notebook.community

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In [2]:



    


import shapefile
from shapely.geometry import Point, shape
from shapely.geometry.polygon import Polygon



    











In [1]:



    


import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
from math import sqrt

import pprint
import matplotlib.pyplot as plt
import seaborn as sns

from sklearn.model_selection import cross_val_score
from sklearn import metrics

%matplotlib inline



    











In [13]:



    


ricel = pd.read_csv("/Users/macbook/Documents/BTP/Notebook/BTP/Satellite/Rice_large.csv")
ricel.head()



    


















    
Out[13]:










  
    

      
      State_Name
      ind_district
      Crop_Year
      Season
      Crop
      Area
      Production
      Value
    

  
  
    

      0
      Chandigarh
      CHANDIGARH
      2002
      Kharif
      Rice
      140
      700.0
      5.0
    

    

      1
      Chandigarh
      CHANDIGARH
      2003
      Kharif
      Rice
      100
      500.0
      5.0
    

    

      2
      Chandigarh
      CHANDIGARH
      2004
      Kharif
      Rice
      80
      400.0
      5.0
    

    

      3
      Chandigarh
      CHANDIGARH
      2005
      Kharif
      Rice
      50
      250.0
      5.0
    

    

      4
      Chandigarh
      CHANDIGARH
      2006
      Kharif
      Rice
      50
      250.0
      5.0
    

  




















In [14]:



    


a = np.empty((ricel.shape[0],1))*np.NAN
rice = ricel.assign(X1 = a)
rice = rice.assign(X2 = a)
rice.head()



    


















    
Out[14]:










  
    

      
      State_Name
      ind_district
      Crop_Year
      Season
      Crop
      Area
      Production
      Value
      X1
      X2
    

  
  
    

      0
      Chandigarh
      CHANDIGARH
      2002
      Kharif
      Rice
      140
      700.0
      5.0
      NaN
      NaN
    

    

      1
      Chandigarh
      CHANDIGARH
      2003
      Kharif
      Rice
      100
      500.0
      5.0
      NaN
      NaN
    

    

      2
      Chandigarh
      CHANDIGARH
      2004
      Kharif
      Rice
      80
      400.0
      5.0
      NaN
      NaN
    

    

      3
      Chandigarh
      CHANDIGARH
      2005
      Kharif
      Rice
      50
      250.0
      5.0
      NaN
      NaN
    

    

      4
      Chandigarh
      CHANDIGARH
      2006
      Kharif
      Rice
      50
      250.0
      5.0
      NaN
      NaN
    

  




















In [15]:



    


l = rice.shape[0]
for row in range(0,l):
    if row-1<0 or rice.iloc[row,1] != rice.iloc[row-1,1]:
        continue
    else:
        rice.iloc[row,8] = rice.iloc[row-1,6]
        if row-2<0 or rice.iloc[row,1] != rice.iloc[row-2,1]:
            continue
        else:
            rice.iloc[row,9] = rice.iloc[row-2,6]



    











In [16]:



    


rice.head()



    


















    
Out[16]:










  
    

      
      State_Name
      ind_district
      Crop_Year
      Season
      Crop
      Area
      Production
      Value
      X1
      X2
    

  
  
    

      0
      Chandigarh
      CHANDIGARH
      2002
      Kharif
      Rice
      140
      700.0
      5.0
      NaN
      NaN
    

    

      1
      Chandigarh
      CHANDIGARH
      2003
      Kharif
      Rice
      100
      500.0
      5.0
      700.0
      NaN
    

    

      2
      Chandigarh
      CHANDIGARH
      2004
      Kharif
      Rice
      80
      400.0
      5.0
      500.0
      700.0
    

    

      3
      Chandigarh
      CHANDIGARH
      2005
      Kharif
      Rice
      50
      250.0
      5.0
      400.0
      500.0
    

    

      4
      Chandigarh
      CHANDIGARH
      2006
      Kharif
      Rice
      50
      250.0
      5.0
      250.0
      400.0
    

  




















In [17]:



    


def func(s):
    x = s.strip()
    return x.lower()



    











In [18]:



    


rice['ind_district'] = rice['ind_district'].apply(func)
rice['Season'] = rice['Season'].apply(func)
rice.head()



    


















    
Out[18]:










  
    

      
      State_Name
      ind_district
      Crop_Year
      Season
      Crop
      Area
      Production
      Value
      X1
      X2
    

  
  
    

      0
      Chandigarh
      chandigarh
      2002
      kharif
      Rice
      140
      700.0
      5.0
      NaN
      NaN
    

    

      1
      Chandigarh
      chandigarh
      2003
      kharif
      Rice
      100
      500.0
      5.0
      700.0
      NaN
    

    

      2
      Chandigarh
      chandigarh
      2004
      kharif
      Rice
      80
      400.0
      5.0
      500.0
      700.0
    

    

      3
      Chandigarh
      chandigarh
      2005
      kharif
      Rice
      50
      250.0
      5.0
      400.0
      500.0
    

    

      4
      Chandigarh
      chandigarh
      2006
      kharif
      Rice
      50
      250.0
      5.0
      250.0
      400.0
    

  




















In [19]:



    


ricex = rice[np.isfinite(rice["Production"])]
ricex = ricex[np.isfinite(ricex["X1"])]
ricex = ricex[np.isfinite(ricex["X2"])]
ricex.head()



    


















    
Out[19]:










  
    

      
      State_Name
      ind_district
      Crop_Year
      Season
      Crop
      Area
      Production
      Value
      X1
      X2
    

  
  
    

      2
      Chandigarh
      chandigarh
      2004
      kharif
      Rice
      80
      400.0
      5.0
      500.0
      700.0
    

    

      3
      Chandigarh
      chandigarh
      2005
      kharif
      Rice
      50
      250.0
      5.0
      400.0
      500.0
    

    

      4
      Chandigarh
      chandigarh
      2006
      kharif
      Rice
      50
      250.0
      5.0
      250.0
      400.0
    

    

      5
      Chandigarh
      chandigarh
      2007
      kharif
      Rice
      50
      250.0
      5.0
      250.0
      250.0
    

    

      6
      Chandigarh
      chandigarh
      2008
      kharif
      Rice
      20
      100.0
      5.0
      250.0
      250.0
    

  




















In [20]:



    


ricex.describe()



    


















    
Out[20]:










  
    

      
      Crop_Year
      Area
      Production
      Value
      X1
      X2
    

  
  
    

      count
      837.000000
      837.000000
      8.370000e+02
      837.000000
      8.370000e+02
      8.370000e+02
    

    

      mean
      2007.005974
      67246.301075
      1.708290e+05
      2.178032
      1.716085e+05
      1.689463e+05
    

    

      std
      2.006261
      70316.628823
      2.300718e+05
      0.905854
      2.330942e+05
      2.319305e+05
    

    

      min
      2004.000000
      1.000000
      2.000000e+00
      0.221533
      2.000000e+00
      0.000000e+00
    

    

      25%
      2005.000000
      9395.000000
      1.400000e+04
      1.567065
      1.400000e+04
      1.248500e+04
    

    

      50%
      2007.000000
      41868.000000
      9.100000e+04
      2.069998
      8.943300e+04
      8.700000e+04
    

    

      75%
      2009.000000
      114107.000000
      2.610000e+05
      2.549000
      2.632940e+05
      2.570980e+05
    

    

      max
      2010.000000
      400000.000000
      1.710000e+06
      6.315789
      1.710000e+06
      1.710000e+06
    

  




















In [21]:



    


ricex.to_csv("Ricep_large.csv")



    











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In [4]:



    


point = Point(0.5, 0.5)
polygon = Polygon([(0, 0), (0, 1), (1, 1), (1, 0)])
print(polygon.contains(point))



    


















    






True

















In [5]:



    


type(polygon)



    


















    
Out[5]:








shapely.geometry.polygon.Polygon

















In [6]:



