
" <td>0.055556</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.027778</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.027778</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.027778</td>\n",
" <td>0.000000</td>\n",
" <td>0.013889</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.027778</td>\n",
" <td>0.000000</td>\n",
" <td>1</td>\n",
" <td>17.443622</td>\n",
" <td>78.351964</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>HITEC City</td>\n",
" <td>0.000000</td>\n",
" <td>0.01</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.01</td>\n",
" <td>0.040000</td>\n",
" <td>0.01</td>\n",
" <td>0.020000</td>\n",
" <td>0.020000</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.01</td>\n",
" <td>0.00</td>\n",
" <td>0.01</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.010000</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.01</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.100000</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.01</td>\n",
" <td>0.040000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.020000</td>\n",
" <td>0.010000</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.01</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.040000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.020000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.01</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.050000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.130000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.030000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.01</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.020000</td>\n",
" <td>0.000000</td>\n",
" <td>0.01</td>\n",
" <td>0.000000</td>\n",
" <td>0.01</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.01</td>\n",
" <td>0.010000</td>\n",
" <td>0.00</td>\n",
" <td>0.01</td>\n",
" <td>0.00</td>\n",
" <td>0.01</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.020000</td>\n",
" <td>0.000000</td>\n",
" <td>0.01</td>\n",
" <td>0.00</td>\n",
" <td>0.060000</td>\n",
" <td>0.020000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.020000</td>\n",
" <td>0.000000</td>\n",
" <td>0.020000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.010000</td>\n",
" <td>0.00</td>\n",
" <td>0.020000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.020000</td>\n",
" <td>0.000000</td>\n",
" <td>1</td>\n",
" <td>17.449005</td>\n",
" <td>78.383138</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>Jubilee Hills</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.010000</td>\n",
" <td>0.00</td>\n",
" <td>0.020000</td>\n",
" <td>0.020000</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.01</td>\n",
" <td>0.00</td>\n",
" <td>0.01</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.010000</td>\n",
" <td>0.010000</td>\n",
" <td>0.040000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.110000</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.02</td>\n",
" <td>0.060000</td>\n",
" <td>0.01</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.01</td>\n",
" <td>0.01</td>\n",
" <td>0.01</td>\n",
" <td>0.000000</td>\n",
" <td>0.030000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.00</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.01</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.01</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.020000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.040000</td>\n",
" <td>0.030000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.040000</td>\n",
" <td>0.060000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.01</td>\n",
" <td>0.040000</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.050000</td>\n",
" <td>0.000000</td>\n",
" <td>0.01</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.03</td>\n",
" <td>0.000000</td>\n",
" <td>0.01</td>\n",
" <td>0.01</td>\n",
" <td>0.01</td>\n",
" <td>0.00</td>\n",
" <td>0.010000</td>\n",
" <td>0.01</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.020000</td>\n",
" <td>0.030000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.020000</td>\n",
" <td>0.000000</td>\n",
" <td>0.01</td>\n",
" <td>0.010000</td>\n",
" <td>0.02</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1</td>\n",
" <td>17.430836</td>\n",
" <td>78.410288</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>Nampally</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.054795</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.027397</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.013699</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.054795</td>\n",
" <td>0.013699</td>\n",
" <td>0.041096</td>\n",
" <td>0.013699</td>\n",
" <td>0.00</td>\n",
" <td>0.013699</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.013699</td>\n",
" <td>0.013699</td>\n",
" <td>0.013699</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.013699</td>\n",
" <td>0.027397</td>\n",
" <td>0.000000</td>\n",
" <td>0.013699</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.013699</td>\n",
" <td>0.013699</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.013699</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.082192</td>\n",
" <td>0.027397</td>\n",
" <td>0.000000</td>\n",
" <td>0.027397</td>\n",
" <td>0.054795</td>\n",
" <td>0.123288</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.027397</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.013699</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.013699</td>\n",
" <td>0.000000</td>\n",
" <td>0.013699</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.013699</td>\n",
" <td>0.000000</td>\n",
" <td>0.013699</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.027397</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.013699</td>\n",
" <td>0.0</td>\n",
" <td>0.013699</td>\n",
" <td>0.00</td>\n",
" <td>0.013699</td>\n",
" <td>0.013699</td>\n",
" <td>0.027397</td>\n",
" <td>0.013699</td>\n",
" <td>0.00</td>\n",
" <td>0.041096</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.013699</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.013699</td>\n",
" <td>0.013699</td>\n",
" <td>1</td>\n",
" <td>17.391852</td>\n",
" <td>78.466005</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>Khairatabad</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.01</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.010000</td>\n",
" <td>0.00</td>\n",
" <td>0.010000</td>\n",
" <td>0.020000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.01</td>\n",
" <td>0.030000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.060000</td>\n",
" <td>0.010000</td>\n",
" <td>0.030000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.040000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.020000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.060000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.01</td>\n",
" <td>0.000000</td>\n",
" <td>0.060000</td>\n",
" <td>0.030000</td>\n",
" <td>0.000000</td>\n",
" <td>0.020000</td>\n",
" <td>0.030000</td>\n",
" <td>0.100000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.020000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.090000</td>\n",
" <td>0.00</td>\n",
" <td>0.01</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.010000</td>\n",
" <td>0.00</td>\n",
" <td>0.020000</td>\n",
" <td>0.040000</td>\n",
" <td>0.000000</td>\n",
" <td>0.02</td>\n",
" <td>0.00</td>\n",
" <td>0.030000</td>\n",
" <td>0.030000</td>\n",
" <td>0.01</td>\n",
