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In [9]: # -*- coding: utf-8 -*-
...: #-----(1) data analysis of agriculture sector -----
...:
...: import numpy as np
...: import pandas as pd
...: import matplotlib.pyplot as plt
...: import random
...:
...: df = pd.read_csv ('C:/Users/Jeet Das/Desktop/Major Project - Indian Economy/Project ( Section 1-Indian Economy)/Section-1_Data_sheet/(02)_agriculture.csv',encoding="cp1252")
...:
...: print("\n------- output data :-----------\n")
...: print("Agriculture data analysis:")
...:
...: # Question – A : get row and column numbers
...:
...: def dimension():
...: print('---------------------------------------------')
...: print("Dimension of the data frame = ",df.shape)
...: print('---------------------------------------------')
...:
...: # Question – B : print column names :
...:
...: def column_names():
...: print('Column names as follows :')
...: print('---------------------------------------------')
...: count = 1
...: for col in df.columns:
...: print(count,"-->",col)
...: count = count+1
...: print("---------------------------------------------\n")
...:
...: #Question – C : State_Name (using GROUP BY method) and no. of states :
...:
...: def states():
...: state_names = df.groupby(['State_Name'])[['District_Name']].count()
...: print("\t states names : ")
...: print("-------------------------------")
...: print(state_names)
...: print("-------------------------------")
...: count = 0
...: for row in range(len(state_names)):
...: count = count+1
...: print("total no. of states = ",count)
...: print("-----------------------------\n")
...:
...: # state - 1 : Andaman and Nicobar Islands
...: df_andaman = df[df.State_Name == 'Andaman and Nicobar Islands']
...:
...: # state -2 : Andhra Pradesh
...: df_andhra = df[df.State_Name == 'Andhra Pradesh']
...:
...: # state -3 : Arunachal Pradesh
...: df_arunachal = df[df.State_Name == 'Arunachal Pradesh']
...:
...: # state -4 : Assam
...: df_assam = df[df.State_Name == 'Assam']
...:
...: # state -5 : Bihar
...: df_bihar = df[df.State_Name == 'Bihar']
...:
...: # state -6 : Chandigarh
...: df_candigarh = df[df.State_Name == 'Chandigarh']
...:
...: # state -7 : Chhattisgarh
...: df_chattisgarh = df[df.State_Name == 'Chhattisgarh']
...:
...: # state -8 : Dadra and Nagar Haveli
...: df_dadra = df[df.State_Name == 'Dadra and Nagar Haveli']
...:
...: # state -9 : Goa
...: df_goa = df[df.State_Name == 'Goa']
...:
...: # state -10: Gujarat
...: df_gujrat = df[df.State_Name == 'Gujarat']
...:
...: # state -11: Haryana
...: df_haryana = df[df.State_Name == 'Haryana']
...:
...: # state -12: Himachal Pradesh
...: df_himachal = df[df.State_Name == 'Himachal Pradesh']
...:
...: # state -13: Jammu and Kashmir
...: df_jammu = df[df.State_Name == 'Jammu and Kashmir']
...:
...: # state -14: Jharkhand
...: df_jharkand = df[df.State_Name == 'Jharkhand']
...:
...: # state -15: Karnataka
...: df_karnataka = df[df.State_Name == 'Karnataka']
...:
...: # state -16: Kerala
...: df_kerala = df[df.State_Name == 'kerala']
...:
...: # state -17: Madhya Pradesh
...: df_madhya = df[df.State_Name == 'Madhya Pradesh']
...:
...: # state -18: Maharashtra
...: df_maharashtra = df[df.State_Name == 'Maharashtra']
...:
...: # state -19: Manipur
...: df_manipur = df[df.State_Name == 'Manipur']
...:
...: # state -20: Meghalaya
...: df_meghalaya = df[df.State_Name == 'Meghalaya']
