import pandas as pd
import numpy as np
from numpy import array
import seaborn as sns
import matplotlib as mpl
from matplotlib import cm
import matplotlib.pyplot as plt
import datetime
%matplotlib inline
df = pd.read_csv(r"E:\ML - Data\India's 500 Cities Data\cities_r2.csv")
df.head()
name_of_city | state_code | state_name | dist_code | population_total | population_male | population_female | 0-6_population_total | 0-6_population_male | 0-6_population_female | ... | literates_female | sex_ratio | child_sex_ratio | effective_literacy_rate_total | effective_literacy_rate_male | effective_literacy_rate_female | location | total_graduates | male_graduates | female_graduates | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | Abohar | 3 | PUNJAB | 9 | 145238 | 76840 | 68398 | 15870 | 8587 | 7283 | ... | 44972 | 890 | 848 | 79.86 | 85.49 | 73.59 | 30.1452928,74.1993043 | 16287 | 8612 | 7675 |
1 | Achalpur | 27 | MAHARASHTRA | 7 | 112293 | 58256 | 54037 | 11810 | 6186 | 5624 | ... | 43086 | 928 | 909 | 91.99 | 94.77 | 89.00 | 21.257584,77.5086754 | 8863 | 5269 | 3594 |
2 | Adilabad | 28 | ANDHRA PRADESH | 1 | 117388 | 59232 | 58156 | 13103 | 6731 | 6372 | ... | 37660 | 982 | 947 | 80.51 | 88.18 | 72.73 | 19.0809075,79.560344 | 10565 | 6797 | 3768 |
3 | Adityapur | 20 | JHARKHAND | 24 | 173988 | 91495 | 82493 | 23042 | 12063 | 10979 | ... | 54515 | 902 | 910 | 83.46 | 89.98 | 76.23 | 22.7834741,86.1576889 | 19225 | 12189 | 7036 |
4 | Adoni | 28 | ANDHRA PRADESH | 21 | 166537 | 82743 | 83794 | 18406 | 9355 | 9051 | ... | 45089 | 1013 | 968 | 68.38 | 76.58 | 60.33 | 15.6322227,77.2728368 | 11902 | 7871 | 4031 |
5 rows × 22 columns
df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 493 entries, 0 to 492 Data columns (total 22 columns): name_of_city 493 non-null object state_code 493 non-null int64 state_name 493 non-null object dist_code 493 non-null int64 population_total 493 non-null int64 population_male 493 non-null int64 population_female 493 non-null int64 0-6_population_total 493 non-null int64 0-6_population_male 493 non-null int64 0-6_population_female 493 non-null int64 literates_total 493 non-null int64 literates_male 493 non-null int64 literates_female 493 non-null int64 sex_ratio 493 non-null int64 child_sex_ratio 493 non-null int64 effective_literacy_rate_total 493 non-null float64 effective_literacy_rate_male 493 non-null float64 effective_literacy_rate_female 493 non-null float64 location 493 non-null object total_graduates 493 non-null int64 male_graduates 493 non-null int64 female_graduates 493 non-null int64 dtypes: float64(3), int64(16), object(3) memory usage: 84.8+ KB
df.describe()
state_code | dist_code | population_total | population_male | population_female | 0-6_population_total | 0-6_population_male | 0-6_population_female | literates_total | literates_male | literates_female | sex_ratio | child_sex_ratio | effective_literacy_rate_total | effective_literacy_rate_male | effective_literacy_rate_female | total_graduates | male_graduates | female_graduates | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
count | 493.000000 | 493.000000 | 4.930000e+02 | 4.930000e+02 | 4.930000e+02 | 4.930000e+02 | 493.000000 | 493.000000 | 4.930000e+02 | 4.930000e+02 | 4.930000e+02 | 493.000000 | 493.000000 | 493.000000 | 493.000000 | 493.000000 | 4.930000e+02 | 4.930000e+02 | 4.930000e+02 |
mean | 18.643002 | 16.782961 | 4.481124e+05 | 2.343468e+05 | 2.137656e+05 | 4.709285e+04 | 24849.527383 | 22243.320487 | 3.461527e+05 | 1.894384e+05 | 1.567143e+05 | 930.294118 | 902.332657 | 85.131460 | 89.920162 | 79.967181 | 6.620236e+04 | 3.771556e+04 | 2.848680e+04 |
std | 9.297168 | 15.566131 | 1.033228e+06 | 5.487786e+05 | 4.848622e+05 | 1.050279e+05 | 55535.310272 | 49523.241379 | 8.220952e+05 | 4.534753e+05 | 3.690677e+05 | 55.849106 | 49.794689 | 6.186345 | 5.377492 | 7.577825 | 1.778187e+05 | 9.849574e+04 | 7.951556e+04 |
min | 1.000000 | 1.000000 | 1.000360e+05 | 5.020100e+04 | 4.512600e+04 | 6.547000e+03 | 3406.000000 | 3107.000000 | 5.699800e+04 | 3.475100e+04 | 2.224700e+04 | 700.000000 | 762.000000 | 49.510000 | 52.270000 | 46.450000 | 2.532000e+03 | 1.703000e+03 | 8.290000e+02 |
25% | 9.000000 | 7.000000 | 1.261420e+05 | 6.638400e+04 | 6.041100e+04 | 1.363900e+04 | 7221.000000 | 6457.000000 | 9.768700e+04 | 5.357800e+04 | 4.391400e+04 | 890.000000 | 868.000000 | 81.750000 | 87.280000 | 75.800000 | 1.527700e+04 | 9.289000e+03 | 6.114000e+03 |
50% | 19.000000 | 13.000000 | 1.841330e+05 | 9.665500e+04 | 8.776800e+04 | 1.944000e+04 | 10342.000000 | 9172.000000 | 1.413290e+05 | 7.590600e+04 | 6.383600e+04 | 922.000000 | 903.000000 | 85.970000 | 91.180000 | 80.920000 | 2.395900e+04 | 1.404900e+04 | 9.558000e+03 |
75% | 27.000000 | 21.000000 | 3.490330e+05 | 1.750550e+05 | 1.700260e+05 | 3.794500e+04 | 19982.000000 | 17954.000000 | 2.679000e+05 | 1.455480e+05 | 1.235030e+05 | 971.000000 | 942.000000 | 89.330000 | 93.400000 | 85.400000 | 5.036700e+04 | 2.787200e+04 | 2.086600e+04 |