" <td>0.010000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.030000</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.010000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>1</td>\n",
" <td>17.411771</td>\n",
" <td>78.462200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>Patancheru</td>\n",
" <td>0.600000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.2</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.2</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>2</td>\n",
" <td>17.528609</td>\n",
" <td>78.267425</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>Rajendranagar</td>\n",
" <td>0.250000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.250000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.25</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.250000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>3</td>\n",
" <td>17.311091</td>\n",
" <td>78.442585</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Alwal</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.250000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.250000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.250000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.250000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>4</td>\n",
" <td>17.502229</td>\n",
" <td>78.508858</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>Hayathnagar</td>\n",
" <td>0.666667</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.333333</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>5</td>\n",
" <td>17.328115</td>\n",
" <td>78.604540</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Balanagar</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.142857</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.285714</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.142857</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.142857</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.142857</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.142857</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>6</td>\n",
" <td>17.476746</td>\n",
" <td>78.422108</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>Kompally</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.142857</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.142857</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.142857</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.285714</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.285714</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>7</td>\n",
" <td>17.544703</td>\n",
" <td>78.491842</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>Sanathnagar</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.058824</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.117647</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.058824</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.117647</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.058824</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.117647</td>\n",
" <td>0.000000</td>\n",
" <td>0.117647</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.058824</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.058824</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.058824</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.058824</td>\n",
" <td>0.058824</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.058824</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>8</td>\n",
" <td>17.456965</td>\n",
" <td>78.443478</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>Malkajgiri</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.111111</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.111111</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.111111</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.111111</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.111111</td>\n",
" <td>0.000000</td>\n",
" <td>0.111111</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.111111</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.111111</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.111111</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>9</td>\n",
" <td>17.448344</td>\n",
" <td>78.528973</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>Uppal</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.100000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.100000</td>\n",
" <td>0.100000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.100000</td>\n",
" <td>0.100000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.1</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.200000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.100000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.100000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>10</td>\n",
" <td>17.402509</td>\n",
" <td>78.561256</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>LB Nagar</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.083333</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.083333</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.083333</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.083333</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.083333</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.083333</td>\n",
" <td>0.000000</td>\n",
" <td>0.083333</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.083333</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.083333</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.166667</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.083333</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>11</td>\n",
" <td>17.350162</td>\n",
" <td>78.551094</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>Musheerabad</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.083333</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.041667</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.041667</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.041667</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.041667</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.041667</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.083333</td>\n",
" <td>0.041667</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.250000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.125000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.125000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.041667</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.041667</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>12</td>\n",
" <td>17.419142</td>\n",
" <td>78.498573</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>Kapra</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.071429</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.142857</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.071429</td>\n",
" <td>0.000000</td>\n",
" <td>0.071429</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.071429</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.142857</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.071429</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.142857</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.071429</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.071429</td>\n",
" <td>0.000000</td>\n",
" <td>0.071429</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>13</td>\n",
" <td>17.484636</td>\n",