...:
...: # state -21: Mizoram
...: df_mizoram = df[df.State_Name == 'Mizoram']
...:
...: # state -22: Nagaland
...: df_nagaland = df[df.State_Name == 'Nagaland']
...:
...: # state -23: Odisha
...: df_odisha = df[df.State_Name == 'Odisha']
...:
...: # state -24: Puducherry
...: df_puducherry = df[df.State_Name == 'Puducherry']
...:
...: # state -25: Punjab
...: df_punjab = df[df.State_Name == 'Punjab']
...:
...: # state -26: Rajasthan
...: df_rajasthan = df[df.State_Name == 'Rajasthan']
...:
...: # state -27: Sikkim
...: df_sikkim = df[df.State_Name == 'Sikkim']
...:
...: # state -28: Tamil Nadu
...: df_tamilnadu = df[df.State_Name == 'Tamil Nadu']
...:
...: # state -29: Telangana
...: df_telangana = df[df.State_Name == 'Telangana']
...:
...: # state -30: Tripura
...: df_tripura = df[df.State_Name == 'Tripura']
...:
...: # state -31: Uttar Pradesh
...: df_uttar = df[df.State_Name == 'Uttar Pradesh']
...:
...: # state -32: Uttarakhand
...: df_uttarakhand = df[df.State_Name == 'Uttarakhand']
...:
...: # state -33: West Bengal
...: df_wb = df[df.State_Name == 'West Bengal']
...:
...: # Question - D : print state names and Calculate no. of districts of all states
...:
...: def district_of_all_states():
...:
...: # state - 1 : Andaman and Nicobar Islands
...:
...: df_andaman = df[df.State_Name == 'Andaman and Nicobar Islands']
...: df_andaman_dist = df_andaman.groupby(['District_Name'])[['Crop_Year']].count()
...: print(df_andaman_dist)
...: print("-----------------------------")
...: count = 0
...: for row in range(len(df_andaman_dist)):
...: count = count+1
...: print("total no. of district in state - 1 : Andaman and Nicobar Islands = ",count)
...: print("-----------------------------\n")
...:
...: # state -2 : Andhra Pradesh
...:
...: df_andhra = df[df.State_Name == 'Andhra Pradesh']
...: df_andhra_dist = df_andhra.groupby(['District_Name'])[['Crop_Year']].count()
...: print(df_andhra_dist)
...: print("-----------------------------")
...: count = 0
...: for row in range(len(df_andhra_dist)):
...: count = count+1
...: print("total no. of district in state -2 : Andhra Pradesh = ",count)
...: print("-----------------------------\n")
...:
...: # state -3 : Arunachal Pradesh
...:
...: df_arunachal = df[df.State_Name == 'Arunachal Pradesh']
...: df_arunachal_dist = df_arunachal.groupby(['District_Name'])[['Crop_Year']].count()
...: print(df_arunachal_dist)
...: print("-----------------------------")
...: count = 0
...: for row in range(len(df_arunachal_dist)):
...: count = count+1
...: print("total no. of district in state -3 : Arunachal Pradesh = ",count)
...: print("-----------------------------\n")
...:
...: # state -4 : Assam
...:
...: df_assam = df[df.State_Name == 'Assam']
...: df_assam_dist = df_assam.groupby(['District_Name'])[['Crop_Year']].count()
...: print(df_assam_dist)
...: print("-----------------------------")
...: count = 0
...: for row in range(len(df_assam_dist)):
...: count = count+1
...: print("total no. of district in state -4 : Assam = ",count)
...: print("-----------------------------\n")
...:
...: # state -5 : Bihar
...:
...: df_bihar = df[df.State_Name == 'Bihar']
...: df_bihar_dist = df_bihar.groupby(['District_Name'])[['Crop_Year']].count()
...: print(df_bihar_dist)
...: print("-----------------------------")
...: count = 0
...: for row in range(len(df_bihar_dist)):