max | 35.000000 | 99.000000 | 1.247845e+07 | 6.736815e+06 | 5.741632e+06 | 1.209275e+06 | 647938.000000 | 561337.000000 | 1.023759e+07 | 5.727774e+06 | 4.509812e+06 | 1093.000000 | 1185.000000 | 98.800000 | 99.300000 | 98.310000 | 2.221137e+06 | 1.210040e+06 | 1.011097e+06 |
df.shape
(493, 22)
# Extracting Co-ordinates details from the provided data
df['latitude'] = df['location'].apply(lambda x: x.split(',')[0])
df['longitude'] = df['location'].apply(lambda x: x.split(',')[1])
df.head()
name_of_city | state_code | state_name | dist_code | population_total | population_male | population_female | 0-6_population_total | 0-6_population_male | 0-6_population_female | ... | child_sex_ratio | effective_literacy_rate_total | effective_literacy_rate_male | effective_literacy_rate_female | location | total_graduates | male_graduates | female_graduates | latitude | longitude | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | Abohar | 3 | PUNJAB | 9 | 145238 | 76840 | 68398 | 15870 | 8587 | 7283 | ... | 848 | 79.86 | 85.49 | 73.59 | 30.1452928,74.1993043 | 16287 | 8612 | 7675 | 30.1452928 | 74.1993043 |
1 | Achalpur | 27 | MAHARASHTRA | 7 | 112293 | 58256 | 54037 | 11810 | 6186 | 5624 | ... | 909 | 91.99 | 94.77 | 89.00 | 21.257584,77.5086754 | 8863 | 5269 | 3594 | 21.257584 | 77.5086754 |
2 | Adilabad | 28 | ANDHRA PRADESH | 1 | 117388 | 59232 | 58156 | 13103 | 6731 | 6372 | ... | 947 | 80.51 | 88.18 | 72.73 | 19.0809075,79.560344 | 10565 | 6797 | 3768 | 19.0809075 | 79.560344 |
3 | Adityapur | 20 | JHARKHAND | 24 | 173988 | 91495 | 82493 | 23042 | 12063 | 10979 | ... | 910 | 83.46 | 89.98 | 76.23 | 22.7834741,86.1576889 | 19225 | 12189 | 7036 | 22.7834741 | 86.1576889 |
4 | Adoni | 28 | ANDHRA PRADESH | 21 | 166537 | 82743 | 83794 | 18406 | 9355 | 9051 | ... | 968 | 68.38 | 76.58 | 60.33 | 15.6322227,77.2728368 | 11902 | 7871 | 4031 | 15.6322227 | 77.2728368 |
5 rows × 24 columns
pop_cities = df.sort_values(by = 'population_total', ascending = False)
ten_pop_cities = pop_cities.head(10)
ten_pop_cities
name_of_city | state_code | state_name | dist_code | population_total | population_male | population_female | 0-6_population_total | 0-6_population_male | 0-6_population_female | ... | child_sex_ratio | effective_literacy_rate_total | effective_literacy_rate_male | effective_literacy_rate_female | location | total_graduates | male_graduates | female_graduates | latitude | longitude | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
185 | Greater Mumbai | 27 | MAHARASHTRA | 99 | 12478447 | 6736815 | 5741632 | 1139146 | 599007 | 540139 | ... | 902 | 90.28 | 93.32 | 86.70 | 19.0760,72.8777 | 1802371 | 964964 | 837407 | 19.0760 | 72.8777 |
141 | Delhi | 7 | NCT OF DELHI | 99 | 11007835 | 5871362 | 5136473 | 1209275 | 647938 | 561337 | ... | 866 | 87.60 | 91.44 | 83.20 | 28.7041,77.1025 | 2221137 | 1210040 | 1011097 | 28.7041 | 77.1025 |
72 | Bengaluru | 29 | KARNATAKA | 18 | 8425970 | 4401299 | 4024671 | 862493 | 444639 | 417854 | ... | 940 | 89.59 | 92.63 | 86.25 | 12.9716,77.5946 | 1591163 | 908363 | 682800 | 12.9716 | 77.5946 |
184 | Greater Hyderabad | 28 | ANDHRA PRADESH | 99 | 6809970 | 3500802 | 3309168 | 725816 | 373794 | 352022 | ... | 942 | 82.96 | 85.96 | 79.79 | 17.3850,78.4867 | 1164149 | 685402 | 478747 | 17.3850 | 78.4867 |
7 | Ahmadabad | 24 | GUJARAT | 7 | 5570585 | 2935869 | 2634716 | 589076 | 317917 | 271159 | ... | 853 | 89.62 | 93.96 | 84.81 | 23.022505,72.5713621 | 769858 | 435267 | 334591 | 23.022505 | 72.5713621 |
119 | Chennai | 33 | TAMIL NADU | 2 | 4681087 | 2357633 | 2323454 | 418541 | 213084 | 205457 | ... | 964 | 90.33 | 93.47 | 87.16 | 13.0826802,80.2707184 | 879695 | 487428 | 392267 | 13.0826802 | 80.2707184 |
274 | Kolkata | 19 | WEST BENGAL | 16 | 4486679 | 2362662 | 2124017 | 300052 | 155475 | 144577 | ... | 930 | 87.14 | 89.08 | 84.98 | 22.572646,88.363895 | 818476 | 461615 | 356861 | 22.572646 | 88.363895 |
449 | Surat | 24 | GUJARAT | 25 | 4462002 | 2538243 | 1923759 | 531522 | 293208 | 238314 | ... | 813 | 89.03 | 92.76 | 84.05 | 21.1702401,72.8310607 | 278795 | 160566 | 118229 | 21.1702401 | 72.8310607 |
380 | Pune | 27 | MAHARASHTRA | 25 | 3115431 | 1602137 | 1513294 | 324572 | 171152 | 153420 | ... | 896 | 91.61 | 95.13 | 87.91 | 18.5204303,73.8567437 | 656508 | 349022 | 307486 | 18.5204303 | 73.8567437 |
225 | Jaipur | 8 | RAJASTHAN | 12 | 3073350 | 1619280 | 1454070 | 378788 | 204320 | 174468 | ... | 854 | 84.34 | 90.61 | 77.41 | 26.9124336,75.7872709 | 533148 | 319107 | 214041 | 26.9124336 | 75.7872709 |
10 rows × 24 columns
from mpl_toolkits.basemap import Basemap
plt.subplots(figsize=(20, 15))
map = Basemap(width=1200000,height=900000,projection='lcc',resolution='l',
llcrnrlon=67,llcrnrlat=5,urcrnrlon=99,urcrnrlat=37,lat_0=28,lon_0=77)
#set a background colour
map.drawmapboundary(fill_color='#85A6D9')