" <td>78.561009</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>Kukatpally</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.02</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.02</td>\n",
" <td>0.000000</td>\n",
" <td>0.080000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.02</td>\n",
" <td>0.020000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.020000</td>\n",
" <td>0.00</td>\n",
" <td>0.020000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.160000</td>\n",
" <td>0.000000</td>\n",
" <td>0.020000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.02</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.020000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.020000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.060000</td>\n",
" <td>0.180000</td>\n",
" <td>0.000000</td>\n",
" <td>0.020000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.020000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.020000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.020000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.060000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.020000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.02</td>\n",
" <td>0.080000</td>\n",
" <td>0.000000</td>\n",
" <td>0.040000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.020000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>13</td>\n",
" <td>17.493084</td>\n",
" <td>78.405441</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Dilsukhnagar</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.076923</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.076923</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.153846</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.076923</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.076923</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.076923</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.076923</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.076923</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.076923</td>\n",
" <td>0.00</td>\n",
" <td>0.076923</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.076923</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.076923</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>14</td>\n",
" <td>17.368431</td>\n",
" <td>78.523428</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Neighborhood ATM Afghan Restaurant American Restaurant \\\n",
"23 Serilingampally 0.000000 0.00 0.00 \n",
"21 Saroornagar 0.000000 0.00 0.00 \n",
"1 Amberpet 0.000000 0.00 0.00 \n",
"2 Ameerpet 0.000000 0.00 0.00 \n",
"15 Mehdipatnam 0.000000 0.00 0.00 \n",
"22 Secunderabad 0.000000 0.00 0.00 \n",
"5 Gachibowli 0.000000 0.00 0.00 \n",
"6 HITEC City 0.000000 0.01 0.00 \n",
"8 Jubilee Hills 0.000000 0.00 0.00 \n",
"17 Nampally 0.000000 0.00 0.00 \n",
"10 Khairatabad 0.000000 0.00 0.01 \n",
"18 Patancheru 0.600000 0.00 0.00 \n",
"19 Rajendranagar 0.250000 0.00 0.00 \n",
"0 Alwal 0.000000 0.00 0.00 \n",
"7 Hayathnagar 0.666667 0.00 0.00 \n",
"3 Balanagar 0.000000 0.00 0.00 \n",
"11 Kompally 0.000000 0.00 0.00 \n",
"20 Sanathnagar 0.000000 0.00 0.00 \n",
"14 Malkajgiri 0.000000 0.00 0.00 \n",
"24 Uppal 0.000000 0.00 0.00 \n",
"13 LB Nagar 0.000000 0.00 0.00 \n",
"16 Musheerabad 0.000000 0.00 0.00 \n",
"9 Kapra 0.000000 0.00 0.00 \n",
"12 Kukatpally 0.000000 0.00 0.02 \n",
"4 Dilsukhnagar 0.000000 0.00 0.00 \n",
"\n",
" Andhra Restaurant Arcade Arts & Crafts Store Asian Restaurant \\\n",
"23 0.000000 0.142857 0.00 0.000000 \n",
"21 0.000000 0.000000 0.00 0.000000 \n",
"1 0.000000 0.000000 0.00 0.000000 \n",
"2 0.000000 0.000000 0.00 0.010000 \n",
"15 0.000000 0.000000 0.00 0.062500 \n",
"22 0.000000 0.030769 0.00 0.015385 \n",
"5 0.013889 0.000000 0.00 0.000000 \n",
"6 0.000000 0.000000 0.01 0.040000 \n",
"8 0.000000 0.000000 0.00 0.010000 \n",
"17 0.000000 0.000000 0.00 0.000000 \n",
"10 0.000000 0.000000 0.00 0.010000 \n",
"18 0.000000 0.000000 0.00 0.000000 \n",
"19 0.000000 0.000000 0.00 0.000000 \n",
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" Indian Sweet Shop Indie Movie Theater Irish Pub Italian Restaurant \\\n",
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" Japanese Restaurant Juice Bar Lake Laser Tag Light Rail Station \\\n",
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" Metro Station Mexican Restaurant Middle Eastern Restaurant \\\n",
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"\n",
" Miscellaneous Shop Mobile Phone Shop Movie Theater \\\n",
"23 0.000000 0.000000 0.000000 \n",
"21 0.000000 0.000000 0.055556 \n",
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"\n",
" Multicuisine Indian Restaurant Multiplex New American Restaurant \\\n",
"23 0.00 0.000000 0.00 \n",
"21 0.00 0.055556 0.00 \n",
"1 0.00 0.000000 0.00 \n",
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"24 0.00 0.100000 0.00 \n",
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"9 0.00 0.000000 0.00 \n",
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"\n",
" Nightclub North Indian Restaurant Office Park Parsi Restaurant \\\n",
"23 0.00 0.00 0.00 0.000000 0.00 \n",
"21 0.00 0.00 0.00 0.000000 0.00 \n",
"1 0.00 0.00 0.00 0.000000 0.00 \n",
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"18 0.00 0.00 0.00 0.000000 0.00 \n",
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"\n",
" Performing Arts Venue Pizza Place Platform Pub Rajasthani Restaurant \\\n",
"23 0.000000 0.285714 0.000000 0.00 0.00 \n",
"21 0.000000 0.055556 0.000000 0.00 0.00 \n",
"1 0.000000 0.047619 0.047619 0.00 0.00 \n",
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"\n",
" Restaurant Sandwich Place Scenic Lookout Science Museum \\\n",
"23 0.142857 0.000000 0.00 0.000000 \n",
"21 0.055556 0.000000 0.00 0.000000 \n",
"1 0.000000 0.095238 0.00 0.000000 \n",
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"15 0.062500 0.020833 0.00 0.000000 \n",
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"5 0.041667 0.055556 0.00 0.000000 \n",
"6 0.060000 0.020000 0.00 0.000000 \n",
"8 0.020000 0.030000 0.00 0.000000 \n",
"17 0.027397 0.000000 0.00 0.013699 \n",
"10 0.030000 0.030000 0.01 0.010000 \n",
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"\n",
" Shipping Store Shoe Store Shop & Service Shopping Mall Smoke Shop \\\n",
"23 0.0 0.000000 0.00 0.000000 0.000000 \n",
"21 0.0 0.000000 0.00 0.055556 0.000000 \n",
"1 0.0 0.000000 0.00 0.047619 0.000000 \n",
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"22 0.0 0.000000 0.00 0.000000 0.015385 \n",
"5 0.0 0.000000 0.00 0.000000 0.000000 \n",
"6 0.0 0.000000 0.00 0.020000 0.000000 \n",
"8 0.0 0.000000 0.00 0.000000 0.000000 \n",
"17 0.0 0.013699 0.00 0.013699 0.013699 \n",
"10 0.0 0.000000 0.00 0.030000 0.010000 \n",
"18 0.2 0.000000 0.00 0.000000 0.000000 \n",
"19 0.0 0.000000 0.00 0.000000 0.000000 \n",
"0 0.0 0.000000 0.00 0.000000 0.000000 \n",
"7 0.0 0.000000 0.00 0.000000 0.000000 \n",
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"11 0.0 0.000000 0.00 0.000000 0.000000 \n",
"20 0.0 0.000000 0.00 0.000000 0.000000 \n",
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"9 0.0 0.000000 0.00 0.071429 0.000000 \n",
"12 0.0 0.000000 0.02 0.080000 0.000000 \n",
"4 0.0 0.000000 0.00 0.076923 0.000000 \n",
"\n",
" Snack Place Soccer Field Social Club South Indian Restaurant Spa \\\n",
"23 0.000000 0.000000 0.00 0.000000 0.00 \n",
"21 0.000000 0.000000 0.00 0.055556 0.00 \n",
"1 0.000000 0.000000 0.00 0.000000 0.00 \n",