...: count = count+1
...: print("total no. of district in state -5 : Bihar = ",count)
...: print("-----------------------------\n")
...:
...: # state -6 : Chandigarh
...:
...: df_candigarh = df[df.State_Name == 'Chandigarh']
...: df_candigarh_dist = df_candigarh.groupby(['District_Name'])[['Crop_Year']].count()
...: print(df_candigarh_dist)
...: print("-----------------------------")
...: count = 0
...: for row in range(len(df_candigarh_dist)):
...: count = count+1
...: print("total no. of district in state -6 : Chandigarh = ",count)
...: print("-----------------------------\n")
...:
...: # state -7 : Chhattisgarh
...:
...: df_chattisgarh = df[df.State_Name == 'Chhattisgarh']
...: df_chattisgarh_dist = df_chattisgarh.groupby(['District_Name'])[['Crop_Year']].count()
...: print(df_chattisgarh_dist)
...: print("-----------------------------")
...: count = 0
...: for row in range(len(df_chattisgarh_dist)):
...: count = count+1
...: print("total no. of district in state -7 : Chhattisgarh = ",count)
...: print("-----------------------------\n")
...:
...: # state -8 : Dadra and Nagar Haveli
...:
...: df_dadra = df[df.State_Name == 'Dadra and Nagar Haveli']
...: df_dadra_dist = df_dadra.groupby(['District_Name'])[['Crop_Year']].count()
...: print(df_dadra_dist)
...: print("-----------------------------")
...: count = 0
...: for row in range(len(df_dadra_dist)):
...: count = count+1
...: print("total no. of district in state -8 : Dadra and Nagar Haveli = ",count)
...: print("-----------------------------\n")
...:
...: # state -9 : Goa
...:
...: df_goa = df[df.State_Name == 'Goa']
...: df_goa_dist = df_goa.groupby(['District_Name'])[['Crop_Year']].count()
...: print(df_goa_dist)
...: print("-----------------------------")
...: count = 0
...: for row in range(len(df_goa_dist)):
...: count = count+1
...: print("total no. of district in state -9 : Goa = ",count)
...: print("-----------------------------\n")
...:
...: # state -10: Gujarat
...:
...: df_gujrat = df[df.State_Name == 'Gujarat']
...: df_gujrat_dist = df_gujrat.groupby(['District_Name'])[['Crop_Year']].count()
...: print(df_gujrat_dist)
...: print("-----------------------------")
...: count = 0
...: for row in range(len(df_gujrat_dist)):
...: count = count+1
...: print("total no. of district in state -10: Gujarat = ",count)
...: print("-----------------------------\n")
...:
...: # state -11: Haryana
...:
...: df_haryana = df[df.State_Name == 'Haryana']
...: df_haryana_dist = df_haryana.groupby(['District_Name'])[['Crop_Year']].count()
...: print(df_haryana_dist)
...: print("-----------------------------")
...: count = 0
...: for row in range(len(df_haryana_dist)):
...: count = count+1
...: print("total no. of district in state -11: Haryana = ",count)
...: print("-----------------------------\n")
...:
...: # state -12: Himachal Pradesh
...:
...: df_himachal = df[df.State_Name == 'Himachal Pradesh']
...: df_himachal_dist = df_himachal.groupby(['District_Name'])[['Crop_Year']].count()
...: print(df_himachal_dist)
...: print("-----------------------------")
...: count = 0
...: for row in range(len(df_himachal_dist)):
...: count = count+1
...: print("total no. of district in state -12: Himachal Pradesh = ",count)
...: print("-----------------------------\n")
...:
...: # state -13: Jammu and Kashmir
...:
...: df_jammu = df[df.State_Name == 'Jammu and Kashmir']