# draw coastlines, country boundaries, fill continents.
map.fillcontinents(color='white',lake_color='#85A6D9')
map.drawcoastlines(color='#6D5F47', linewidth=.4)
map.drawcountries(color='#6D5F47', linewidth=.4)
# lat/lon coordinates of top ten indian cities
lngs=array(ten_pop_cities['longitude'])
lats=array(ten_pop_cities['latitude'])
populations = array(ten_pop_cities['population_total'])
nc=array(ten_pop_cities['name_of_city'])
#scale populations to emphasise different relative pop sizes
s_populations = [p/5000 for p in populations]
# compute the native map projection coordinates for cities
x,y = map(lngs,lats)
#scatter scaled circles at the city locations
map.scatter(
x,
y,
s=s_populations, #size
c=s_populations, #color
marker='o', #symbol
alpha=0.75, #transparency
zorder = 2, #plotting order
cmap = cm.Dark2
)
# plot population labels of the ten cities.
for name, xpt, ypt in zip(nc, x, y):
plt.text(
xpt+60000,
ypt+30000,
name,
fontsize= 15,
fontweight='bold',
horizontalalignment='center',
verticalalignment='center',
zorder = 3,
)
#add a title and display the map on screen
plt.title('Top Ten Cities of India By Population', fontsize=20)
plt.show()
C:\Users\rabiulawal\Anaconda3\lib\site-packages\mpl_toolkits\basemap\__init__.py:3236: MatplotlibDeprecationWarning: The ishold function was deprecated in version 2.0. b = ax.ishold() C:\Users\rabiulawal\Anaconda3\lib\site-packages\mpl_toolkits\basemap\__init__.py:3245: MatplotlibDeprecationWarning: axes.hold is deprecated. See the API Changes document (http://matplotlib.org/api/api_changes.html) for more details. ax.hold(b)
def plot_map(sz, colorbarVal, title):
plt.subplots(figsize=(20, 15))
map = Basemap(width=1200000,height=900000,projection='lcc',resolution='l',
llcrnrlon=67,llcrnrlat=5,urcrnrlon=99,urcrnrlat=37,lat_0=28,lon_0=77)
#set a background colour
map.drawmapboundary(fill_color='#85A6D9')
# draw coastlines, country boundaries, fill continents.
map.fillcontinents(color='white',lake_color='#85A6D9')
map.drawcoastlines(color='#6D5F47', linewidth=.4)
map.drawcountries(color='#6D5F47', linewidth=.4)
# lat/lon coordinates of top ten indian cities
lngs=array(df['longitude'])
lats=array(df['latitude'])
# compute the native map projection coordinates for cities
x,y = map(lngs,lats)
#scatter scaled circles at the city locations
cs = map.scatter(
x,
y,
s=sz, #size
c=sz, #color
marker='o', #symbol
alpha=0.5, #transparency
zorder = 2, #plotting order
cmap = cm.Dark2
)
# adding colorbar
cbar = map.colorbar(cs, location='right',pad="5%")
cbar.ax.set_yticklabels(colorbarVal)
#add a title and display the map on screen
plt.title(title, fontsize=20)
plt.show()
plt.figure(figsize=(15, 5))
city_states = df.groupby(['state_name'])['name_of_city'].count().sort_values(ascending = False)
city_states.plot(kind = 'bar', fontsize = 12, )
plt.grid(b=True, which='both', color='Black',linestyle='-')
plt.xlabel('States', fontsize=15)
plt.ylabel('Population', fontsize=15)
plt.title('States by number of cities in top 500', fontsize = 20)
plt.show ()
Uttara Pradesh and West Bengal has most cities of India, more than 60 cities in each. Meghalaya, Mizoram, Nagaland and Anadaman & Nicobar Islands are the least counted states (less than 5 cities).
plt.figure(figsize=(15, 5))
pop_states = df[['population_total','state_name']].groupby(['state_name'])['population_total'].sum().sort_values(ascending = False)
pop_states.plot(kind = 'bar', fontsize = 12, alpha = 1)
plt.grid(b=True, which='both', color='Black',linestyle='-')
plt.xlabel('States', fontsize=15)
plt.ylabel('Population', fontsize=15)
plt.title('States by Total population in top 500', fontsize = 20)
plt.show ()
# Plotting the same on the map
population_sizes = df["population_total"].apply(lambda x: int(x / 5000))
colorbarValue = np.linspace(df["population_total"].min(), df["population_total"].max(),
num=10)
colorbarValue = colorbarValue.astype(int)
title = 'Population of Cities in Indian Map'
plot_map(population_sizes, colorbarValue, title)
C:\Users\rabiulawal\Anaconda3\lib\site-packages\mpl_toolkits\basemap\__init__.py:3236: MatplotlibDeprecationWarning: The ishold function was deprecated in version 2.0. b = ax.ishold() C:\Users\rabiulawal\Anaconda3\lib\site-packages\mpl_toolkits\basemap\__init__.py:3245: MatplotlibDeprecationWarning: axes.hold is deprecated. See the API Changes document (http://matplotlib.org/api/api_changes.html) for more details. ax.hold(b)
plt.figure(figsize=(15, 5))
pop_male_states = df.groupby(['state_name'])['population_male'].sum().sort_values(ascending = False)
pop_male_states.plot(kind = 'bar', fontsize = 12)
plt.grid(b=True, which='both', color='Black',linestyle='-')
plt.xlabel('States', fontsize=15)
plt.ylabel('Population Male', fontsize=15)
plt.title('States by Total Male Population in Top 500 cities', fontsize = 20)
plt.show ()
# Plotting the same on the map
population_sizes = df["population_male"].apply(lambda x: int(x / 5000))
colorbarValue = np.linspace(df["population_male"].min(), df["population_male"].max(),
num=10)
colorbarValue = colorbarValue.astype(int)
title = "Male Population of Cities in India's Map"
plot_map(population_sizes, colorbarValue, title)