    


base = "/Users/macbook/Documents/BTP/Satellite/Data/Maps/Districts/Census_2011"



    











In [7]:



    


sf = shapefile.Reader(base+"/2011_Dist.shp")



    











In [28]:



    


type(sf)
print sf.fields



    


















    






[('DeletionFlag', 'C', 1, 0), ['DISTRICT', 'C', 28, 0], ['ST_NM', 'C', 24, 0], ['ST_CEN_CD', 'N', 9, 0], ['DT_CEN_CD', 'N', 9, 0], ['censuscode', 'N', 14, 0]]

















In [37]:



    


records = sf.records()
records



    


















    
Out[37]:








[['Adilabad', 'Andhra Pradesh', 28, 1, 532],
 ['Agra', 'Uttar Pradesh', 9, 15, 146],
 ['Ahmadabad', 'Gujarat', 24, 7, 474],
 ['Ahmadnagar', 'Maharashtra', 27, 26, 522],
 ['Aizawl', 'Mizoram', 15, 3, 283],
 ['Ajmer', 'Rajasthan', 8, 21, 119],
 ['Akola', 'Maharashtra', 27, 5, 501],
 ['Alappuzha', 'Kerala', 32, 11, 598],
 ['Aligarh', 'Uttar Pradesh', 9, 12, 143],
 ['Alirajpur', 'Madhya Pradesh', 23, 48, 465],
 ['Allahabad', 'Uttar Pradesh', 9, 44, 175],
 ['Almora', 'Uttarakhand', 5, 9, 64],
 ['Alwar', 'Rajasthan', 8, 6, 104],
 ['Ambala', 'Haryana', 6, 2, 70],
 ['Ambedkar Nagar', 'Uttar Pradesh', 9, 47, 178],
 ['Amravati', 'Maharashtra', 27, 7, 503],
 ['Amreli', 'Gujarat', 24, 13, 480],
 ['Amritsar', 'Punjab', 3, 15, 49],
 ['Anand', 'Gujarat', 24, 15, 482],
 ['Anantapur', 'Andhra Pradesh', 28, 22, 553],
 ['Anantnag', 'Jammu & Kashmir', 1, 14, 14],
 ['Anjaw', 'Arunanchal Pradesh', 12, 16, 260],
 ['Anugul', 'Odisha', 21, 15, 384],
 ['Anuppur', 'Madhya Pradesh', 23, 44, 461],
 ['Araria', 'Bihar', 10, 7, 209],
 ['Ariyalur', 'Tamil Nadu', 33, 15, 616],
 ['Ashoknagar', 'Madhya Pradesh', 23, 42, 459],
 ['Auraiya', 'Uttar Pradesh', 9, 31, 162],
 ['Aurangabad', 'Bihar', 10, 33, 235],
 ['Aurangabad', 'Maharashtra', 27, 19, 515],
 ['Azamgarh', 'Uttar Pradesh', 9, 60, 191],
 ['Badgam', 'Jammu & Kashmir', 1, 2, 2],
 ['Bagalkot', 'Karnataka', 29, 2, 556],
 ['Bageshwar', 'Uttarakhand', 5, 8, 63],
 ['Baghpat', 'Uttar Pradesh', 9, 8, 139],
 ['Bahraich', 'Uttar Pradesh', 9, 49, 180],
 ['Baksa', 'Assam', 18, 25, 324],
 ['Balaghat', 'Madhya Pradesh', 23, 40, 457],
 ['Balangir', 'Odisha', 21, 24, 393],
 ['Baleshwar', 'Odisha', 21, 8, 377],
 ['Ballia', 'Uttar Pradesh', 9, 62, 193],
 ['Balrampur', 'Uttar Pradesh', 9, 51, 182],
 ['Banas Kantha', 'Gujarat', 24, 2, 469],
 ['Banda', 'Uttar Pradesh', 9, 39, 170],
 ['Bandipore', 'Jammu & Kashmir', 1, 9, 9],
 ['Bangalore Rural', 'Karnataka', 29, 29, 583],
 ['Bangalore', 'Karnataka', 29, 18, 572],
 ['Banka', 'Bihar', 10, 23, 225],
 ['Bankura', 'West Bengal', 19, 13, 339],
 ['Banswara', 'Rajasthan', 8, 27, 125],
 ['Bara Banki', 'Uttar Pradesh', 9, 45, 176],
 ['Bargarh', 'Odisha', 21, 1, 370],
 ['Baramula', 'Jammu & Kashmir', 1, 8, 8],
 ['Baran', 'Rajasthan', 8, 30, 128],
 ['Barddhaman', 'West Bengal', 19, 9, 335],
 ['Bareilly', 'Uttar Pradesh', 9, 19, 150],
 ['Barmer', 'Rajasthan', 8, 17, 115],
 ['Barnala', 'Punjab', 3, 20, 54],
 ['Barpeta', 'Assam', 18, 4, 303],
 ['Barwani', 'Madhya Pradesh', 23, 24, 441],
 ['Bastar', 'Chhattisgarh', 22, 15, 414],
 ['Basti', 'Uttar Pradesh', 9, 54, 185],
 ['Bathinda', 'Punjab', 3, 12, 46],
 ['Bauda', 'Odisha', 21, 22, 391],
 ['Begusarai', 'Bihar', 10, 20, 222],
 ['Belgaum', 'Karnataka', 29, 1, 555],
 ['Bellary', 'Karnataka', 29, 11, 565],
 ['Betul', 'Madhya Pradesh', 23, 30, 447],
 ['Bhadrak', 'Odisha', 21, 9, 378],
 ['Bhagalpur', 'Bihar', 10, 22, 224],
 ['Bhandara', 'Maharashtra', 27, 10, 506],
 ['Bharatpur', 'Rajasthan', 8, 7, 105],
 ['Bharuch', 'Gujarat', 24, 21, 488],
 ['Bhavnagar', 'Gujarat', 24, 14, 481],
 ['Bhilwara', 'Rajasthan', 8, 24, 122],
 ['Bhind', 'Madhya Pradesh', 23, 3, 420],
 ['Bhiwani', 'Haryana', 6, 13, 81],
 ['Bhojpur', 'Bihar', 10, 29, 231],
 ['Bhopal', 'Madhya Pradesh', 23, 27, 444],
 ['Bid', 'Maharashtra', 27, 27, 523],
 ['Bidar', 'Karnataka', 29, 4, 558],
 ['Bijapur', 'Chhattisgarh', 22, 18, 417],
 ['Bijapur', 'Karnataka', 29, 3, 557],
 ['Bijnor', 'Uttar Pradesh', 9, 3, 134],
 ['Bikaner', 'Rajasthan', 8, 3, 101],
 ['Bilaspur', 'Chhattisgarh', 22, 7, 406],
 ['Bilaspur', 'Himachal Pradesh', 2, 8, 30],
 ['Birbhum', 'West Bengal', 19, 8, 334],
 ['Bishnupur', 'Manipur', 14, 4, 275],
 ['Bokaro', 'Jharkhand', 20, 10, 355],
 ['Bongaigaon', 'Assam', 18, 20, 319],
 ['Budaun', 'Uttar Pradesh', 9, 18, 149],
 ['Bulandshahr', 'Uttar Pradesh', 9, 11, 142],
 ['Buldana', 'Maharashtra', 27, 4, 500],
 ['Bundi', 'Rajasthan', 8, 23, 121],
 ['Burhanpur', 'Madhya Pradesh', 23, 50, 467],
 ['Buxar', 'Bihar', 10, 30, 232],
 ['Cachar', 'Assam', 18, 17, 316],
 ['Central', 'NCT of Delhi', 7, 6, 95],