...: df_jammu_dist = df_jammu.groupby(['District_Name'])[['Crop_Year']].count()
...: print(df_jammu_dist)
...: print("-----------------------------")
...: count = 0
...: for row in range(len(df_jammu_dist)):
...: count = count+1
...: print("total no. of district in state -13: Jammu and Kashmir = ",count)
...: print("-----------------------------\n")
...:
...: # state -14: Jharkhand
...:
...: df_jharkand = df[df.State_Name == 'Jharkhand']
...: df_jharkand_dist = df_jharkand.groupby(['District_Name'])[['Crop_Year']].count()
...: print(df_jharkand_dist)
...: print("-----------------------------")
...: count = 0
...: for row in range(len(df_jharkand_dist)):
...: count = count+1
...: print("total no. of district in state -14: Jharkhand = ",count)
...: print("-----------------------------\n")
...:
...: # state -15: Karnataka
...:
...: df_karnataka = df[df.State_Name == 'Karnataka']
...: df_karnataka_dist = df_karnataka.groupby(['District_Name'])[['Crop_Year']].count()
...: print(df_karnataka_dist)
...: print("-----------------------------")
...: count = 0
...: for row in range(len(df_karnataka_dist)):
...: count = count+1
...: print("total no. of district in state -15: Karnataka = ",count)
...: print("-----------------------------\n")
...:
...: # state -16: Kerala
...:
...: df_kerala = df[df.State_Name == 'kerala']
...: df_kerala_dist = df_kerala.groupby(['District_Name'])[['Crop_Year']].count()
...: print(df_kerala_dist)
...: print("-----------------------------")
...: count = 0
...: for row in range(len(df_kerala_dist)):
...: count = count+1
...: print("total no. of district in state -16: Kerala = ",count)
...: print("-----------------------------\n")
...:
...: # state -17: Madhya Pradesh
...:
...: df_madhya = df[df.State_Name == 'Madhya Pradesh']
...: df_madhya_dist = df_madhya.groupby(['District_Name'])[['Crop_Year']].count()
...: print(df_madhya_dist)
...: print("-----------------------------")
...: count = 0
...: for row in range(len(df_madhya_dist)):
...: count = count+1
...: print("total no. of district in state -17: Madhya Pradesh = ",count)
...: print("-----------------------------\n")
...:
...: # state -18: Maharashtra
...:
...: df_maharashtra = df[df.State_Name == 'Maharashtra']
...: df_maharashtra_dist = df_maharashtra.groupby(['District_Name'])[['Crop_Year']].count()
...: print(df_maharashtra_dist)
...: print("-----------------------------")
...: count = 0
...: for row in range(len(df_maharashtra_dist)):
...: count = count+1
...: print("total no. of district in state -18: Maharashtra = ",count)
...: print("-----------------------------\n")
...:
...: # state -19: Manipur
...:
...: df_manipur = df[df.State_Name == 'Manipur']
...: df_manipur_dist = df_manipur.groupby(['District_Name'])[['Crop_Year']].count()
...: print(df_manipur_dist)
...: print("-----------------------------")
...: count = 0
...: for row in range(len(df_manipur_dist)):
...: count = count+1
...: print("total no. of district in state -19: Manipur = ",count)
...: print("-----------------------------\n")
...:
...: # state -20: Meghalaya
...:
...: df_meghalaya = df[df.State_Name == 'Meghalaya']
...: df_meghalaya_dist = df_meghalaya.groupby(['District_Name'])[['Crop_Year']].count()
...: print(df_meghalaya_dist)
...: print("-----------------------------")
...: count = 0
...: for row in range(len(df_meghalaya_dist)):
...: count = count+1