"2 0.000000 0.000000 0.00 0.010000 0.00 \n",
"15 0.020833 0.000000 0.00 0.000000 0.00 \n",
"22 0.015385 0.000000 0.00 0.000000 0.00 \n",
"5 0.027778 0.000000 0.00 0.027778 0.00 \n",
"6 0.020000 0.000000 0.00 0.010000 0.00 \n",
"8 0.020000 0.000000 0.01 0.010000 0.02 \n",
"17 0.027397 0.013699 0.00 0.041096 0.00 \n",
"10 0.000000 0.000000 0.00 0.010000 0.00 \n",
"18 0.000000 0.000000 0.00 0.000000 0.00 \n",
"19 0.000000 0.000000 0.00 0.000000 0.00 \n",
"0 0.000000 0.000000 0.00 0.000000 0.00 \n",
"7 0.000000 0.000000 0.00 0.000000 0.00 \n",
"3 0.000000 0.000000 0.00 0.000000 0.00 \n",
"11 0.000000 0.000000 0.00 0.000000 0.00 \n",
"20 0.000000 0.000000 0.00 0.000000 0.00 \n",
"14 0.000000 0.000000 0.00 0.000000 0.00 \n",
"24 0.000000 0.000000 0.00 0.000000 0.00 \n",
"13 0.000000 0.000000 0.00 0.083333 0.00 \n",
"16 0.000000 0.000000 0.00 0.000000 0.00 \n",
"9 0.071429 0.000000 0.00 0.000000 0.00 \n",
"12 0.040000 0.000000 0.00 0.000000 0.00 \n",
"4 0.000000 0.000000 0.00 0.000000 0.00 \n",
"\n",
" Sports Bar Stadium Steakhouse Supermarket Tea Room Thai Restaurant \\\n",
"23 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 \n",
"21 0.000000 0.055556 0.000000 0.000000 0.000000 0.00 \n",
"1 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 \n",
"2 0.000000 0.000000 0.000000 0.000000 0.000000 0.01 \n",
"15 0.000000 0.000000 0.020833 0.000000 0.000000 0.00 \n",
"22 0.015385 0.000000 0.000000 0.000000 0.015385 0.00 \n",
"5 0.000000 0.027778 0.000000 0.013889 0.000000 0.00 \n",
"6 0.020000 0.000000 0.000000 0.010000 0.000000 0.00 \n",
"8 0.010000 0.000000 0.000000 0.000000 0.000000 0.00 \n",
"17 0.000000 0.013699 0.000000 0.000000 0.000000 0.00 \n",
"10 0.000000 0.010000 0.000000 0.000000 0.000000 0.00 \n",
"18 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 \n",
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"20 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 \n",
"14 0.000000 0.000000 0.000000 0.000000 0.111111 0.00 \n",
"24 0.000000 0.100000 0.000000 0.000000 0.000000 0.00 \n",
"13 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 \n",
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"12 0.000000 0.000000 0.000000 0.020000 0.000000 0.00 \n",
"4 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 \n",
"\n",
" Train Station Vegetarian / Vegan Restaurant Women's Store \\\n",
"23 0.000000 0.000000 0.000000 \n",
"21 0.055556 0.000000 0.000000 \n",
"1 0.047619 0.000000 0.000000 \n",
"2 0.000000 0.050000 0.000000 \n",
"15 0.000000 0.020833 0.000000 \n",
"22 0.000000 0.030769 0.000000 \n",
"5 0.000000 0.027778 0.000000 \n",
"6 0.000000 0.020000 0.000000 \n",
"8 0.000000 0.000000 0.000000 \n",
"17 0.000000 0.013699 0.013699 \n",
"10 0.000000 0.010000 0.000000 \n",
"18 0.000000 0.000000 0.000000 \n",
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"20 0.058824 0.000000 0.000000 \n",
"14 0.000000 0.000000 0.000000 \n",
"24 0.000000 0.000000 0.000000 \n",
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"4 0.000000 0.000000 0.000000 \n",
"\n",
" Cluster_labels Latitude Longitude \n",
"23 0 17.465657 78.340672 \n",
"21 1 17.361165 78.538756 \n",
"1 1 17.390263 78.516481 \n",
"2 1 17.437501 78.448251 \n",
"15 1 17.394263 78.434251 \n",
"22 1 17.433725 78.500683 \n",
"5 1 17.443622 78.351964 \n",
"6 1 17.449005 78.383138 \n",
"8 1 17.430836 78.410288 \n",
"17 1 17.391852 78.466005 \n",
"10 1 17.411771 78.462200 \n",
"18 2 17.528609 78.267425 \n",
"19 3 17.311091 78.442585 \n",
"0 4 17.502229 78.508858 \n",
"7 5 17.328115 78.604540 \n",
"3 6 17.476746 78.422108 \n",
"11 7 17.544703 78.491842 \n",
"20 8 17.456965 78.443478 \n",
"14 9 17.448344 78.528973 \n",
"24 10 17.402509 78.561256 \n",
"13 11 17.350162 78.551094 \n",
"16 12 17.419142 78.498573 \n",
"9 13 17.484636 78.561009 \n",
"12 13 17.493084 78.405441 \n",
"4 14 17.368431 78.523428 "
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hyd_venues_merged.sort_values([\"Cluster_labels\"], inplace=True)\n",
"hyd_venues_merged"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" Create a map of hyderabad and add neighborhoods and cluster them based on venues it has"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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</script> onload=\"this.contentDocument.open();this.contentDocument.write(atob(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
],
"text/plain": [
"<folium.folium.Map at 0x7f2e9fe515f8>"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"venue_map_clusters = folium.Map(location=[hyd_location.latitude, hyd_location.longitude], zoom_start=11)\n",
"\n",
"# set color scheme for the clusters\n",
"x = np.arange(n_clusters1)\n",
"ys = [i+x+(i*x)**2 for i in range(n_clusters1)]\n",
"colors_array = cm.rainbow(np.linspace(0, 1, len(ys)))\n",
"rainbow = [colors.rgb2hex(i) for i in colors_array]\n",
"\n",
"# add markers to the map\n",
"markers_colors = []\n",
"for lat, lon, poi, cluster in zip(hyd_venues_merged['Latitude'], hyd_venues_merged['Longitude'], hyd_venues_merged['Neighborhood'], hyd_venues_merged['Cluster_labels']):\n",
" label = folium.Popup(str(poi) + ' - Cluster ' + str(cluster), parse_html=True)\n",
" folium.CircleMarker(\n",
" [lat, lon],\n",
" radius=5,\n",
" popup=label,\n",
" color=rainbow[cluster-1],\n",
" fill=True,\n",
" fill_color=rainbow[cluster-1],\n",
" fill_opacity=0.7).add_to(venue_map_clusters)\n",
"venue_map_clusters"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 5.4 Cluster the Neighborhoods by Shopping Malls using K-means clustering"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" Lets cluster the above Shopping malls dataframe using K-means clustering, first we will have to find the number of clusters to be formed such that it should be an optimal one, for that we are going to use elbow curve lineplot with x-axis as k and y-axis as error"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [],
"source": [
"def get_inertia(n_clusters):\n",
" km = KMeans(n_clusters=n_clusters, init='k-means++', max_iter=15, random_state=8)\n",
" km.fit(X2)\n",
" return km.inertia_"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" We are gonna use above function to get the error score for each k value"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [],
"source": [
"X2 = hyd_mall.drop([\"Neighborhoods\"], 1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" We are going to pass X2 value as hyd_mall['Shopping Mall']"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/jupyterlab/conda/envs/python/lib/python3.6/site-packages/sklearn/cluster/k_means_.py:971: ConvergenceWarning: Number of distinct clusters (11) found smaller than n_clusters (12). Possibly due to duplicate points in X.\n",
" return_n_iter=True)\n",
"/home/jupyterlab/conda/envs/python/lib/python3.6/site-packages/sklearn/cluster/k_means_.py:971: ConvergenceWarning: Number of distinct clusters (11) found smaller than n_clusters (13). Possibly due to duplicate points in X.\n",
" return_n_iter=True)\n",
"/home/jupyterlab/conda/envs/python/lib/python3.6/site-packages/sklearn/cluster/k_means_.py:971: ConvergenceWarning: Number of distinct clusters (11) found smaller than n_clusters (14). Possibly due to duplicate points in X.\n",
" return_n_iter=True)\n",
"/home/jupyterlab/conda/envs/python/lib/python3.6/site-packages/sklearn/cluster/k_means_.py:971: ConvergenceWarning: Number of distinct clusters (11) found smaller than n_clusters (15). Possibly due to duplicate points in X.\n",