C:\Users\rabiulawal\Anaconda3\lib\site-packages\mpl_toolkits\basemap\__init__.py:3236: MatplotlibDeprecationWarning: The ishold function was deprecated in version 2.0. b = ax.ishold() C:\Users\rabiulawal\Anaconda3\lib\site-packages\mpl_toolkits\basemap\__init__.py:3245: MatplotlibDeprecationWarning: axes.hold is deprecated. See the API Changes document (http://matplotlib.org/api/api_changes.html) for more details. ax.hold(b)
plt.figure(figsize=(15, 5))
pop_female_states = df.groupby(['state_name'])['population_female'].sum().sort_values(ascending = False)
pop_female_states.plot(kind = 'bar', fontsize = 12)
plt.grid(b=True, which='both', color='Black',linestyle='-')
plt.xlabel('Population Male', fontsize=15)
plt.ylabel('States', fontsize=15)
plt.title('States by most female populion in top 500 cities', fontsize = 20)
plt.show ()
# Plotting the same on the map
population_sizes = df["population_female"].apply(lambda x: int(x / 5000))
colorbarValue = np.linspace(df["population_female"].min(), df["population_female"].max(),
num=10)
colorbarValue = colorbarValue.astype(int)
title = "Female Population of Cities in India's Map"
plot_map(population_sizes, colorbarValue, title)
C:\Users\rabiulawal\Anaconda3\lib\site-packages\mpl_toolkits\basemap\__init__.py:3236: MatplotlibDeprecationWarning: The ishold function was deprecated in version 2.0. b = ax.ishold() C:\Users\rabiulawal\Anaconda3\lib\site-packages\mpl_toolkits\basemap\__init__.py:3245: MatplotlibDeprecationWarning: axes.hold is deprecated. See the API Changes document (http://matplotlib.org/api/api_changes.html) for more details. ax.hold(b)
male_pop_cities = df.sort_values(by = 'population_total', ascending = False)
ten_male_pop_cities = pop_cities.head(10)
ten_male_pop_cities
name_of_city | state_code | state_name | dist_code | population_total | population_male | population_female | 0-6_population_total | 0-6_population_male | 0-6_population_female | ... | child_sex_ratio | effective_literacy_rate_total | effective_literacy_rate_male | effective_literacy_rate_female | location | total_graduates | male_graduates | female_graduates | latitude | longitude | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
185 | Greater Mumbai | 27 | MAHARASHTRA | 99 | 12478447 | 6736815 | 5741632 | 1139146 | 599007 | 540139 | ... | 902 | 90.28 | 93.32 | 86.70 | 19.0760,72.8777 | 1802371 | 964964 | 837407 | 19.0760 | 72.8777 |
141 | Delhi | 7 | NCT OF DELHI | 99 | 11007835 | 5871362 | 5136473 | 1209275 | 647938 | 561337 | ... | 866 | 87.60 | 91.44 | 83.20 | 28.7041,77.1025 | 2221137 | 1210040 | 1011097 | 28.7041 | 77.1025 |
72 | Bengaluru | 29 | KARNATAKA | 18 | 8425970 | 4401299 | 4024671 | 862493 | 444639 | 417854 | ... | 940 | 89.59 | 92.63 | 86.25 | 12.9716,77.5946 | 1591163 | 908363 | 682800 | 12.9716 | 77.5946 |
184 | Greater Hyderabad | 28 | ANDHRA PRADESH | 99 | 6809970 | 3500802 | 3309168 | 725816 | 373794 | 352022 | ... | 942 | 82.96 | 85.96 | 79.79 | 17.3850,78.4867 | 1164149 | 685402 | 478747 | 17.3850 | 78.4867 |
7 | Ahmadabad | 24 | GUJARAT | 7 | 5570585 | 2935869 | 2634716 | 589076 | 317917 | 271159 | ... | 853 | 89.62 | 93.96 | 84.81 | 23.022505,72.5713621 | 769858 | 435267 | 334591 | 23.022505 | 72.5713621 |
119 | Chennai | 33 | TAMIL NADU | 2 | 4681087 | 2357633 | 2323454 | 418541 | 213084 | 205457 | ... | 964 | 90.33 | 93.47 | 87.16 | 13.0826802,80.2707184 | 879695 | 487428 | 392267 | 13.0826802 | 80.2707184 |
274 | Kolkata | 19 | WEST BENGAL | 16 | 4486679 | 2362662 | 2124017 | 300052 | 155475 | 144577 | ... | 930 | 87.14 | 89.08 | 84.98 | 22.572646,88.363895 | 818476 | 461615 | 356861 | 22.572646 | 88.363895 |
449 | Surat | 24 | GUJARAT | 25 | 4462002 | 2538243 | 1923759 | 531522 | 293208 | 238314 | ... | 813 | 89.03 | 92.76 | 84.05 | 21.1702401,72.8310607 | 278795 | 160566 | 118229 | 21.1702401 | 72.8310607 |
380 | Pune | 27 | MAHARASHTRA | 25 | 3115431 | 1602137 | 1513294 | 324572 | 171152 | 153420 | ... | 896 | 91.61 | 95.13 | 87.91 | 18.5204303,73.8567437 | 656508 | 349022 | 307486 | 18.5204303 | 73.8567437 |
225 | Jaipur | 8 | RAJASTHAN | 12 | 3073350 | 1619280 | 1454070 | 378788 | 204320 | 174468 | ... | 854 | 84.34 | 90.61 | 77.41 | 26.9124336,75.7872709 | 533148 | 319107 | 214041 | 26.9124336 | 75.7872709 |
10 rows × 24 columns
from mpl_toolkits.basemap import Basemap
plt.subplots(figsize=(20, 15))
map = Basemap(width=1200000,height=900000,projection='lcc',resolution='l',
llcrnrlon=67,llcrnrlat=5,urcrnrlon=99,urcrnrlat=37,lat_0=28,lon_0=77)
#set a background colour
map.drawmapboundary(fill_color='#85A6D9')
# draw coastlines, country boundaries, fill continents.
map.fillcontinents(color='white',lake_color='#85A6D9')
map.drawcoastlines(color='#6D5F47', linewidth=.4)
map.drawcountries(color='#6D5F47', linewidth=.4)
# lat/lon coordinates of top ten indian cities
lngs=array(ten_pop_cities['longitude'])
lats=array(ten_pop_cities['latitude'])
populations = array(ten_pop_cities['population_male'])
nc=array(ten_pop_cities['name_of_city'])
#scale populations to emphasise different relative pop sizes
s_populations = [p/5000 for p in populations]
# compute the native map projection coordinates for cities
x,y = map(lngs,lats)
#scatter scaled circles at the city locations
map.scatter(
x,
y,
s=s_populations, #size
c=s_populations, #color
marker='o', #symbol
alpha=0.75, #transparency
zorder = 2, #plotting order
cmap = cm.Dark2
)