...: print("total no. of district in state -20: Meghalaya = ",count)
...: print("-----------------------------\n")
...:
...: # state -21: Mizoram
...:
...: df_mizoram = df[df.State_Name == 'Mizoram']
...: df_mizoram_dist = df_mizoram.groupby(['District_Name'])[['Crop_Year']].count()
...: print(df_mizoram_dist)
...: print("-----------------------------")
...: count = 0
...: for row in range(len(df_mizoram_dist)):
...: count = count+1
...: print("total no. of district in state -21: Mizoram = ",count)
...: print("-----------------------------\n")
...:
...: # state -22: Nagaland
...:
...: df_nagaland = df[df.State_Name == 'Nagaland']
...: df_nagaland_dist = df_nagaland.groupby(['District_Name'])[['Crop_Year']].count()
...: print(df_nagaland_dist)
...: print("-----------------------------")
...: count = 0
...: for row in range(len(df_nagaland_dist)):
...: count = count+1
...: print("total no. of district in state -22: Nagaland = ",count)
...: print("-----------------------------\n")
...:
...: # state -23: Odisha
...:
...: df_odisha = df[df.State_Name == 'Odisha']
...: df_odisha_dist = df_odisha.groupby(['District_Name'])[['Crop_Year']].count()
...: print(df_odisha_dist)
...: print("-----------------------------")
...: count = 0
...: for row in range(len(df_odisha_dist)):
...: count = count+1
...: print("total no. of district in state -23: Odisha = ",count)
...: print("-----------------------------\n")
...:
...: # state -24: Puducherry
...:
...: df_puducherry = df[df.State_Name == 'Puducherry']
...: df_puducherry_dist = df_puducherry.groupby(['District_Name'])[['Crop_Year']].count()
...: print(df_puducherry_dist)
...: print("-----------------------------")
...: count = 0
...: for row in range(len(df_puducherry_dist)):
...: count = count+1
...: print("total no. of district in state -24: Puducherry = ",count)
...: print("-----------------------------\n")
...:
...: # state -25: Punjab
...:
...: df_punjab = df[df.State_Name == 'Punjab']
...: df_punjab_dist = df_punjab.groupby(['District_Name'])[['Crop_Year']].count()
...: print(df_punjab_dist)
...: print("-----------------------------")
...: count = 0
...: for row in range(len(df_punjab_dist)):
...: count = count+1
...: print("total no. of district in state -25: Punjab = ",count)
...: print("-----------------------------\n")
...:
...: # state -26: Rajasthan
...:
...: df_rajasthan = df[df.State_Name == 'Rajasthan']
...: df_rajasthan_dist = df_rajasthan.groupby(['District_Name'])[['Crop_Year']].count()
...: print(df_rajasthan_dist)
...: print("-----------------------------")
...: count = 0
...: for row in range(len(df_rajasthan_dist)):
...: count = count+1
...: print("total no. of district in state -26: Rajasthan = ",count)
...: print("-----------------------------\n")
...:
...: # state -27: Sikkim
...:
...: df_sikkim = df[df.State_Name == 'Sikkim']
...: df_sikkim_dist = df_sikkim.groupby(['District_Name'])[['Crop_Year']].count()
...: print(df_sikkim_dist)
...: print("-----------------------------")
...: count = 0
...: for row in range(len(df_sikkim_dist)):
...: count = count+1
...: print("total no. of district in state -27: Sikkim = ",count)
...: print("-----------------------------\n")
...:
...: # state -28: Tamil Nadu
...:
...: df_tamilnadu = df[df.State_Name == 'Tamil Nadu']
...: df_tamilnadu_dist = df_tamilnadu.groupby(['District_Name'])[['Crop_Year']].count()