" return_n_iter=True)\n",
"/home/jupyterlab/conda/envs/python/lib/python3.6/site-packages/sklearn/cluster/k_means_.py:971: ConvergenceWarning: Number of distinct clusters (11) found smaller than n_clusters (16). Possibly due to duplicate points in X.\n",
" return_n_iter=True)\n",
"/home/jupyterlab/conda/envs/python/lib/python3.6/site-packages/sklearn/cluster/k_means_.py:971: ConvergenceWarning: Number of distinct clusters (11) found smaller than n_clusters (17). Possibly due to duplicate points in X.\n",
" return_n_iter=True)\n",
"/home/jupyterlab/conda/envs/python/lib/python3.6/site-packages/sklearn/cluster/k_means_.py:971: ConvergenceWarning: Number of distinct clusters (11) found smaller than n_clusters (18). Possibly due to duplicate points in X.\n",
" return_n_iter=True)\n",
"/home/jupyterlab/conda/envs/python/lib/python3.6/site-packages/sklearn/cluster/k_means_.py:971: ConvergenceWarning: Number of distinct clusters (11) found smaller than n_clusters (19). Possibly due to duplicate points in X.\n",
" return_n_iter=True)\n",
"/home/jupyterlab/conda/envs/python/lib/python3.6/site-packages/sklearn/cluster/k_means_.py:971: ConvergenceWarning: Number of distinct clusters (11) found smaller than n_clusters (20). Possibly due to duplicate points in X.\n",
" return_n_iter=True)\n",
"/home/jupyterlab/conda/envs/python/lib/python3.6/site-packages/sklearn/cluster/k_means_.py:971: ConvergenceWarning: Number of distinct clusters (11) found smaller than n_clusters (21). Possibly due to duplicate points in X.\n",
" return_n_iter=True)\n",
"/home/jupyterlab/conda/envs/python/lib/python3.6/site-packages/sklearn/cluster/k_means_.py:971: ConvergenceWarning: Number of distinct clusters (11) found smaller than n_clusters (22). Possibly due to duplicate points in X.\n",
" return_n_iter=True)\n",
"/home/jupyterlab/conda/envs/python/lib/python3.6/site-packages/sklearn/cluster/k_means_.py:971: ConvergenceWarning: Number of distinct clusters (11) found smaller than n_clusters (23). Possibly due to duplicate points in X.\n",
" return_n_iter=True)\n",
"/home/jupyterlab/conda/envs/python/lib/python3.6/site-packages/sklearn/cluster/k_means_.py:971: ConvergenceWarning: Number of distinct clusters (11) found smaller than n_clusters (24). Possibly due to duplicate points in X.\n",
" return_n_iter=True)\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"\n",
"scores = [get_inertia(x) for x in range(2, 25)]\n",
"plt.figure(figsize=[10, 8])\n",
"sns.lineplot(x=range(2, 25), y=scores)\n",
"plt.title(\"K vs Error\")\n",
"plt.xticks(range(2, 25))\n",
"plt.xlabel(\"K\")\n",
"plt.ylabel(\"Error\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" From the above elbow graph we can use k value for k-means as 5 as the optimal k"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 4, 1, 0, 2, 1, 3, 1, 1, 2], dtype=int32)"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"n_clusters2 = 5\n",
"hyd_mall_clustering = hyd_mall.drop([\"Neighborhoods\"], 1)\n",
"kmeans2 = KMeans(n_clusters=n_clusters2, init='k-means++', max_iter=15, random_state=8).fit(X2)\n",
"kmeans2.labels_[0:10]"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [],
"source": [
"hyd_mall_merged = hyd_mall.copy()\n",
"hyd_mall_merged[\"Cluster_labels\"] = kmeans2.labels_"
]
},
{
"cell_type": "code",
"execution_count": 60,
"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>Neighborhood</th>\n",
" <th>Shopping Mall</th>\n",
" <th>Cluster_labels</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Alwal</td>\n",
" <td>0.000000</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Amberpet</td>\n",
" <td>0.047619</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Ameerpet</td>\n",
" <td>0.010000</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Balanagar</td>\n",
" <td>0.142857</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Dilsukhnagar</td>\n",
" <td>0.076923</td>\n",
" <td>2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Neighborhood Shopping Mall Cluster_labels\n",
"0 Alwal 0.000000 1\n",
"1 Amberpet 0.047619 4\n",
"2 Ameerpet 0.010000 1\n",
"3 Balanagar 0.142857 0\n",
"4 Dilsukhnagar 0.076923 2"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hyd_mall_merged.rename(columns={\"Neighborhoods\": \"Neighborhood\"}, inplace=True)\n",
"hyd_mall_merged.head()"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(25, 5)\n"
]
},
{
"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>Neighborhood</th>\n",
" <th>Shopping Mall</th>\n",
" <th>Cluster_labels</th>\n",
" <th>Latitude</th>\n",
" <th>Longitude</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Alwal</td>\n",
" <td>0.000000</td>\n",
" <td>1</td>\n",
" <td>17.502229</td>\n",
" <td>78.508858</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Amberpet</td>\n",
" <td>0.047619</td>\n",
" <td>4</td>\n",
" <td>17.390263</td>\n",
" <td>78.516481</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Ameerpet</td>\n",
" <td>0.010000</td>\n",
" <td>1</td>\n",
" <td>17.437501</td>\n",
" <td>78.448251</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Balanagar</td>\n",
" <td>0.142857</td>\n",
" <td>0</td>\n",
" <td>17.476746</td>\n",
" <td>78.422108</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Dilsukhnagar</td>\n",
" <td>0.076923</td>\n",
" <td>2</td>\n",
" <td>17.368431</td>\n",
" <td>78.523428</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Neighborhood Shopping Mall Cluster_labels Latitude Longitude\n",
"0 Alwal 0.000000 1 17.502229 78.508858\n",
"1 Amberpet 0.047619 4 17.390263 78.516481\n",
"2 Ameerpet 0.010000 1 17.437501 78.448251\n",
"3 Balanagar 0.142857 0 17.476746 78.422108\n",
"4 Dilsukhnagar 0.076923 2 17.368431 78.523428"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hyd_mall_merged = hyd_mall_merged.join(hyd_df.set_index(\"Neighborhood\"), on=\"Neighborhood\")\n",
"print(hyd_mall_merged.shape)\n",
"hyd_mall_merged.head() # check the last columns!"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"scrolled": true
},
"outputs": [
{
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" Neighborhood Shopping Mall Cluster_labels Latitude Longitude\n",
"3 Balanagar 0.142857 0 17.476746 78.422108\n",
"0 Alwal 0.000000 1 17.502229 78.508858\n",
"22 Secunderabad 0.000000 1 17.433725 78.500683\n",
"20 Sanathnagar 0.000000 1 17.456965 78.443478\n",
"19 Rajendranagar 0.000000 1 17.311091 78.442585\n",
"18 Patancheru 0.000000 1 17.528609 78.267425\n",
"16 Musheerabad 0.000000 1 17.419142 78.498573\n",
"15 Mehdipatnam 0.000000 1 17.394263 78.434251\n",
"14 Malkajgiri 0.000000 1 17.448344 78.528973\n",
"13 LB Nagar 0.000000 1 17.350162 78.551094\n",
"23 Serilingampally 0.000000 1 17.465657 78.340672\n",
"24 Uppal 0.000000 1 17.402509 78.561256\n",
"8 Jubilee Hills 0.000000 1 17.430836 78.410288\n",
"7 Hayathnagar 0.000000 1 17.328115 78.604540\n",
"5 Gachibowli 0.000000 1 17.443622 78.351964\n",
"2 Ameerpet 0.010000 1 17.437501 78.448251\n",
"11 Kompally 0.000000 1 17.544703 78.491842\n",
"9 Kapra 0.071429 2 17.484636 78.561009\n",
"4 Dilsukhnagar 0.076923 2 17.368431 78.523428\n",
"12 Kukatpally 0.080000 2 17.493084 78.405441\n",
"10 Khairatabad 0.030000 3 17.411771 78.462200\n",
"6 HITEC City 0.020000 3 17.449005 78.383138\n",
"17 Nampally 0.013699 3 17.391852 78.466005\n",
"21 Saroornagar 0.055556 4 17.361165 78.538756\n",
"1 Amberpet 0.047619 4 17.390263 78.516481"
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            var popup_51d46f658d274e7ca533202996c95c91 = L.popup({maxWidth: '300'});