# plot population labels of the ten cities.
for name, xpt, ypt in zip(nc, x, y):
plt.text(
xpt+60000,
ypt+30000,
name,
fontsize= 15,
fontweight='bold',
horizontalalignment='center',
verticalalignment='center',
zorder = 3,
)
#add a title and display the map on screen
plt.title('Top Ten Cities of India By male population', fontsize=20)
plt.show()
C:\Users\rabiulawal\Anaconda3\lib\site-packages\mpl_toolkits\basemap\__init__.py:3236: MatplotlibDeprecationWarning: The ishold function was deprecated in version 2.0. b = ax.ishold() C:\Users\rabiulawal\Anaconda3\lib\site-packages\mpl_toolkits\basemap\__init__.py:3245: MatplotlibDeprecationWarning: axes.hold is deprecated. See the API Changes document (http://matplotlib.org/api/api_changes.html) for more details. ax.hold(b)
male_pop_cities = df.sort_values(by = 'population_female', ascending = False)
ten_male_pop_cities = pop_cities.head(10)
ten_male_pop_cities
name_of_city | state_code | state_name | dist_code | population_total | population_male | population_female | 0-6_population_total | 0-6_population_male | 0-6_population_female | ... | child_sex_ratio | effective_literacy_rate_total | effective_literacy_rate_male | effective_literacy_rate_female | location | total_graduates | male_graduates | female_graduates | latitude | longitude | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
185 | Greater Mumbai | 27 | MAHARASHTRA | 99 | 12478447 | 6736815 | 5741632 | 1139146 | 599007 | 540139 | ... | 902 | 90.28 | 93.32 | 86.70 | 19.0760,72.8777 | 1802371 | 964964 | 837407 | 19.0760 | 72.8777 |
141 | Delhi | 7 | NCT OF DELHI | 99 | 11007835 | 5871362 | 5136473 | 1209275 | 647938 | 561337 | ... | 866 | 87.60 | 91.44 | 83.20 | 28.7041,77.1025 | 2221137 | 1210040 | 1011097 | 28.7041 | 77.1025 |
72 | Bengaluru | 29 | KARNATAKA | 18 | 8425970 | 4401299 | 4024671 | 862493 | 444639 | 417854 | ... | 940 | 89.59 | 92.63 | 86.25 | 12.9716,77.5946 | 1591163 | 908363 | 682800 | 12.9716 | 77.5946 |
184 | Greater Hyderabad | 28 | ANDHRA PRADESH | 99 | 6809970 | 3500802 | 3309168 | 725816 | 373794 | 352022 | ... | 942 | 82.96 | 85.96 | 79.79 | 17.3850,78.4867 | 1164149 | 685402 | 478747 | 17.3850 | 78.4867 |
7 | Ahmadabad | 24 | GUJARAT | 7 | 5570585 | 2935869 | 2634716 | 589076 | 317917 | 271159 | ... | 853 | 89.62 | 93.96 | 84.81 | 23.022505,72.5713621 | 769858 | 435267 | 334591 | 23.022505 | 72.5713621 |
119 | Chennai | 33 | TAMIL NADU | 2 | 4681087 | 2357633 | 2323454 | 418541 | 213084 | 205457 | ... | 964 | 90.33 | 93.47 | 87.16 | 13.0826802,80.2707184 | 879695 | 487428 | 392267 | 13.0826802 | 80.2707184 |
274 | Kolkata | 19 | WEST BENGAL | 16 | 4486679 | 2362662 | 2124017 | 300052 | 155475 | 144577 | ... | 930 | 87.14 | 89.08 | 84.98 | 22.572646,88.363895 | 818476 | 461615 | 356861 | 22.572646 | 88.363895 |
449 | Surat | 24 | GUJARAT | 25 | 4462002 | 2538243 | 1923759 | 531522 | 293208 | 238314 | ... | 813 | 89.03 | 92.76 | 84.05 | 21.1702401,72.8310607 | 278795 | 160566 | 118229 | 21.1702401 | 72.8310607 |
380 | Pune | 27 | MAHARASHTRA | 25 | 3115431 | 1602137 | 1513294 | 324572 | 171152 | 153420 | ... | 896 | 91.61 | 95.13 | 87.91 | 18.5204303,73.8567437 | 656508 | 349022 | 307486 | 18.5204303 | 73.8567437 |
225 | Jaipur | 8 | RAJASTHAN | 12 | 3073350 | 1619280 | 1454070 | 378788 | 204320 | 174468 | ... | 854 | 84.34 | 90.61 | 77.41 | 26.9124336,75.7872709 | 533148 | 319107 | 214041 | 26.9124336 | 75.7872709 |
10 rows × 24 columns
from mpl_toolkits.basemap import Basemap
plt.subplots(figsize=(20, 15))
map = Basemap(width=1200000,height=900000,projection='lcc',resolution='l',
llcrnrlon=67,llcrnrlat=5,urcrnrlon=99,urcrnrlat=37,lat_0=28,lon_0=77)
#set a background colour
map.drawmapboundary(fill_color='#85A6D9')
# draw coastlines, country boundaries, fill continents.
map.fillcontinents(color='white',lake_color='#85A6D9')
map.drawcoastlines(color='#6D5F47', linewidth=.4)
map.drawcountries(color='#6D5F47', linewidth=.4)
# lat/lon coordinates of top ten indian cities
lngs=array(ten_pop_cities['longitude'])
lats=array(ten_pop_cities['latitude'])
populations = array(ten_pop_cities['population_female'])
nc=array(ten_pop_cities['name_of_city'])
#scale populations to emphasise different relative pop sizes
s_populations = [p/5000 for p in populations]
# compute the native map projection coordinates for cities
x,y = map(lngs,lats)
#scatter scaled circles at the city locations
map.scatter(
x,
y,
s=s_populations, #size
c=s_populations, #color
marker='o', #symbol
alpha=0.75, #transparency
zorder = 2, #plotting order
cmap = cm.Dark2
)