 ['Chamba', 'Himachal Pradesh', 2, 1, 23],
 ['Chamoli', 'Uttarakhand', 5, 2, 57],
 ['Champawat', 'Uttarakhand', 5, 10, 65],
 ['Champhai', 'Mizoram', 15, 4, 284],
 ['Chamrajnagar', 'Karnataka', 29, 24, 578],
 ['Chandauli', 'Uttar Pradesh', 9, 65, 196],
 ['Chandel', 'Manipur', 14, 9, 280],
 ['Chandigarh', 'Chandigarh', 4, 1, 55],
 ['Chandrapur', 'Maharashtra', 27, 13, 509],
 ['Changlang', 'Arunanchal Pradesh', 12, 9, 253],
 ['Chatra', 'Jharkhand', 20, 2, 347],
 ['Chennai', 'Tamil Nadu', 33, 2, 603],
 ['Chhatarpur', 'Madhya Pradesh', 23, 8, 425],
 ['Chhindwara', 'Madhya Pradesh', 23, 38, 455],
 ['Chikkaballapura', 'Karnataka', 29, 28, 582],
 ['Chikmagalur', 'Karnataka', 29, 16, 570],
 ['Chirang', 'Assam', 18, 21, 320],
 ['Chitradurga', 'Karnataka', 29, 12, 566],
 ['Chitrakoot', 'Uttar Pradesh', 9, 40, 171],
 ['Chittaurgarh', 'Rajasthan', 8, 28, 126],
 ['Chittoor', 'Andhra Pradesh', 28, 23, 554],
 ['Churachandpur', 'Manipur', 14, 3, 274],
 ['Churu', 'Rajasthan', 8, 4, 102],
 ['Coimbatore', 'Tamil Nadu', 33, 31, 632],
 ['Cuddalore', 'Tamil Nadu', 33, 16, 617],
 ['Y.s.r.', 'Andhra Pradesh', 28, 20, 551],
 ['Cuttack', 'Odisha', 21, 12, 381],
 ['Dadra & Nagar Haveli', 'Dadara & Nagar Havelli', 26, 1, 496],
 ['Dakshin Bastar Dantewada', 'Chhattisgarh', 22, 17, 416],
 ['Dakshin Dinajpur', 'West Bengal', 19, 5, 331],
 ['Dakshina Kannada', 'Karnataka', 29, 21, 575],
 ['Daman', 'Daman & Diu', 25, 2, 495],
 ['Damoh', 'Madhya Pradesh', 23, 11, 428],
 ['Darbhanga', 'Bihar', 10, 13, 215],
 ['Darjiling', 'West Bengal', 19, 1, 327],
 ['Darrang', 'Assam', 18, 26, 325],
 ['Data Not Available', 'Jammu & Kashmir', 99, 99, 0],
 ['Datia', 'Madhya Pradesh', 23, 5, 422],
 ['Davanagere', 'Karnataka', 29, 13, 567],
 ['Debagarh', 'Odisha', 21, 4, 373],
 ['Dehradun', 'Uttarakhand', 5, 5, 60],
 ['Deoghar', 'Jharkhand', 20, 5, 350],
 ['Deoria', 'Uttar Pradesh', 9, 59, 190],
 ['Dewas', 'Madhya Pradesh', 23, 20, 437],
 ['Dhalai', 'Tripura', 16, 3, 291],
 ['Dhamtari', 'Chhattisgarh', 22, 13, 412],
 ['Dhanbad', 'Jharkhand', 20, 9, 354],
 ['Dhar', 'Madhya Pradesh', 23, 21, 438],
 ['Dharmapuri', 'Tamil Nadu', 33, 29, 630],
 ['Dharwad', 'Karnataka', 29, 8, 562],
 ['Dhaulpur', 'Rajasthan', 8, 8, 106],
 ['Dhemaji', 'Assam', 18, 9, 308],
 ['Dhenkanal', 'Odisha', 21, 14, 383],
 ['Dhubri', 'Assam', 18, 2, 301],
 ['Dhule', 'Maharashtra', 27, 2, 498],
 ['Dibang Valley', 'Arunanchal Pradesh', 12, 13, 257],
 ['Dibrugarh', 'Assam', 18, 11, 310],
 ['Dindigul', 'Tamil Nadu', 33, 11, 612],
 ['Dindori', 'Madhya Pradesh', 23, 36, 453],
 ['Diu', 'Daman & Diu', 25, 1, 494],
 ['Doda', 'Jammu & Kashmir', 1, 16, 16],
 ['Dohad', 'Gujarat', 24, 18, 485],
 ['Dumka', 'Jharkhand', 20, 17, 362],
 ['Dungarpur', 'Rajasthan', 8, 26, 124],
 ['Durg', 'Chhattisgarh', 22, 10, 409],
 ['Dimapur', 'Nagaland', 13, 5, 265],
 ['East', 'NCT of Delhi', 7, 4, 93],
 ['East', 'Sikkim', 11, 4, 244],
 ['East Garo Hills', 'Meghalaya', 17, 2, 294],
 ['East Godavari', 'Andhra Pradesh', 28, 14, 545],
 ['East Kameng', 'Arunanchal Pradesh', 12, 3, 247],
 ['East Khasi Hills', 'Meghalaya', 17, 6, 298],
 ['East Nimar', 'Madhya Pradesh', 23, 49, 466],
 ['East Siang', 'Arunanchal Pradesh', 12, 7, 251],
 ['Ernakulam', 'Kerala', 32, 8, 595],
 ['Erode', 'Tamil Nadu', 33, 9, 610],
 ['Etah', 'Uttar Pradesh', 9, 70, 201],
 ['Etawah', 'Uttar Pradesh', 9, 30, 161],
 ['Faizabad', 'Uttar Pradesh', 9, 46, 177],
 ['Faridabad', 'Haryana', 6, 20, 88],
 ['Faridkot', 'Punjab', 3, 11, 45],
 ['Farrukhabad', 'Uttar Pradesh', 9, 28, 159],
 ['Fatehabad', 'Haryana', 6, 10, 78],
 ['Fatehgarh Sahib', 'Punjab', 3, 6, 40],
 ['Fatehpur', 'Uttar Pradesh', 9, 41, 172],
 ['Firozabad', 'Uttar Pradesh', 9, 16, 147],
 ['Firozpur', 'Punjab', 3, 9, 43],
 ['Gadag', 'Karnataka', 29, 7, 561],
 ['Gajapati', 'Odisha', 21, 20, 389],
 ['Ganderbal', 'Jammu & Kashmir', 1, 11, 11],
 ['Gandhinagar', 'Gujarat', 24, 6, 473],
 ['Ganganagar', 'Rajasthan', 8, 1, 99],
 ['Ganjam', 'Odisha', 21, 19, 388],
 ['Garhchiroli', 'Maharashtra', 27, 12, 508],
 ['Garhwa', 'Jharkhand', 20, 1, 346],
 ['Garhwal', 'Uttarakhand', 5, 6, 61],
 ['Gautam Buddha Nagar', 'Uttar Pradesh', 9, 10, 141],
 ['Gaya', 'Bihar', 10, 34, 236],
 ['Ghaziabad', 'Uttar Pradesh', 9, 9, 140],
 ['Ghazipur', 'Uttar Pradesh', 9, 64, 195],
 ['Giridih', 'Jharkhand', 20, 4, 349],
 ['Goalpara', 'Assam', 18, 3, 302],
 ['Godda', 'Jharkhand', 20, 6, 351],
 ['Golaghat', 'Assam', 18, 14, 313],
 ['Gonda', 'Uttar Pradesh', 9, 52, 183],
 ['Gondiya', 'Maharashtra', 27, 11, 507],
 ['Gopalganj', 'Bihar', 10, 15, 217],
 ['Gorakhpur', 'Uttar Pradesh', 9, 57, 188],