            
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                popup_51d46f658d274e7ca533202996c95c91.setContent(html_66a320e6ac334272aaf4c15a4d078531);
            

            circle_marker_35b15d6e0d9545a9af4d5c4f05587bc9.bindPopup(popup_51d46f658d274e7ca533202996c95c91);

            
        
    
            var circle_marker_da395cab84214bd3abe045098fd42710 = L.circleMarker(
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                {
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                ).addTo(map_969c79fbd9ce42d6993482109e7e4a0d);
            
    
            var popup_d6ffd053fe6640609da33a59d32c0056 = L.popup({maxWidth: '300'});

            
                var html_6b8ce53a7d9643c08edbcc09f2fee8cb = $('<div id="html_6b8ce53a7d9643c08edbcc09f2fee8cb" style="width: 100.0%; height: 100.0%;">Malkajgiri - Cluster 1</div>')[0];
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            circle_marker_da395cab84214bd3abe045098fd42710.bindPopup(popup_d6ffd053fe6640609da33a59d32c0056);

            
        
    
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                {
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                ).addTo(map_969c79fbd9ce42d6993482109e7e4a0d);
            
    
            var popup_422c9462e73442fba6511084fe9817ad = L.popup({maxWidth: '300'});

            
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            circle_marker_c9319b352c3a44348e3124c9c5a815f0.bindPopup(popup_422c9462e73442fba6511084fe9817ad);

            
        
    
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}
                ).addTo(map_969c79fbd9ce42d6993482109e7e4a0d);
            
    
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                var html_fcfa3a719d1e485baec5b53f08387ea1 = $('<div id="html_fcfa3a719d1e485baec5b53f08387ea1" style="width: 100.0%; height: 100.0%;">Serilingampally - Cluster 1</div>')[0];
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            circle_marker_217da21f00504cb0b912c8f259df209e.bindPopup(popup_3d2adafdf7f24e5bace43b6d78dfdf44);

            
        
    
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                {
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                ).addTo(map_969c79fbd9ce42d6993482109e7e4a0d);
            
    
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            circle_marker_0d1e091a422e4666b1634ba15d1a08de.bindPopup(popup_b66d8e9eff614fc5bb90defd575fef33);

            
        
    
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                ).addTo(map_969c79fbd9ce42d6993482109e7e4a0d);
            
    
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                var html_02d5e4a3458f4bfcb9d8c36376b232dc = $('<div id="html_02d5e4a3458f4bfcb9d8c36376b232dc" style="width: 100.0%; height: 100.0%;">Jubilee Hills - Cluster 1</div>')[0];
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            circle_marker_3e253f2d85114fcc939f9a0f5a564b67.bindPopup(popup_9f6e0f73cdcd4f829e46786f9ba217f1);

            
        
    
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}
                ).addTo(map_969c79fbd9ce42d6993482109e7e4a0d);
            
    
            var popup_7084017bdf8d47efa162c1462c2793c2 = L.popup({maxWidth: '300'});

            
                var html_72f7296fbede4b63963e6c4ffb8aec17 = $('<div id="html_72f7296fbede4b63963e6c4ffb8aec17" style="width: 100.0%; height: 100.0%;">Hayathnagar - Cluster 1</div>')[0];
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            circle_marker_4d9275e31eee48c79fedd58093ccd416.bindPopup(popup_7084017bdf8d47efa162c1462c2793c2);

            
        
    
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}
                ).addTo(map_969c79fbd9ce42d6993482109e7e4a0d);
            
    
            var popup_38b52eb0cb0547e99178ce1bd39ef4ff = L.popup({maxWidth: '300'});

            
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            circle_marker_9f39f0359de94db8b97a03d1a7292312.bindPopup(popup_38b52eb0cb0547e99178ce1bd39ef4ff);

            
        
    
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}
                ).addTo(map_969c79fbd9ce42d6993482109e7e4a0d);
            
    
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}
                ).addTo(map_969c79fbd9ce42d6993482109e7e4a0d);
            
    
            var popup_dbe470c83caf4274ac3c1958055d1d96 = L.popup({maxWidth: '300'});

            
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}
                ).addTo(map_969c79fbd9ce42d6993482109e7e4a0d);
            
    
            var popup_466c05145ffa483aa75c09aa94041ba5 = L.popup({maxWidth: '300'});

            
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                popup_466c05145ffa483aa75c09aa94041ba5.setContent(html_912bee46ced445109b152c7e7b349811);
            

            circle_marker_1aa23e0531f2463ba42b4d489fef01b3.bindPopup(popup_466c05145ffa483aa75c09aa94041ba5);

            
        
    
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}
                ).addTo(map_969c79fbd9ce42d6993482109e7e4a0d);
            
    
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}
                ).addTo(map_969c79fbd9ce42d6993482109e7e4a0d);
            
    
            var popup_d314488ef0394744aa33495c0617c08a = L.popup({maxWidth: '300'});

            
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                popup_d314488ef0394744aa33495c0617c08a.setContent(html_75083a677138465e8fd3689defa5b60d);
            

            circle_marker_05f175bbbb854bf88f2e653fc70a52e9.bindPopup(popup_d314488ef0394744aa33495c0617c08a);

            
        
    
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}
                ).addTo(map_969c79fbd9ce42d6993482109e7e4a0d);
            
    
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                popup_a262a2fa82a94a4a8080799eb7e6ad8e.setContent(html_d0be57b97e0a44f8a20a6bea9040a0f4);
            

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}
                ).addTo(map_969c79fbd9ce42d6993482109e7e4a0d);
            
    
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}
                ).addTo(map_969c79fbd9ce42d6993482109e7e4a0d);
            
    
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                popup_05f10e9fa2d546c5bc57a30674f565ca.setContent(html_6ee02382e5c04c27b5aa80b638472146);
            