...: print(df_tamilnadu_dist)
...: print("-----------------------------")
...: count = 0
...: for row in range(len(df_tamilnadu_dist)):
...: count = count+1
...: print("total no. of district in state -28: Tamil Nadu = ",count)
...: print("-----------------------------\n")
...:
...: # state -29: Telangana
...:
...: df_telangana = df[df.State_Name == 'Telangana']
...: df_telangana_dist = df_telangana.groupby(['District_Name'])[['Crop_Year']].count()
...: print(df_telangana_dist)
...: print("-----------------------------")
...: count = 0
...: for row in range(len(df_telangana_dist)):
...: count = count+1
...: print("total no. of district in state -29: Telangana = ",count)
...: print("-----------------------------\n")
...:
...: # state -30: Tripura
...:
...: df_tripura = df[df.State_Name == 'Tripura']
...: df_tripura_dist = df_tripura.groupby(['District_Name'])[['Crop_Year']].count()
...: print(df_tripura_dist)
...: print("-----------------------------")
...: count = 0
...: for row in range(len(df_tripura_dist)):
...: count = count+1
...: print("total no. of district in state -30: Tripura = ",count)
...: print("-----------------------------\n")
...:
...: # state -31: Uttar Pradesh
...:
...: df_uttar = df[df.State_Name == 'Uttar Pradesh']
...: df_uttar_dist = df_uttar.groupby(['District_Name'])[['Crop_Year']].count()
...: print(df_uttar_dist)
...: print("-----------------------------")
...: count = 0
...: for row in range(len(df_uttar_dist)):
...: count = count+1
...: print("total no. of district in state -31: Uttar Pradesh = ",count)
...: print("-----------------------------\n")
...:
...: # state -32: Uttarakhand
...:
...: df_uttarakhand = df[df.State_Name == 'Uttarakhand']
...: df_uttarakhand_dist = df_uttarakhand.groupby(['District_Name'])[['Crop_Year']].count()
...: print(df_uttarakhand_dist)
...: print("-----------------------------")
...: count = 0
...: for row in range(len(df_uttarakhand_dist)):
...: count = count+1
...: print("total no. of district in state -32: Uttarakhand = ",count)
...: print("-----------------------------\n")
...:
...: # state -33: West Bengal
...:
...: df_wb = df[df.State_Name == 'West Bengal']
...: df_wb_dist = df_wb.groupby(['District_Name'])[['Crop_Year']].count()
...: print(df_wb_dist)
...: print("-----------------------------")
...: count = 0
...: for row in range(len(df_wb_dist)):
...: count = count+1
...: print("total no. of district in state -33: West Bengal = ",count)
...: print("-----------------------------\n")
...:
...:
...: # ----- indian area of production in year 2000 ------
...:
...: df_area_2000 = (
...: sum(df_andaman['Area'][df_andaman['Crop_Year'] == 2000])
...: + sum(df_andhra['Area'][df_andhra['Crop_Year'] == 2000])
...: + sum(df_arunachal['Area'][df_arunachal['Crop_Year'] == 2000])
...: + sum(df_assam['Area'][df_assam['Crop_Year'] == 2000])
...: + sum(df_bihar['Area'][df_bihar['Crop_Year'] == 2000])
...: + sum(df_candigarh['Area'][df_candigarh['Crop_Year'] == 2000])
...: + sum(df_chattisgarh['Area'][df_chattisgarh['Crop_Year'] == 2000])
...: + sum(df_dadra['Area'][df_dadra['Crop_Year'] == 2000])
...: + sum(df_goa['Area'][df_goa['Crop_Year'] == 2000])
...: + sum(df_gujrat['Area'][df_gujrat['Crop_Year'] == 2000])
...: + sum(df_haryana['Area'][df_haryana['Crop_Year'] == 2000])
...: + sum(df_himachal['Area'][df_himachal['Crop_Year'] == 2000])