 ['Gulbarga', 'Karnataka', 29, 25, 579],
 ['Gumla', 'Jharkhand', 20, 21, 366],
 ['Guna', 'Madhya Pradesh', 23, 41, 458],
 ['Guntur', 'Andhra Pradesh', 28, 17, 548],
 ['Gurdaspur', 'Punjab', 3, 1, 35],
 ['Gurgaon', 'Haryana', 6, 18, 86],
 ['Gwalior', 'Madhya Pradesh', 23, 4, 421],
 ['Hailakandi', 'Assam', 18, 19, 318],
 ['Hamirpur', 'Himachal Pradesh', 2, 6, 28],
 ['Hamirpur', 'Uttar Pradesh', 9, 37, 168],
 ['Hanumangarh', 'Rajasthan', 8, 2, 100],
 ['Haora', 'West Bengal', 19, 15, 341],
 ['Harda', 'Madhya Pradesh', 23, 31, 448],
 ['Hardoi', 'Uttar Pradesh', 9, 24, 155],
 ['Hardwar', 'Uttarakhand', 5, 13, 68],
 ['Hassan', 'Karnataka', 29, 20, 574],
 ['Mahamaya Nagar', 'Uttar Pradesh', 9, 13, 144],
 ['Haveri', 'Karnataka', 29, 10, 564],
 ['Hazaribagh', 'Jharkhand', 20, 15, 360],
 ['Hingoli', 'Maharashtra', 27, 16, 512],
 ['Hisar', 'Haryana', 6, 12, 80],
 ['Hoshangabad', 'Madhya Pradesh', 23, 32, 449],
 ['Hoshiarpur', 'Punjab', 3, 4, 38],
 ['Hugli', 'West Bengal', 19, 12, 338],
 ['Hyderabad', 'Andhra Pradesh', 28, 5, 536],
 ['Idukki', 'Kerala', 32, 9, 596],
 ['Imphal East', 'Manipur', 14, 7, 278],
 ['Imphal West', 'Manipur', 14, 6, 277],
 ['Indore', 'Madhya Pradesh', 23, 22, 439],
 ['Jabalpur', 'Madhya Pradesh', 23, 34, 451],
 ['Jagatsinghapur', 'Odisha', 21, 11, 380],
 ['Jaintia Hills', 'Meghalaya', 17, 7, 299],
 ['Jaipur', 'Rajasthan', 8, 12, 110],
 ['Jaisalmer', 'Rajasthan', 8, 16, 114],
 ['Jajapur', 'Odisha', 21, 13, 382],
 ['Jalandhar', 'Punjab', 3, 3, 37],
 ['Jalaun', 'Uttar Pradesh', 9, 34, 165],
 ['Jalgaon', 'Maharashtra', 27, 3, 499],
 ['Jalna', 'Maharashtra', 27, 18, 514],
 ['Jalor', 'Rajasthan', 8, 18, 116],
 ['Jalpaiguri', 'West Bengal', 19, 2, 328],
 ['Jammu', 'Jammu & Kashmir', 1, 21, 21],
 ['Jamnagar', 'Gujarat', 24, 10, 477],
 ['Jamtara', 'Jharkhand', 20, 18, 363],
 ['Jamui', 'Bihar', 10, 36, 238],
 ['Janjgir-champa', 'Chhattisgarh', 22, 6, 405],
 ['Jashpur', 'Chhattisgarh', 22, 3, 402],
 ['Jaunpur', 'Uttar Pradesh', 9, 63, 194],
 ['Jhabua', 'Madhya Pradesh', 23, 47, 464],
 ['Jhajjar', 'Haryana', 6, 15, 83],
 ['Jhalawar', 'Rajasthan', 8, 31, 129],
 ['Jhansi', 'Uttar Pradesh', 9, 35, 166],
 ['Jharsuguda', 'Odisha', 21, 2, 371],
 ['Jhunjhunun', 'Rajasthan', 8, 5, 103],
 ['Jodhpur', 'Rajasthan', 8, 15, 113],
 ['Jorhat', 'Assam', 18, 13, 312],
 ['Junagadh', 'Gujarat', 24, 12, 479],
 ['Jyotiba Phule Nagar', 'Uttar Pradesh', 9, 6, 137],
 ['Kachchh', 'Gujarat', 24, 1, 468],
 ['Kaimur (bhabua)', 'Bihar', 10, 31, 233],
 ['Kaithal', 'Haryana', 6, 5, 73],
 ['Kalahandi', 'Odisha', 21, 26, 395],
 ['Kamrup', 'Assam', 18, 22, 321],
 ['Kamrup Metropolitan', 'Assam', 18, 23, 322],
 ['Kancheepuram', 'Tamil Nadu', 33, 3, 604],
 ['Kandhamal', 'Odisha', 21, 21, 390],
 ['Kangra', 'Himachal Pradesh', 2, 2, 24],
 ['Uttar Bastar Kanker', 'Chhattisgarh', 22, 14, 413],
 ['Kannauj', 'Uttar Pradesh', 9, 29, 160],
 ['Kanniyakumari', 'Tamil Nadu', 33, 28, 629],
 ['Kannur', 'Kerala', 32, 2, 589],
 ['Kanpur Dehat', 'Uttar Pradesh', 9, 32, 163],
 ['Kanpur Nagar', 'Uttar Pradesh', 9, 33, 164],
 ['Kansiram Nagar', 'Uttar Pradesh', 9, 71, 202],
 ['Kapurthala', 'Punjab', 3, 2, 36],
 ['Karbi Anglong', 'Assam', 18, 15, 314],
 ['Kargil', 'Jammu & Kashmir', 1, 4, 4],
 ['Karimganj', 'Assam', 18, 18, 317],
 ['Karimnagar', 'Andhra Pradesh', 28, 3, 534],
 ['Karnal', 'Haryana', 6, 6, 74],
 ['Karur', 'Tamil Nadu', 33, 12, 613],
 ['Kasaragod', 'Kerala', 32, 1, 588],
 ['Kathua', 'Jammu & Kashmir', 1, 7, 7],
 ['Katihar', 'Bihar', 10, 10, 212],
 ['Katni', 'Madhya Pradesh', 23, 33, 450],
 ['Kaushambi', 'Uttar Pradesh', 9, 43, 174],
 ['Kendrapara', 'Odisha', 21, 10, 379],
 ['Kendujhar', 'Odisha', 21, 6, 375],
 ['Khagaria', 'Bihar', 10, 21, 223],
 ['Khammam', 'Andhra Pradesh', 28, 10, 541],
 ['Kheda', 'Gujarat', 24, 16, 483],
 ['Kheri', 'Uttar Pradesh', 9, 22, 153],
 ['Khordha', 'Odisha', 21, 17, 386],
 ['Khunti', 'Jharkhand', 20, 20, 365],
 ['Kinnaur', 'Himachal Pradesh', 2, 12, 34],
 ['Kiphire', 'Nagaland', 13, 9, 269],
 ['Kishanganj', 'Bihar', 10, 8, 210],
 ['Kishtwar', 'Jammu & Kashmir', 1, 18, 18],
 ['Koch Bihar', 'West Bengal', 19, 3, 329],
 ['Kodagu', 'Karnataka', 29, 22, 576],
 ['Kodarma', 'Jharkhand', 20, 3, 348],
 ['Kohima', 'Nagaland', 13, 10, 270],
 ['Kokrajhar', 'Assam', 18, 1, 300],
 ['Kolar', 'Karnataka', 29, 27, 581],
 ['Kolasib', 'Mizoram', 15, 2, 282],
 ['Kolhapur', 'Maharashtra', 27, 34, 530],
 ['Kolkata', 'West Bengal', 19, 16, 342],
 ['Kollam', 'Kerala', 32, 13, 600],
 ['Koppal', 'Karnataka', 29, 6, 560],
 ['Koraput', 'Odisha', 21, 29, 398],