# plot population labels of the ten cities.
for name, xpt, ypt in zip(nc, x, y):
plt.text(
xpt+60000,
ypt+30000,
name,
fontsize= 15,
fontweight='bold',
horizontalalignment='center',
verticalalignment='center',
zorder = 3,
)
#add a title and display the map on screen
plt.title('Top Ten Cities of India By female population', fontsize=20)
plt.show()
C:\Users\rabiulawal\Anaconda3\lib\site-packages\mpl_toolkits\basemap\__init__.py:3236: MatplotlibDeprecationWarning: The ishold function was deprecated in version 2.0. b = ax.ishold() C:\Users\rabiulawal\Anaconda3\lib\site-packages\mpl_toolkits\basemap\__init__.py:3245: MatplotlibDeprecationWarning: axes.hold is deprecated. See the API Changes document (http://matplotlib.org/api/api_changes.html) for more details. ax.hold(b)
lit_cities = df.sort_values('literates_total', ascending = False)
ten_lit_cities = lit_cities.head(10)
ten_lit_cities
name_of_city | state_code | state_name | dist_code | population_total | population_male | population_female | 0-6_population_total | 0-6_population_male | 0-6_population_female | ... | child_sex_ratio | effective_literacy_rate_total | effective_literacy_rate_male | effective_literacy_rate_female | location | total_graduates | male_graduates | female_graduates | latitude | longitude | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
185 | Greater Mumbai | 27 | MAHARASHTRA | 99 | 12478447 | 6736815 | 5741632 | 1139146 | 599007 | 540139 | ... | 902 | 90.28 | 93.32 | 86.70 | 19.0760,72.8777 | 1802371 | 964964 | 837407 | 19.0760 | 72.8777 |
141 | Delhi | 7 | NCT OF DELHI | 99 | 11007835 | 5871362 | 5136473 | 1209275 | 647938 | 561337 | ... | 866 | 87.60 | 91.44 | 83.20 | 28.7041,77.1025 | 2221137 | 1210040 | 1011097 | 28.7041 | 77.1025 |
72 | Bengaluru | 29 | KARNATAKA | 18 | 8425970 | 4401299 | 4024671 | 862493 | 444639 | 417854 | ... | 940 | 89.59 | 92.63 | 86.25 | 12.9716,77.5946 | 1591163 | 908363 | 682800 | 12.9716 | 77.5946 |
184 | Greater Hyderabad | 28 | ANDHRA PRADESH | 99 | 6809970 | 3500802 | 3309168 | 725816 | 373794 | 352022 | ... | 942 | 82.96 | 85.96 | 79.79 | 17.3850,78.4867 | 1164149 | 685402 | 478747 | 17.3850 | 78.4867 |
7 | Ahmadabad | 24 | GUJARAT | 7 | 5570585 | 2935869 | 2634716 | 589076 | 317917 | 271159 | ... | 853 | 89.62 | 93.96 | 84.81 | 23.022505,72.5713621 | 769858 | 435267 | 334591 | 23.022505 | 72.5713621 |
119 | Chennai | 33 | TAMIL NADU | 2 | 4681087 | 2357633 | 2323454 | 418541 | 213084 | 205457 | ... | 964 | 90.33 | 93.47 | 87.16 | 13.0826802,80.2707184 | 879695 | 487428 | 392267 | 13.0826802 | 80.2707184 |
274 | Kolkata | 19 | WEST BENGAL | 16 | 4486679 | 2362662 | 2124017 | 300052 | 155475 | 144577 | ... | 930 | 87.14 | 89.08 | 84.98 | 22.572646,88.363895 | 818476 | 461615 | 356861 | 22.572646 | 88.363895 |
449 | Surat | 24 | GUJARAT | 25 | 4462002 | 2538243 | 1923759 | 531522 | 293208 | 238314 | ... | 813 | 89.03 | 92.76 | 84.05 | 21.1702401,72.8310607 | 278795 | 160566 | 118229 | 21.1702401 | 72.8310607 |
380 | Pune | 27 | MAHARASHTRA | 25 | 3115431 | 1602137 | 1513294 | 324572 | 171152 | 153420 | ... | 896 | 91.61 | 95.13 | 87.91 | 18.5204303,73.8567437 | 656508 | 349022 | 307486 | 18.5204303 | 73.8567437 |
225 | Jaipur | 8 | RAJASTHAN | 12 | 3073350 | 1619280 | 1454070 | 378788 | 204320 | 174468 | ... | 854 | 84.34 | 90.61 | 77.41 | 26.9124336,75.7872709 | 533148 | 319107 | 214041 | 26.9124336 | 75.7872709 |
10 rows × 24 columns
from mpl_toolkits.basemap import Basemap
plt.subplots(figsize=(20, 15))
map = Basemap(width=1200000,height=900000,projection='lcc',resolution='l',
llcrnrlon=67,llcrnrlat=5,urcrnrlon=99,urcrnrlat=37,lat_0=28,lon_0=77)
#set a background colour
map.drawmapboundary(fill_color='#85A6D9')
# draw coastlines, country boundaries, fill continents.
map.fillcontinents(color='white',lake_color='#85A6D9')
map.drawcoastlines(color='#6D5F47', linewidth=.4)
map.drawcountries(color='#6D5F47', linewidth=.4)
# lat/lon coordinates of top ten indian cities
lngs=array(ten_lit_cities['longitude'])
lats=array(ten_lit_cities['latitude'])
populations = array(ten_lit_cities['literates_total'])
nc=array(ten_lit_cities['name_of_city'])
#scale populations to emphasise different relative pop sizes
s_populations = [p/5000 for p in populations]
# compute the native map projection coordinates for cities
x,y = map(lngs,lats)
#scatter scaled circles at the city locations
map.scatter(
x,
y,
s=s_populations, #size
c=s_populations, #color
marker='o', #symbol
alpha=0.75, #transparency
zorder = 2, #plotting order
cmap = cm.Dark2
)
# plot population labels of the ten cities.
for name, xpt, ypt in zip(nc, x, y):
plt.text(
xpt+60000,
ypt+30000,
name,
fontsize= 15,
fontweight='bold',
horizontalalignment='center',
verticalalignment='center',
zorder = 3,
)
#add a title and display the map on screen
plt.title('Top Ten Cities of India by total literates', fontsize=20)
plt.show()
C:\Users\rabiulawal\Anaconda3\lib\site-packages\mpl_toolkits\basemap\__init__.py:3236: MatplotlibDeprecationWarning: The ishold function was deprecated in version 2.0. b = ax.ishold() C:\Users\rabiulawal\Anaconda3\lib\site-packages\mpl_toolkits\basemap\__init__.py:3245: MatplotlibDeprecationWarning: axes.hold is deprecated. See the API Changes document (http://matplotlib.org/api/api_changes.html) for more details. ax.hold(b)
fig = plt.figure(figsize=(15,5))
lit_states = df.groupby(['state_name'])['effective_literacy_rate_total'].mean().sort_values(ascending = False)
lit_states.plot(kind = 'bar', fontsize = 12)
plt.grid(b=True, which='both', color='Black',linestyle='-')
plt.xlabel('Sates', fontsize = 15)
plt.ylabel('effective literates total', fontsize = 15)
plt.title('States by Average effective literates rate', fontsize = 20)
plt.show()