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}
                ).addTo(map_969c79fbd9ce42d6993482109e7e4a0d);
            
    
            var popup_acd1067f9d004104a4eaaef39a1bccbe = L.popup({maxWidth: '300'});

            
                var html_98f5c88cd9584b8893d04164e8b393d3 = $('<div id="html_98f5c88cd9584b8893d04164e8b393d3" style="width: 100.0%; height: 100.0%;">Saroornagar - Cluster 4</div>')[0];
                popup_acd1067f9d004104a4eaaef39a1bccbe.setContent(html_98f5c88cd9584b8893d04164e8b393d3);
            

            circle_marker_96b2ab6445b84541bddda74f757ed9e9.bindPopup(popup_acd1067f9d004104a4eaaef39a1bccbe);

            
        
    
            var circle_marker_9b258deb81464fa4842e31714f12d297 = L.circleMarker(
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                ).addTo(map_969c79fbd9ce42d6993482109e7e4a0d);
            
    
            var popup_efef9914bdd942658bdec9dcee30718a = L.popup({maxWidth: '300'});

            
                var html_d3e1845ce358455e866a049858a64792 = $('<div id="html_d3e1845ce358455e866a049858a64792" style="width: 100.0%; height: 100.0%;">Amberpet - Cluster 4</div>')[0];
                popup_efef9914bdd942658bdec9dcee30718a.setContent(html_d3e1845ce358455e866a049858a64792);
            

            circle_marker_9b258deb81464fa4842e31714f12d297.bindPopup(popup_efef9914bdd942658bdec9dcee30718a);

            
        