 ['Korba', 'Chhattisgarh', 22, 5, 404],
 ['Koriya', 'Chhattisgarh', 22, 1, 400],
 ['Kota', 'Rajasthan', 8, 29, 127],
 ['Kottayam', 'Kerala', 32, 10, 597],
 ['Kozhikode', 'Kerala', 32, 4, 591],
 ['Krishna', 'Andhra Pradesh', 28, 16, 547],
 ['Krishnagiri', 'Tamil Nadu', 33, 30, 631],
 ['Kulgam', 'Jammu & Kashmir', 1, 15, 15],
 ['Kullu', 'Himachal Pradesh', 2, 4, 26],
 ['Kupwara', 'Jammu & Kashmir', 1, 1, 1],
 ['Kurnool', 'Andhra Pradesh', 28, 21, 552],
 ['Kurukshetra', 'Haryana', 6, 4, 72],
 ['Kurung Kumey', 'Arunanchal Pradesh', 12, 12, 256],
 ['Kushinagar', 'Uttar Pradesh', 9, 58, 189],
 ['Lahul & Spiti', 'Himachal Pradesh', 2, 3, 25],
 ['Lakhimpur', 'Assam', 18, 8, 307],
 ['Lakhisarai', 'Bihar', 10, 25, 227],
 ['Lalitpur', 'Uttar Pradesh', 9, 36, 167],
 ['Latehar', 'Jharkhand', 20, 14, 359],
 ['Latur', 'Maharashtra', 27, 28, 524],
 ['Lawangtlai', 'Mizoram', 15, 7, 287],
 ['Leh (ladakh)', 'Jammu & Kashmir', 1, 3, 3],
 ['Lohardaga', 'Jharkhand', 20, 11, 356],
 ['Lohit', 'Arunanchal Pradesh', 12, 15, 259],
 ['Longleng', 'Nagaland', 13, 8, 268],
 ['Lower Dibang Valley', 'Arunanchal Pradesh', 12, 14, 258],
 ['Lower Subansiri', 'Arunanchal Pradesh', 12, 11, 255],
 ['Lucknow', 'Uttar Pradesh', 9, 26, 157],
 ['Ludhiana', 'Punjab', 3, 7, 41],
 ['Lunglei', 'Mizoram', 15, 6, 286],
 ['Madhepura', 'Bihar', 10, 11, 213],
 ['Madhubani', 'Bihar', 10, 5, 207],
 ['Madurai', 'Tamil Nadu', 33, 22, 623],
 ['Maharajganj', 'Uttar Pradesh', 9, 56, 187],
 ['Mahasamund', 'Chhattisgarh', 22, 12, 411],
 ['Mahbubnagar', 'Andhra Pradesh', 28, 7, 538],
 ['Mahe', 'Puducherry', 34, 3, 636],
 ['Mahendragarh', 'Haryana', 6, 16, 84],
 ['Mahesana', 'Gujarat', 24, 4, 471],
 ['Mahoba', 'Uttar Pradesh', 9, 38, 169],
 ['Mainpuri', 'Uttar Pradesh', 9, 17, 148],
 ['Malappuram', 'Kerala', 32, 5, 592],
 ['Maldah', 'West Bengal', 19, 6, 332],
 ['Malkangiri', 'Odisha', 21, 30, 399],
 ['Mamit', 'Mizoram', 15, 1, 281],
 ['Mandi', 'Himachal Pradesh', 2, 5, 27],
 ['Mandla', 'Madhya Pradesh', 23, 37, 454],
 ['Mandsaur', 'Madhya Pradesh', 23, 16, 433],
 ['Mandya', 'Karnataka', 29, 19, 573],
 ['Mansa', 'Punjab', 3, 13, 47],
 ['Marigaon', 'Assam', 18, 5, 304],
 ['Mathura', 'Uttar Pradesh', 9, 14, 145],
 ['Mau', 'Uttar Pradesh', 9, 61, 192],
 ['Mayurbhanj', 'Odisha', 21, 7, 376],
 ['Medak', 'Andhra Pradesh', 28, 4, 535],
 ['Meerut', 'Uttar Pradesh', 9, 7, 138],
 ['Mewat', 'Haryana', 6, 19, 87],
 ['Mirzapur', 'Uttar Pradesh', 9, 68, 199],
 ['Moga', 'Punjab', 3, 8, 42],
 ['Mokokchung', 'Nagaland', 13, 2, 262],
 ['Mon', 'Nagaland', 13, 1, 261],
 ['Moradabad', 'Uttar Pradesh', 9, 4, 135],
 ['Morena', 'Madhya Pradesh', 23, 2, 419],
 ['Muktsar', 'Punjab', 3, 10, 44],
 ['Mumbai', 'Maharashtra', 27, 23, 519],
 ['Mumbai Suburban', 'Maharashtra', 27, 22, 518],
 ['Munger', 'Bihar', 10, 24, 226],
 ['Murshidabad', 'West Bengal', 19, 7, 333],
 ['Muzaffarnagar', 'Uttar Pradesh', 9, 2, 133],
 ['Muzaffarpur', 'Bihar', 10, 14, 216],
 ['Mysore', 'Karnataka', 29, 23, 577],
 ['Nabarangapur', 'Odisha', 21, 28, 397],
 ['Nadia', 'West Bengal', 19, 10, 336],
 ['Nagaon', 'Assam', 18, 6, 305],
 ['Nagaur', 'Rajasthan', 8, 14, 112],
 ['Nagpur', 'Maharashtra', 27, 9, 505],
 ['Nainital', 'Uttarakhand', 5, 11, 66],
 ['Nalanda', 'Bihar', 10, 27, 229],
 ['Nalbari', 'Assam', 18, 24, 323],
 ['Nalgonda', 'Andhra Pradesh', 28, 8, 539],
 ['Namakkal', 'Tamil Nadu', 33, 8, 609],
 ['Nanded', 'Maharashtra', 27, 15, 511],
 ['Nandurbar', 'Maharashtra', 27, 1, 497],
 ['Narayanpur', 'Chhattisgarh', 22, 16, 415],
 ['Narmada', 'Gujarat', 24, 20, 487],
 ['Narsimhapur', 'Madhya Pradesh', 23, 35, 452],
 ['Nashik', 'Maharashtra', 27, 20, 516],
 ['Navsari', 'Gujarat', 24, 23, 490],
 ['Nawada', 'Bihar', 10, 35, 237],
 ['Nayagarh', 'Odisha', 21, 16, 385],
 ['Neemuch', 'Madhya Pradesh', 23, 15, 432],
 ['Sri Potti Sriramulu Nellore', 'Andhra Pradesh', 28, 19, 550],
 ['New Delhi', 'NCT of Delhi', 7, 5, 94],
 ['Nizamabad', 'Andhra Pradesh', 28, 2, 533],
 ['North', 'NCT of Delhi', 7, 2, 91],
 ['North', 'Sikkim', 11, 1, 241],
 ['North 24 Parganas', 'West Bengal', 19, 11, 337],
 ['Dima Hasao', 'Assam', 18, 16, 315],
 ['North East', 'NCT of Delhi', 7, 3, 92],
 ['North Goa', 'Goa', 30, 1, 585],
 ['North Tripura', 'Tripura', 16, 4, 292],
 ['North West', 'NCT of Delhi', 7, 1, 90],
 ['Nuapada', 'Odisha', 21, 25, 394],
 ['Osmanabad', 'Maharashtra', 27, 29, 525],
 ['Pakur', 'Jharkhand', 20, 8, 353],
 ['Palakkad', 'Kerala', 32, 6, 593],
 ['Palamu', 'Jharkhand', 20, 13, 358],
 ['Pali', 'Rajasthan', 8, 20, 118],
 ['Palwal', 'Haryana', 6, 21, 89],
 ['Panch Mahals', 'Gujarat', 24, 17, 484],