graduate_states = df[["state_name","total_graduates","male_graduates","female_graduates"]].groupby("state_name").agg({"total_graduates":np.sum,
"male_graduates":np.sum,
"female_graduates":np.sum})
sort_graduates = graduate_states.sort_values(by=['total_graduates'], ascending=False)
sort_graduates.plot(kind = 'bar', figsize=(17,5), fontsize = 12, alpha = 1, colormap='Set2')
sort_graduates['total_graduates'].plot(kind='line',color = 'orange',linewidth=2.0, use_index = True)
plt.xticks(rotation = 90)
plt.xlabel('Sates', fontsize = 15)
plt.ylabel('number of Grduates in different states', fontsize = 15)
plt.title('States by number of total graduates in 500 cities', fontsize = 20)
plt.show()
df['graduate_ratio'] = (100 * df['total_graduates'] ) / df['population_total']
grad_rat_states = df.groupby(['state_name'])['graduate_ratio'].mean().sort_values(ascending = False)
fig = plt.figure(figsize=(15,5))
grad_rat_states.plot(kind = 'bar', fontsize = 12, alpha = 1, color='Orange')
plt.grid(b=True, which='both', color='Black',linestyle='-')
plt.xlabel('Total Graduates', fontsize = 15)
plt.ylabel('Sates', fontsize = 15)
plt.title('States by total graduates percentage in entire population', fontsize = 20)
plt.show()
df['male_graduate_ratio'] = (100 * df['male_graduates'] ) / df['population_male']
grad_rat_states = df.groupby(['state_name'])['male_graduate_ratio'].mean().sort_values(ascending = False)
fig = plt.figure(figsize=(15,5))
grad_rat_states.plot(kind = 'bar', fontsize = 12, alpha = 1, color='Orange')
plt.grid(b=True, which='both', color='Black',linestyle='-')
plt.xlabel('Total Male Graduates', fontsize = 15)
plt.ylabel('Sates', fontsize = 15)
plt.title('States by male graduates percentage in male population', fontsize = 20)
plt.show()
df['female_graduate_ratio'] = (100 *df['female_graduates'] ) / df['population_male']
grad_rat_states = df.groupby(['state_name'])['female_graduate_ratio'].mean().sort_values(ascending = False)
fig = plt.figure(figsize=(15,5))
grad_rat_states.plot(kind = 'bar', fontsize = 12, alpha = 1, color='Orange')
plt.grid(b=True, which='both', color='Black',linestyle='-')
plt.xlabel('Total Female Graduates', fontsize = 15)
plt.ylabel('Sates', fontsize = 15)
plt.title('States by female graduates percentage in female population', fontsize = 20)
plt.show()
cities = df.sort_values(by = 'population_total', ascending = False)
fifty_pop_cities = cities.head(50)
plt.figure(figsize = (15,15))
ax = sns.barplot(data = fifty_pop_cities, y = 'name_of_city', x = 'population_total', palette = 'Paired')
ax.set(xlabel='Population in million', ylabel='Name of City')
sns.plt.title('Top 50 cities by population', fontsize = 20)
plt.show()
df['total_grad_per'] = (100*df['total_graduates'])/df['population_total']
cities = df.sort_values(by = 'total_grad_per', ascending = False)
fifty_cities = cities.head(50)
plt.figure(figsize = (15,15))
ax = sns.barplot(data = fifty_cities, y = 'name_of_city', x = 'total_grad_per', palette = 'Paired')
ax.set(xlabel='Population in million', ylabel='Name of City')
sns.plt.title('Total graduate percentage in entire population of top 50 cities', fontsize = 20)
plt.show()
plt.figure(figsize = (12,6))
top_female_grad = df.sort_values(by=['female_graduates'],ascending=False).head(15)
sns.barplot(x='name_of_city', y='female_graduates', data=top_female_grad, palette = 'viridis')
plt.xticks(rotation = 90)
plt.xlabel('City', fontsize =15)
plt.title('Top 15 cities where female gradutes lives', fontsize = 20)
<matplotlib.text.Text at 0xc9972b0>
fig = plt.figure(figsize=(15,5))
df['diff_grad'] = df['male_graduates'] - df['female_graduates']
grad_disc_states = df.groupby('state_name')['diff_grad'].mean()
grad_disc_states.plot(kind = 'bar', color='orange')
plt.title('Difference in average number of male and female graduates', fontsize = 20)
plt.xlabel('State', fontsize = 15)
plt.ylabel('Diff', fontsize = 15)
plt.show()
fig = plt.figure(figsize=(15,5))
df['diff_child'] = df['0-6_population_male'] - df['0-6_population_female']
child_disc_states = df.groupby('state_name')['diff_child'].mean()
child_disc_states.plot(kind = 'bar', color='orange')
plt.title('Difference in average number of male and female kids (Age 0-6)', fontsize = 20)
plt.xlabel('State', fontsize = 15)
plt.ylabel('Diff', fontsize = 15)
plt.show()
fig = plt.figure(figsize=(15,5))
df['diff_lit'] = df['literates_male'] - df['literates_female']
lit_disc_states = df.groupby('state_name')['diff_lit'].mean()
lit_disc_states.plot(kind = 'bar', color='orange')
plt.title('Difference in average number of male and female literates', fontsize = 20)
plt.xlabel('State', fontsize = 15)
plt.ylabel('Diff', fontsize = 15)
plt.show()
fig = plt.figure(figsize=(15,5))
df['diff_efec_lit'] = df['effective_literacy_rate_male'] - df['effective_literacy_rate_female']
efec_lit_disc_states = df.groupby('state_name')['diff_efec_lit'].mean()
efec_lit_disc_states.plot(kind = 'bar', color='orange')
plt.title('Difference in average effective literates of male and female ', fontsize = 20)
plt.xlabel('State', fontsize = 15)
plt.ylabel('Diff', fontsize = 15)
plt.show()
fig = plt.figure(figsize=(15,5))
sex_rat_states = df.groupby('state_name')['sex_ratio'].mean().sort_values(ascending=False)
sex_rat_states.plot(kind="bar", fontsize = 12, color = 'orange')
plt.grid(b=True, which='both', color='Black',linestyle='-')
plt.title('Sex ratio across states', fontsize = 20)
plt.show ()
states = df[['literates_male', 'literates_female',
'state_name']].groupby('state_name').sum().sort_values(['literates_male', 'literates_female'], ascending=False)
plt.figure(figsize=(15, 5))
sns.set_palette(sns.color_palette("muted"))
states['literates_male'].plot(kind = 'line', ls="--", label = 'male')
states['literates_female'].plot(kind = 'line', label = 'female')
plt.xticks(range(len(states.index)), list(states.index), rotation=90)