</script> onload=\"this.contentDocument.open();this.contentDocument.write(atob(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
],
"text/plain": [
"<folium.folium.Map at 0x7f2e9fc87320>"
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"map_mall_clusters = folium.Map(location=[hyd_location.latitude, hyd_location.longitude], zoom_start=11)\n",
"\n",
"# set color scheme for the clusters\n",
"x = np.arange(n_clusters2)\n",
"ys = [i+x+(i*x)**2 for i in range(n_clusters2)]\n",
"colors_array = cm.rainbow(np.linspace(0, 1, len(ys)))\n",
"rainbow = [colors.rgb2hex(i) for i in colors_array]\n",
"\n",
"# add markers to the map\n",
"markers_colors = []\n",
"for lat, lon, poi, cluster in zip(hyd_mall_merged['Latitude'], hyd_mall_merged['Longitude'], hyd_mall_merged['Neighborhood'], hyd_mall_merged['Cluster_labels']):\n",
" label = folium.Popup(str(poi) + ' - Cluster ' + str(cluster), parse_html=True)\n",
" folium.CircleMarker(\n",
" [lat, lon],\n",
" radius=5,\n",
" popup=label,\n",
" color=rainbow[cluster-1],\n",
" fill=True,\n",
" fill_color=rainbow[cluster-1],\n",
" fill_opacity=0.7).add_to(map_mall_clusters)\n",
"map_mall_clusters"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" There are 5 clusters out of which we will examine each cluster in the next session, lets save this map as html"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [],
"source": [
"map_mall_clusters.save('map_mall_clusters.html')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 5.5 Examining each Neighborhoods' Shopping Mall cluster"
]
},
{
"cell_type": "code",
"execution_count": 87,
"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>Neighborhood</th>\n",
" <th>Shopping Mall</th>\n",
" <th>Cluster_labels</th>\n",
" <th>Latitude</th>\n",
" <th>Longitude</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Balanagar</td>\n",
" <td>0.142857</td>\n",
" <td>0</td>\n",
" <td>17.476746</td>\n",
" <td>78.422108</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Neighborhood Shopping Mall Cluster_labels Latitude Longitude\n",
"3 Balanagar 0.142857 0 17.476746 78.422108"
]
},
"execution_count": 87,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hyd_mall_merged.loc[hyd_mall_merged['Cluster_labels'] == 0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" This is the cluster 0 with only 1 Neighborhood with mean value more than all the clusters as it is an industrial area with small shopping malls around it"
]
},
{
"cell_type": "code",
"execution_count": 88,
"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>Neighborhood</th>\n",
" <th>Shopping Mall</th>\n",
" <th>Cluster_labels</th>\n",
" <th>Latitude</th>\n",
" <th>Longitude</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Alwal</td>\n",
" <td>0.00</td>\n",
" <td>1</td>\n",
" <td>17.502229</td>\n",
" <td>78.508858</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>Secunderabad</td>\n",
" <td>0.00</td>\n",
" <td>1</td>\n",
" <td>17.433725</td>\n",
" <td>78.500683</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>Sanathnagar</td>\n",
" <td>0.00</td>\n",
" <td>1</td>\n",
" <td>17.456965</td>\n",
" <td>78.443478</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>Rajendranagar</td>\n",
" <td>0.00</td>\n",
" <td>1</td>\n",
" <td>17.311091</td>\n",
" <td>78.442585</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>Patancheru</td>\n",
" <td>0.00</td>\n",
" <td>1</td>\n",
" <td>17.528609</td>\n",
" <td>78.267425</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>Musheerabad</td>\n",
" <td>0.00</td>\n",
" <td>1</td>\n",
" <td>17.419142</td>\n",
" <td>78.498573</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>Mehdipatnam</td>\n",
" <td>0.00</td>\n",
" <td>1</td>\n",
" <td>17.394263</td>\n",
" <td>78.434251</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>Malkajgiri</td>\n",
" <td>0.00</td>\n",
" <td>1</td>\n",
" <td>17.448344</td>\n",
" <td>78.528973</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>LB Nagar</td>\n",
" <td>0.00</td>\n",
" <td>1</td>\n",
" <td>17.350162</td>\n",
" <td>78.551094</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>Serilingampally</td>\n",
" <td>0.00</td>\n",
" <td>1</td>\n",
" <td>17.465657</td>\n",
" <td>78.340672</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>Uppal</td>\n",
" <td>0.00</td>\n",
" <td>1</td>\n",
" <td>17.402509</td>\n",
" <td>78.561256</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>Jubilee Hills</td>\n",
" <td>0.00</td>\n",
" <td>1</td>\n",
" <td>17.430836</td>\n",
" <td>78.410288</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>Hayathnagar</td>\n",
" <td>0.00</td>\n",
" <td>1</td>\n",
" <td>17.328115</td>\n",
" <td>78.604540</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Gachibowli</td>\n",
" <td>0.00</td>\n",
" <td>1</td>\n",
" <td>17.443622</td>\n",
" <td>78.351964</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Ameerpet</td>\n",
" <td>0.01</td>\n",
" <td>1</td>\n",
" <td>17.437501</td>\n",
" <td>78.448251</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>Kompally</td>\n",
" <td>0.00</td>\n",
" <td>1</td>\n",
" <td>17.544703</td>\n",
" <td>78.491842</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Neighborhood Shopping Mall Cluster_labels Latitude Longitude\n",
"0 Alwal 0.00 1 17.502229 78.508858\n",
"22 Secunderabad 0.00 1 17.433725 78.500683\n",
"20 Sanathnagar 0.00 1 17.456965 78.443478\n",
"19 Rajendranagar 0.00 1 17.311091 78.442585\n",
"18 Patancheru 0.00 1 17.528609 78.267425\n",
"16 Musheerabad 0.00 1 17.419142 78.498573\n",
"15 Mehdipatnam 0.00 1 17.394263 78.434251\n",
"14 Malkajgiri 0.00 1 17.448344 78.528973\n",
"13 LB Nagar 0.00 1 17.350162 78.551094\n",
"23 Serilingampally 0.00 1 17.465657 78.340672\n",
"24 Uppal 0.00 1 17.402509 78.561256\n",
"8 Jubilee Hills 0.00 1 17.430836 78.410288\n",
"7 Hayathnagar 0.00 1 17.328115 78.604540\n",
"5 Gachibowli 0.00 1 17.443622 78.351964\n",
"2 Ameerpet 0.01 1 17.437501 78.448251\n",
"11 Kompally 0.00 1 17.544703 78.491842"
]
},
"execution_count": 88,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hyd_mall_merged.loc[hyd_mall_merged['Cluster_labels'] == 1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" This is the cluster-1 with almost no shopping malls, some of the areas might be having one or two shopping malls but our model did not detect it because of the radius(2000m) that we have chosed."
]
},
{
"cell_type": "code",
"execution_count": 89,
"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>Neighborhood</th>\n",
" <th>Shopping Mall</th>\n",
" <th>Cluster_labels</th>\n",
" <th>Latitude</th>\n",
" <th>Longitude</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>Kapra</td>\n",
" <td>0.071429</td>\n",
" <td>2</td>\n",
" <td>17.484636</td>\n",
" <td>78.561009</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Dilsukhnagar</td>\n",
" <td>0.076923</td>\n",
" <td>2</td>\n",
" <td>17.368431</td>\n",
" <td>78.523428</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>Kukatpally</td>\n",
" <td>0.080000</td>\n",
" <td>2</td>\n",
" <td>17.493084</td>\n",
" <td>78.405441</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Neighborhood Shopping Mall Cluster_labels Latitude Longitude\n",
"9 Kapra 0.071429 2 17.484636 78.561009\n",
"4 Dilsukhnagar 0.076923 2 17.368431 78.523428\n",
"12 Kukatpally 0.080000 2 17.493084 78.405441"
]
},
"execution_count": 89,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hyd_mall_merged.loc[hyd_mall_merged['Cluster_labels'] == 2]"
]
},
{
"cell_type": "code",
"execution_count": 90,
"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>Neighborhood</th>\n",
" <th>Shopping Mall</th>\n",
" <th>Cluster_labels</th>\n",
" <th>Latitude</th>\n",
" <th>Longitude</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>Khairatabad</td>\n",
" <td>0.030000</td>\n",
" <td>3</td>\n",
" <td>17.411771</td>\n",
" <td>78.462200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>HITEC City</td>\n",
" <td>0.020000</td>\n",
" <td>3</td>\n",
" <td>17.449005</td>\n",
" <td>78.383138</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>Nampally</td>\n",
" <td>0.013699</td>\n",
" <td>3</td>\n",
" <td>17.391852</td>\n",
" <td>78.466005</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Neighborhood Shopping Mall Cluster_labels Latitude Longitude\n",
"10 Khairatabad 0.030000 3 17.411771 78.462200\n",
"6 HITEC City 0.020000 3 17.449005 78.383138\n",
"17 Nampally 0.013699 3 17.391852 78.466005"
]
},
"execution_count": 90,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hyd_mall_merged.loc[hyd_mall_merged['Cluster_labels'] == 3]"
]
},
{
"cell_type": "code",
"execution_count": 91,
"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>Neighborhood</th>\n",
" <th>Shopping Mall</th>\n",
" <th>Cluster_labels</th>\n",
" <th>Latitude</th>\n",
" <th>Longitude</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>Saroornagar</td>\n",
" <td>0.055556</td>\n",
" <td>4</td>\n",
" <td>17.361165</td>\n",
" <td>78.538756</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Amberpet</td>\n",
" <td>0.047619</td>\n",
" <td>4</td>\n",
" <td>17.390263</td>\n",
" <td>78.516481</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Neighborhood Shopping Mall Cluster_labels Latitude Longitude\n",
"21 Saroornagar 0.055556 4 17.361165 78.538756\n",
"1 Amberpet 0.047619 4 17.390263 78.516481"
]
},
"execution_count": 91,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hyd_mall_merged.loc[hyd_mall_merged['Cluster_labels'] == 4]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" These clusters 2,3 and 4 has enough number of shopping malls in these neighborhoods with different frequencies of mean.\n",
" Neighborhoods in clusters 2 and 3 comes under main city or popular areas of the city Hyderabad."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 6. Conclusion"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Most of the shopping malls are situated in the main areas of Hyderabad city, with the highest number in cluster 2,3 and moderate number of malls in cluster 0 and 4. On the other hand, cluster 1 has very low number to completely no shopping malls in the neighborhoods. This represents a great opportunity and high potential areas to open new shopping malls as there is very little to no competition from existing malls and are popular areas of the city with IT hubs.\n",
" \n",
"- Meanwhile, shopping malls in cluster 2 are likely suffering from intense competition due to oversupply and high concentration of shopping malls, this also shows that the oversupply of shopping malls mostly happened in the central area to main area of the city, with the outskirts area still have very few shopping malls. Therefore, this project recommends property developers to invest on these findings to open new shopping malls in neighborhoods in cluster 1 with little to no competition.\n",
" \n",
"- Property developers with unique selling propositions to stand out from the competition can also open new shopping malls in neighborhoods in cluster 4 with moderate competition.\n",
" \n",
"- Finally, property developers are advised to avoid neighborhoods in cluster 2,3 which already have high concentration of shopping malls and suffering from intense competition."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python",
"language": "python",
"name": "conda-env-python-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
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