 ['Panchkula', 'Haryana', 6, 1, 69],
 ['Panipat', 'Haryana', 6, 7, 75],
 ['Panna', 'Madhya Pradesh', 23, 9, 426],
 ['Papum Pare', 'Arunanchal Pradesh', 12, 4, 248],
 ['Parbhani', 'Maharashtra', 27, 17, 513],
 ['Pashchim Champaran', 'Bihar', 10, 1, 203],
 ['Pashchim Medinipur', 'West Bengal', 19, 18, 344],
 ['Pashchimi Singhbhum', 'Jharkhand', 20, 23, 368],
 ['Patan', 'Gujarat', 24, 3, 470],
 ['Pathanamthitta', 'Kerala', 32, 12, 599],
 ['Patiala', 'Punjab', 3, 14, 48],
 ['Patna', 'Bihar', 10, 28, 230],
 ['Perambalur', 'Tamil Nadu', 33, 14, 615],
 ['Peren', 'Nagaland', 13, 11, 271],
 ['Phek', 'Nagaland', 13, 6, 266],
 ['Pilibhit', 'Uttar Pradesh', 9, 20, 151],
 ['Pithoragarh', 'Uttarakhand', 5, 7, 62],
 ['Porbandar', 'Gujarat', 24, 11, 478],
 ['Prakasam', 'Andhra Pradesh', 28, 18, 549],
 ['Pratapgarh', 'Rajasthan', 8, 33, 131],
 ['Pratapgarh', 'Uttar Pradesh', 9, 42, 173],
 ['Pudukkottai', 'Tamil Nadu', 33, 20, 621],
 ['Pulwama', 'Jammu & Kashmir', 1, 12, 12],
 ['Punch', 'Jammu & Kashmir', 1, 5, 5],
 ['Pune', 'Maharashtra', 27, 25, 521],
 ['Purba Champaran', 'Bihar', 10, 2, 204],
 ['Purba Medinipur', 'West Bengal', 19, 19, 345],
 ['Purbi Singhbhum', 'Jharkhand', 20, 12, 357],
 ['Puri', 'Odisha', 21, 18, 387],
 ['Purnia', 'Bihar', 10, 9, 211],
 ['Puruliya', 'West Bengal', 19, 14, 340],
 ['Rae Bareli', 'Uttar Pradesh', 9, 27, 158],
 ['Raichur', 'Karnataka', 29, 5, 559],
 ['Raigarh', 'Chhattisgarh', 22, 4, 403],
 ['Raigarh', 'Maharashtra', 27, 24, 520],
 ['Raipur', 'Chhattisgarh', 22, 11, 410],
 ['Raisen', 'Madhya Pradesh', 23, 29, 446],
 ['Rajouri', 'Jammu & Kashmir', 1, 6, 6],
 ['Rajgarh', 'Madhya Pradesh', 23, 25, 442],
 ['Rajkot', 'Gujarat', 24, 9, 476],
 ['Rajnandgaon', 'Chhattisgarh', 22, 9, 408],
 ['Rajsamand', 'Rajasthan', 8, 25, 123],
 ['Ramanagara', 'Karnataka', 29, 30, 584],
 ['Ramanathapuram', 'Tamil Nadu', 33, 25, 626],
 ['Ramban', 'Jammu & Kashmir', 1, 17, 17],
 ['Ramgarh', 'Jharkhand', 20, 16, 361],
 ['Rampur', 'Uttar Pradesh', 9, 5, 136],
 ['Ranchi', 'Jharkhand', 20, 19, 364],
 ['Rangareddy', 'Andhra Pradesh', 28, 6, 537],
 ['Ratlam', 'Madhya Pradesh', 23, 17, 434],
 ['Ratnagiri', 'Maharashtra', 27, 32, 528],
 ['Rayagada', 'Odisha', 21, 27, 396],
 ['Reasi', 'Jammu & Kashmir', 1, 20, 20],
 ['Rewa', 'Madhya Pradesh', 23, 13, 430],
 ['Rewari', 'Haryana', 6, 17, 85],
 ['Ri Bhoi', 'Meghalaya', 17, 5, 297],
 ['Rohtak', 'Haryana', 6, 14, 82],
 ['Rohtas', 'Bihar', 10, 32, 234],
 ['Rudraprayag', 'Uttarakhand', 5, 3, 58],
 ['Rupnagar', 'Punjab', 3, 17, 51],
 ['Sabar Kantha', 'Gujarat', 24, 5, 472],
 ['Sagar', 'Madhya Pradesh', 23, 10, 427],
 ['Saharanpur', 'Uttar Pradesh', 9, 1, 132],
 ['Saharsa', 'Bihar', 10, 12, 214],
 ['Sahibganj', 'Jharkhand', 20, 7, 352],
 ['Sahibzada Ajit Singh Nagar', 'Punjab', 3, 18, 52],
 ['Saiha', 'Mizoram', 15, 8, 288],
 ['Salem', 'Tamil Nadu', 33, 7, 608],
 ['Samastipur', 'Bihar', 10, 19, 221],
 ['Samba', 'Jammu & Kashmir', 1, 22, 22],
 ['Sambalpur', 'Odisha', 21, 3, 372],
 ['Sangli', 'Maharashtra', 27, 35, 531],
 ['Sangrur', 'Punjab', 3, 19, 53],
 ['Sant Kabir Nagar', 'Uttar Pradesh', 9, 55, 186],
 ['Sant Ravi Das Nagar(bhadohi)', 'Uttar Pradesh', 9, 67, 198],
 ['Saraikela-kharsawan', 'Jharkhand', 20, 24, 369],
 ['Saran (chhapra)', 'Bihar', 10, 17, 219],
 ['Satara', 'Maharashtra', 27, 31, 527],
 ['Satna', 'Madhya Pradesh', 23, 12, 429],
 ['Sehore', 'Madhya Pradesh', 23, 28, 445],
 ['Senapati', 'Manipur', 14, 1, 272],
 ['Seoni', 'Madhya Pradesh', 23, 39, 456],
 ['Serchhip', 'Mizoram', 15, 5, 285],
 ['Shahdol', 'Madhya Pradesh', 23, 43, 460],
 ['Shahid Bhagat Singh Nagar', 'Punjab', 3, 5, 39],
 ['Shahjahanpur', 'Uttar Pradesh', 9, 21, 152],
 ['Shajapur', 'Madhya Pradesh', 23, 19, 436],
 ['Sheikhpura', 'Bihar', 10, 26, 228],
 ['Sheohar', 'Bihar', 10, 3, 205],
 ['Sheopur', 'Madhya Pradesh', 23, 1, 418],
 ['Shimla', 'Himachal Pradesh', 2, 11, 33],
 ['Shimoga', 'Karnataka', 29, 14, 568],
 ['Shivpuri', 'Madhya Pradesh', 23, 6, 423],
 ['Shrawasti', 'Uttar Pradesh', 9, 50, 181],
 ['Shupiyan', 'Jammu & Kashmir', 1, 13, 13],
 ['Sivasagar', 'Assam', 18, 12, 311],
 ['Siddharth Nagar', 'Uttar Pradesh', 9, 53, 184],
 ['Sidhi', 'Madhya Pradesh', 23, 45, 462],
 ['Sikar', 'Rajasthan', 8, 13, 111],
 ['Simdega', 'Jharkhand', 20, 22, 367],
 ['Sindhudurg', 'Maharashtra', 27, 33, 529],
 ['Singrauli', 'Madhya Pradesh', 23, 46, 463],
 ['Sirmaur', 'Himachal Pradesh', 2, 10, 32],
 ['Sirohi', 'Rajasthan', 8, 19, 117],
 ['Sirsa', 'Haryana', 6, 11, 79],
 ['Sitamarhi', 'Bihar', 10, 4, 206],
 ['Sitapur', 'Uttar Pradesh', 9, 23, 154],
 ['Sivaganga', 'Tamil Nadu', 33, 21, 622],