plt.xlabel('States')
plt.title('Male vs female literates across states', fontsize = 20)
plt.show()
states = df[['male_graduates', 'female_graduates',
'state_name']].groupby('state_name').sum().sort_values(['male_graduates', 'female_graduates'], ascending=False)
plt.figure(figsize=(15, 5))
sns.set_palette(sns.color_palette("muted"))
states['male_graduates'].plot(kind = 'line', ls="--", label = 'male')
states['female_graduates'].plot(kind = 'line', label = 'female')
plt.xticks(range(len(states.index)), list(states.index), rotation=90)
plt.xlabel('States')
plt.title('Male vs female graduates across states', fontsize = 20)
plt.show()
child_states = df[['state_name', '0-6_population_total', '0-6_population_male', '0-6_population_female']].groupby('state_name').agg({'0-6_population_total':np.average,
'0-6_population_male':np.average,
'0-6_population_female':np.average}).sort('0-6_population_total', ascending=False)
child_states.plot(kind='bar', figsize=(16,5), alpha = 0.7, colormap = 'Set1', width=0.6)
plt.title('most children lives region across sates', fontsize = 20)
C:\Users\rabiulawal\Anaconda3\lib\site-packages\ipykernel\__main__.py:3: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....) app.launch_new_instance()
<matplotlib.text.Text at 0xbae36d8>
print("Top States having better Total Literacy Rates, Sex-Ratio and Graduation Ratio are as follows:")
los = [lit_states.head(20),sex_rat_states.head(20),grad_rat_states.head(20)]
pd.concat(los, axis=1, join='inner')
Top States having better Total Literacy Rates, Sex-Ratio and Graduation Ratio are as follows:
effective_literacy_rate_total | sex_ratio | female_graduate_ratio | |
---|---|---|---|
state_name | |||
MANIPUR | 91.700000 | 1055.000000 | 23.241113 |
MEGHALAYA | 92.340000 | 1042.000000 | 18.059919 |
KERALA | 95.444286 | 1066.714286 | 16.919466 |
PUDUCHERRY | 89.745000 | 1035.000000 | 14.630500 |
ASSAM | 91.512500 | 950.750000 | 12.329167 |
TAMIL NADU | 90.093125 | 1001.031250 | 12.262423 |
CHHATTISGARH | 87.138889 | 950.222222 | 11.992663 |
ORISSA | 88.301000 | 930.300000 | 11.848383 |
TRIPURA | 93.880000 | 1002.000000 | 11.269638 |
WEST BENGAL | 87.890328 | 943.262295 | 10.505134 |
MADHYA PRADESH | 85.424687 | 914.812500 | 10.291140 |
MAHARASHTRA | 89.827907 | 920.511628 | 10.053646 |
fig = plt.figure(figsize=(12,6))
sns.regplot(x="sex_ratio", y='graduate_ratio', data=df)
plt.title('Linear relation between sex ration and graduate ratio', fontsize = 20)
<matplotlib.text.Text at 0xc175320>
poor_states = df[['population_total', 'literates_total', 'total_graduates', 'sex_ratio', 'graduate_ratio',
'state_name']].groupby('state_name').sum().sort_values(['population_total', 'literates_total', 'total_graduates', 'sex_ratio', 'graduate_ratio'], ascending = True)
poor_states['sex_ratio'] = poor_states['sex_ratio']*50
poor_states['graduate_ratio'] = poor_states['graduate_ratio']*1000
poor_states.head(5).plot(kind='bar', figsize=(12,6), alpha = 0.7, colormap = 'viridis', width = 0.7)
plt.xticks(rotation=90, fontsize = 12)
plt.xlabel('States', fontsize = 15)
plt.title('Top 5 undeveloped states of India', fontsize = 20)
plt.show()
undeveloped_cities = df.sort_values(by = ['total_graduates', 'literates_total', 'sex_ratio', 'graduate_ratio'], ascending = False)
undeveloped_cities = pop_cities.tail(10)
undeveloped_cities
name_of_city | state_code | state_name | dist_code | population_total | population_male | population_female | 0-6_population_total | 0-6_population_male | 0-6_population_female | ... | child_sex_ratio | effective_literacy_rate_total | effective_literacy_rate_male | effective_literacy_rate_female | location | total_graduates | male_graduates | female_graduates | latitude | longitude | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
34 | Aurangabad | 10 | BIHAR | 33 | 101520 | 53307 | 48213 | 14292 | 7548 | 6744 | ... | 893 | 86.89 | 92.52 | 80.68 | 24.7457189,84.3804888 | 13141 | 9371 | 3770 | 24.7457189 | 84.3804888 |
77 | Bhadreswar | 19 | WEST BENGAL | 12 | 101334 | 53318 | 48016 | 8981 | 4674 | 4307 | ... | 921 | 86.89 | 91.47 | 81.80 | 22.8275529,88.3456516 | 9225 | 5743 | 3482 | 22.8275529 | 88.3456516 |
257 | Kasganj | 9 | UTTAR PRADESH | 71 | 101241 | 53507 | 47734 | 13043 | 7139 | 5904 | ... | 827 | 78.56 | 83.97 | 72.57 | 27.8129344,78.649785 | 13306 | 7833 | 5473 | 27.8129344 | 78.649785 |
247 | Kalyani | 19 | WEST BENGAL | 10 | 100620 | 50867 | 49753 | 7604 | 3890 | 3714 | ... | 955 | 88.75 | 92.79 | 84.63 | 22.9750855,88.4345078 | 17457 | 9775 | 7682 | 22.9750855 | 88.4345078 |
331 | Nagaur | 8 | RAJASTHAN | 14 | 100618 | 52220 | 48398 | 12974 | 6914 | 6060 | ... | 876 | 73.05 | 82.06 | 63.42 | 27.1991222,73.740924 | 7812 | 5384 | 2428 | 27.1991222 | 73.740924 |
376 | Port Blair | 35 | ANDAMAN & NICOBAR ISLANDS | 3 | 100608 | 53247 | 47361 | 9358 | 4852 | 4506 | ... | 929 | 89.76 | 92.79 | 86.34 | 11.6233774,92.7264828 | 12420 | 6717 | 5703 | 11.6233774 | 92.7264828 |
136 | Datia | 23 | MADHYA PRADESH | 5 | 100466 | 52931 | 47535 | 12218 | 6553 | 5665 | ... | 864 | 80.54 | 87.29 | 73.06 | 25.6653262,78.4609393 | 12717 | 7960 | 4757 | 25.6653262 | 78.4609393 |
211 | Hinganghat | 27 | MAHARASHTRA | 8 | 100416 | 51881 | 48535 | 9681 | 5175 | 4506 | ... | 871 | 94.34 | 97.91 | 90.56 | 20.5505728,78.8411405 | 11256 | 6684 | 4572 | 20.5505728 | 78.8411405 |
53 | Banswara | 8 | RAJASTHAN | 27 | 100128 | 51240 | 48888 | 12244 | 6602 | 5642 | ... | 855 | 86.98 | 92.76 | 81.01 | 23.5461394,74.4349761 | 13852 | 7837 | 6015 | 23.5461394 | 74.4349761 |
332 | Nagda | 23 | MADHYA PRADESH | 18 | 100036 | 51410 | 48626 | 11053 | 5840 | 5213 | ... | 893 | 81.61 | 89.46 | 73.37 | 23.4454599,75.4169918 | 9844 | 5707 | 4137 | 23.4454599 | 75.4169918 |
10 rows × 24 columns