 ['Siwan', 'Bihar', 10, 16, 218],
 ['Solan', 'Himachal Pradesh', 2, 9, 31],
 ['Solapur', 'Maharashtra', 27, 30, 526],
 ['Subarnapur', 'Odisha', 21, 23, 392],
 ['Sonbhadra', 'Uttar Pradesh', 9, 69, 200],
 ['Sonipat', 'Haryana', 6, 8, 76],
 ['Sonitpur', 'Assam', 18, 7, 306],
 ['South', 'NCT of Delhi', 7, 9, 98],
 ['South', 'Sikkim', 11, 3, 243],
 ['South 24 Parganas', 'West Bengal', 19, 17, 343],
 ['South Garo Hills', 'Meghalaya', 17, 3, 295],
 ['South Goa', 'Goa', 30, 2, 586],
 ['South Tripura', 'Tripura', 16, 2, 290],
 ['South West', 'NCT of Delhi', 7, 8, 97],
 ['Srikakulam', 'Andhra Pradesh', 28, 11, 542],
 ['Srinagar', 'Jammu & Kashmir', 1, 10, 10],
 ['Sultanpur', 'Uttar Pradesh', 9, 48, 179],
 ['Sundargarh', 'Odisha', 21, 5, 374],
 ['Supaul', 'Bihar', 10, 6, 208],
 ['Surat', 'Gujarat', 24, 25, 492],
 ['Surendranagar', 'Gujarat', 24, 8, 475],
 ['Surguja', 'Chhattisgarh', 22, 2, 401],
 ['Tamenglong', 'Manipur', 14, 2, 273],
 ['Tarn Taran', 'Punjab', 3, 16, 50],
 ['Tawang', 'Arunanchal Pradesh', 12, 1, 245],
 ['Tehri Garhwal', 'Uttarakhand', 5, 4, 59],
 ['Thane', 'Maharashtra', 27, 21, 517],
 ['Thanjavur', 'Tamil Nadu', 33, 19, 620],
 ['The Dangs', 'Gujarat', 24, 22, 489],
 ['The Nilgiris', 'Tamil Nadu', 33, 10, 611],
 ['Theni', 'Tamil Nadu', 33, 23, 624],
 ['Thiruvallur', 'Tamil Nadu', 33, 1, 602],
 ['Thiruvananthapuram', 'Kerala', 32, 14, 601],
 ['Thiruvarur', 'Tamil Nadu', 33, 18, 619],
 ['Thoothukkudi', 'Tamil Nadu', 33, 26, 627],
 ['Thoubal', 'Manipur', 14, 5, 276],
 ['Thrissur', 'Kerala', 32, 7, 594],
 ['Tikamgarh', 'Madhya Pradesh', 23, 7, 424],
 ['Tinsukia', 'Assam', 18, 10, 309],
 ['Tirap', 'Arunanchal Pradesh', 12, 10, 254],
 ['Tiruchirappalli', 'Tamil Nadu', 33, 13, 614],
 ['Tirunelveli', 'Tamil Nadu', 33, 27, 628],
 ['Tiruppur', 'Tamil Nadu', 33, 32, 633],
 ['Tiruvannamalai', 'Tamil Nadu', 33, 5, 606],
 ['Tonk', 'Rajasthan', 8, 22, 120],
 ['Tuensang', 'Nagaland', 13, 7, 267],
 ['Tumkur', 'Karnataka', 29, 17, 571],
 ['Udaipur', 'Rajasthan', 8, 32, 130],
 ['Udalguri', 'Assam', 18, 27, 326],
 ['Udhampur', 'Jammu & Kashmir', 1, 19, 19],
 ['Udham Singh Nagar', 'Uttarakhand', 5, 12, 67],
 ['Udupi', 'Karnataka', 29, 15, 569],
 ['Ujjain', 'Madhya Pradesh', 23, 18, 435],
 ['Ukhrul', 'Manipur', 14, 8, 279],
 ['Umaria', 'Madhya Pradesh', 23, 14, 431],
 ['Una', 'Himachal Pradesh', 2, 7, 29],
 ['Unnao', 'Uttar Pradesh', 9, 25, 156],
 ['Upper Siang', 'Arunanchal Pradesh', 12, 8, 252],
 ['Upper Subansiri', 'Arunanchal Pradesh', 12, 5, 249],
 ['Uttar Dinajpur', 'West Bengal', 19, 4, 330],
 ['Uttara Kannada', 'Karnataka', 29, 9, 563],
 ['Uttarkashi', 'Uttarakhand', 5, 1, 56],
 ['Vadodara', 'Gujarat', 24, 19, 486],
 ['Vaishali', 'Bihar', 10, 18, 220],
 ['Valsad', 'Gujarat', 24, 24, 491],
 ['Varanasi', 'Uttar Pradesh', 9, 66, 197],
 ['Vellore', 'Tamil Nadu', 33, 4, 605],
 ['Vidisha', 'Madhya Pradesh', 23, 26, 443],
 ['Viluppuram', 'Tamil Nadu', 33, 6, 607],
 ['Virudunagar', 'Tamil Nadu', 33, 24, 625],
 ['Visakhapatnam', 'Andhra Pradesh', 28, 13, 544],
 ['Vizianagaram', 'Andhra Pradesh', 28, 12, 543],
 ['Warangal', 'Andhra Pradesh', 28, 9, 540],
 ['Wardha', 'Maharashtra', 27, 8, 504],
 ['Washim', 'Maharashtra', 27, 6, 502],
 ['Wayanad', 'Kerala', 32, 3, 590],
 ['West', 'NCT of Delhi', 7, 7, 96],
 ['West', 'Sikkim', 11, 2, 242],
 ['West Garo Hills', 'Meghalaya', 17, 1, 293],
 ['West Godavari', 'Andhra Pradesh', 28, 15, 546],
 ['West Kameng', 'Arunanchal Pradesh', 12, 2, 246],
 ['West Khasi Hills', 'Meghalaya', 17, 4, 296],
 ['West Nimar', 'Madhya Pradesh', 23, 23, 440],
 ['West Siang', 'Arunanchal Pradesh', 12, 6, 250],
 ['West Tripura', 'Tripura', 16, 1, 289],
 ['Wokha', 'Nagaland', 13, 4, 264],
 ['Yadgir', 'Karnataka', 29, 26, 580],
 ['Yavatmal', 'Maharashtra', 27, 14, 510],
 ['Zunheboto', 'Nagaland', 13, 3, 263],
 ['Kabeerdham', 'Chhattisgarh', 22, 8, 407],
 ['Yamunanagar', 'Haryana', 6, 3, 71],
 ['Jind', 'Haryana', 6, 9, 77],
 ['Dausa', 'Rajasthan', 8, 11, 109],
 ['Karauli', 'Rajasthan', 8, 9, 107],
 ['Sawai Madhopur', 'Rajasthan', 8, 10, 108],
 ['Arwal', 'Bihar', 10, 38, 240],
 ['Jehanabad', 'Bihar', 10, 37, 239],
 ['Nagappattinam', 'Tamil Nadu', 33, 17, 618],
 ['Karaikal', 'Puducherry', 34, 4, 637],
 ['Puducherry', 'Puducherry', 34, 2, 635],
 ['Yanam', 'Puducherry', 34, 1, 634],
 ['Tapi', 'Gujarat', 24, 26, 493],
 ['Nicobar', 'Andaman & Nicobar Island', 35, 1, 638],
 ['South Andaman', 'Andaman & Nicobar Island', 35, 3, 640],
 ['North & Middle Andaman', 'Andaman & Nicobar Island', 35, 2, 639],
 ['Lakshadweep', 'Lakshadweep', 31, 1, 587]]

















In [18]:



    


for name in dir(shapes[3]):
    if not name.startswith('__'):
        print(name)



    


















    






bbox
parts
points
shapeType

















In [63]:



    


# Point(Longitude,Latitude)
pt = Point(74.6,19.78)
pt2 = Point(75.7, 29.1)



    











In [64]:



    


import fiona
from shapely.geometry import Point, shape

base = "/Users/macbook/Documents/BTP/Satellite/Data/Maps/Districts/Census_2011"
fc = fiona.open(base+"/2011_Dist.shp")



    











In [66]:



    


def reverse_geocode(pt):
    for feature in fc:
        if shape(feature['geometry']).contains(pt):
            print feature['properties']['DISTRICT'], feature['properties']['ST_NM']
            return feature['properties']['DISTRICT']
    return "NRI"

Finally able to do Reverse Geocoding without using any paid API

Lets try selecting rows from a DataFrame with indexes





In [4]:



    


import numpy as np
import pandas as pd



    











In [5]:



    


# importing the dataset we prepared and saved using Baseline 1 Notebook
ricep = pd.read_csv("/Users/macbook/Documents/BTP/Notebook/BTP/ricep.csv")
ricep.head()



    


















    
Out[5]:










  
    

      
      Unnamed: 0
      State_Name
      ind_district
      Crop_Year
      Season
      Crop
      Area
      Production
      phosphorus
      X1
      X2
      X3
      X4
    

  
  
    

      0
      15
      Andhra Pradesh
      anantapur
      1999
      kharif
      Rice
      37991.0
      105082.0
      0.0
      96800.0
      75400.0
      643.720
      881.473
    

    

      1
      16
      Andhra Pradesh
      anantapur
      2000
      kharif
      Rice
      39905.0
      117680.0
      0.0
      105082.0
      96800.0
      767.351
      643.720
    

    

      2
      17
      Andhra Pradesh
      anantapur
      2001
      kharif
      Rice
      32878.0
      95609.0
      0.0
      117680.0
      105082.0
      579.338
      767.351
    

    

      3
      18
      Andhra Pradesh
      anantapur
      2002
      kharif
      Rice
      29066.0
      66329.0
      0.0
      95609.0
      117680.0
      540.070
      579.338
    

    

      4
      21
      Andhra Pradesh
      anantapur
      2005
      kharif
      Rice
      25008.0
      69972.0
      0.0
      85051.0
      44891.0
      819.700
      564.500
    

  




















In [6]:



    


ricep = ricep.drop(["Unnamed: 0"],axis=1)
ricep["phosphorus"] = ricep["phosphorus"]*10
ricep["value"] = ricep["Production"]/ricep["Area"]



    











In [7]:



    


ricep.head()



    


















    
Out[7]:










  
    

      
      State_Name
      ind_district
      Crop_Year
      Season
      Crop
      Area
      Production
      phosphorus
      X1
      X2
      X3
      X4
      value
    

  
  
    

      0
      Andhra Pradesh
      anantapur
      1999
      kharif
      Rice
      37991.0
      105082.0
      0.0
      96800.0
      75400.0
      643.720
      881.473
      2.765971
    

    

      1
      Andhra Pradesh
      anantapur
      2000
      kharif
      Rice
      39905.0
      117680.0
      0.0
      105082.0
      96800.0
      767.351
      643.720
      2.949004
    

    

      2
      Andhra Pradesh
      anantapur
      2001
      kharif
      Rice
      32878.0
      95609.0
      0.0
      117680.0
      105082.0
      579.338
      767.351
      2.907993
    

    

      3
      Andhra Pradesh
      anantapur
      2002
      kharif
      Rice
      29066.0
      66329.0
      0.0
      95609.0
      117680.0
      540.070
      579.338
      2.282013
    

    

      4
      Andhra Pradesh
      anantapur
      2005
      kharif
      Rice
      25008.0
      69972.0
      0.0
      85051.0
      44891.0
      819.700
      564.500
      2.797985
    

  




















In [8]:



    


ricep.index[ ricep['ind_district'] == 'anantapur'].tolist()



    


















    
Out[8]:








[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

















In [9]:



    


ricep.index[ ricep['ind_district'] == 'anantapur' & ricep['Crop_Year'] == '2002' ]



    


















    






---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-9-37ee57b86976> in <module>()
----> 1 ricep.index[ ricep['ind_district'] == 'anantapur' & ricep['Crop_Year'] == '2002' ]

/Users/macbook/Library/Python/2.7/lib/python/site-packages/pandas/core/ops.pyc in wrapper(self, other)
    927                       is_integer_dtype(np.asarray(other)) else fill_bool)
    928             return filler(self._constructor(
--> 929                 na_op(self.values, other),
    930                 index=self.index)).__finalize__(self)
    931 

/Users/macbook/Library/Python/2.7/lib/python/site-packages/pandas/core/ops.pyc in na_op(x, y)
    897                     raise TypeError("cannot compare a dtyped [{0}] array with "
    898                                     "a scalar of type [{1}]".format(
--> 899                                         x.dtype, type(y).__name__))
    900 
    901         return result

TypeError: cannot compare a dtyped [int64] array with a scalar of type [bool]
















In [10]:



    


ricep.index[ (ricep['ind_district'] == 'anantapur') & (ricep['Crop_Year'] == 2002) ].tolist()



    


















    
Out[10]:








[3]

So we had to just put the parentheses





In [ ]:



    






    











In [ ]:



    






    











In [11]:



    


a = np.empty((ricep.shape[0],1))*np.NAN
ricex = ricep.assign(test = a)
ricex.head()



    


















    
Out[11]:










  
    

      
      State_Name
      ind_district
      Crop_Year
      Season
      Crop
      Area
      Production
      phosphorus
      X1
      X2
      X3
      X4
      value
      test
    

  
  
    

      0
      Andhra Pradesh
      anantapur
      1999
      kharif
      Rice
      37991.0
      105082.0
      0.0
      96800.0
      75400.0
      643.720
      881.473
      2.765971
      NaN
    

    

      1
      Andhra Pradesh
      anantapur
      2000
      kharif
      Rice
      39905.0
      117680.0
      0.0
      105082.0
      96800.0
      767.351
      643.720
      2.949004
      NaN
    

    

      2
      Andhra Pradesh
      anantapur
      2001
      kharif
      Rice
      32878.0
      95609.0
      0.0
      117680.0
      105082.0
      579.338
      767.351
      2.907993
      NaN
    

    

      3
      Andhra Pradesh
      anantapur
      2002
      kharif
      Rice
      29066.0
      66329.0
      0.0
      95609.0
      117680.0
      540.070
      579.338
      2.282013
      NaN
    

    

      4
      Andhra Pradesh
      anantapur
      2005
      kharif
      Rice
      25008.0
      69972.0
      0.0
      85051.0
      44891.0
      819.700
      564.500
      2.797985
      NaN

Time taken to execute a cell





In [14]:



    


%time

v = ricex.iloc[0,13]
v



    


















    






CPU times: user 3 µs, sys: 1 µs, total: 4 µs
Wall time: 5.01 µs










    
Out[14]:








nan

















In [15]:



    


d = v + 5



    











In [16]:



    


d



    


















    
Out[16]:








nan

















In [17]:



    


if pd.isnull(v):
    v = 3



    











In [18]:



    


v+5



    


















    
Out[18]:








8

















In [19]:



    


v



    


















    
Out[19]:








3

















In [20]:



    


df = pd.DataFrame(np.arange(1,7).reshape(2,3),
                  columns = list('abc'), 
                  index=pd.Series([2,5], name='b'))
df



    


















    
Out[20]:










  
    

      
      a
      b
      c
    

    

      b
      
      
      
    

  
  
    

      2
      1
      2
      3
    

    

      5
      4
      5
      6
    

  




















In [1]:



    


x = 3



    











In [2]:



    


x += 2



    











In [3]:



    


x



    


















    
Out[3]:








5

















In [4]:



    


s = "Akshansh"
s[-4]



    


















    
Out[4]:








'a'

















In [6]:



    


# bx = False
# if bx: continue
# else: bx = True



    











In [ ]:

Content source: AkshanshChahal/BTP

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