...: + sum(df_jammu['Area'][df_jammu['Crop_Year'] == 2000])
...: + sum(df_jharkand['Area'][df_jharkand['Crop_Year'] == 2000])
...: + sum(df_karnataka['Area'][df_karnataka['Crop_Year'] == 2000])
...: + sum(df_kerala['Area'][df_kerala['Crop_Year'] == 2000])
...: + sum(df_madhya['Area'][df_madhya['Crop_Year'] == 2000])
...: + sum(df_maharashtra['Area'][df_maharashtra['Crop_Year'] == 2000])
...: + sum(df_manipur['Area'][df_manipur['Crop_Year'] == 2000])
...: + sum(df_meghalaya['Area'][df_meghalaya['Crop_Year'] == 2000])
...: + sum(df_mizoram['Area'][df_mizoram['Crop_Year'] == 2000])
...: + sum(df_nagaland['Area'][df_nagaland['Crop_Year'] == 2000])
...: + sum(df_odisha['Area'][df_odisha['Crop_Year'] == 2000])
...: + sum(df_puducherry['Area'][df_puducherry['Crop_Year'] == 2000])
...: + sum(df_punjab['Area'][df_punjab['Crop_Year'] == 2000])
...: + sum(df_rajasthan['Area'][df_rajasthan['Crop_Year'] == 2000])
...: + sum(df_sikkim['Area'][df_sikkim['Crop_Year'] == 2000])
...: + sum(df_tamilnadu['Area'][df_tamilnadu['Crop_Year'] == 2000])
...: + sum(df_telangana['Area'][df_telangana['Crop_Year'] == 2000])
...: + sum(df_tripura['Area'][df_tripura['Crop_Year'] == 2000])
...: + sum(df_uttar['Area'][df_uttar['Crop_Year'] == 2000])
...: + sum(df_uttarakhand['Area'][df_uttarakhand['Crop_Year'] == 2000])
...: + sum(df_wb['Area'][df_wb['Crop_Year'] == 2000])
...: )
...:
...: # ----- indian area of production in year 2001 ------
...:
...: df_area_2001 = (
...: sum(df_andaman['Area'][df_andaman['Crop_Year'] == 2001])
...: + sum(df_andhra['Area'][df_andhra['Crop_Year'] == 2001])
...: + sum(df_arunachal['Area'][df_arunachal['Crop_Year'] == 2001])
...: + sum(df_assam['Area'][df_assam['Crop_Year'] == 2001])
...: + sum(df_bihar['Area'][df_bihar['Crop_Year'] == 2001])
...: + sum(df_candigarh['Area'][df_candigarh['Crop_Year'] == 2001])
...: + sum(df_chattisgarh['Area'][df_chattisgarh['Crop_Year'] == 2001])
...: + sum(df_dadra['Area'][df_dadra['Crop_Year'] == 2001])
...: + sum(df_goa['Area'][df_goa['Crop_Year'] == 2001])
...: + sum(df_gujrat['Area'][df_gujrat['Crop_Year'] == 2001])
...: + sum(df_haryana['Area'][df_haryana['Crop_Year'] == 2001])
...: + sum(df_himachal['Area'][df_himachal['Crop_Year'] == 2001])
...: + sum(df_jammu['Area'][df_jammu['Crop_Year'] == 2001])
...: + sum(df_jharkand['Area'][df_jharkand['Crop_Year'] == 2001])
...: + sum(df_karnataka['Area'][df_karnataka['Crop_Year'] == 2001])
...: + sum(df_kerala['Area'][df_kerala['Crop_Year'] == 2001])
...: + sum(df_madhya['Area'][df_madhya['Crop_Year'] == 2001])
...: + sum(df_maharashtra['Area'][df_maharashtra['Crop_Year'] == 2001])
...: + sum(df_manipur['Area'][df_manipur['Crop_Year'] == 2001])
...: + sum(df_meghalaya['Area'][df_meghalaya['Crop_Year'] == 2001])
...: + sum(df_mizoram['Area'][df_mizoram['Crop_Year'] == 2001])
...: + sum(df_nagaland['Area'][df_nagaland['Crop_Year'] == 2001])
...: + sum(df_odisha['Area'][df_odisha['Crop_Year'] == 2001])
...: + sum(df_puducherry['Area'][df_puducherry['Crop_Year'] == 2001])
...: + sum(df_punjab['Area'][df_punjab['Crop_Year'] == 2001])
...: + sum(df_rajasthan['Area'][df_rajasthan['Crop_Year'] == 2001])
...: + sum(df_sikkim['Area'][df_sikkim['Crop_Year'] == 2001])
...: + sum(df_tamilnadu['Area'][df_tamilnadu['Crop_Year'] == 2001])
...: + sum(df_telangana['Area'][df_telangana['Crop_Year'] == 2001])
...: + sum(df_tripura['Area'][df_tripura['Crop_Year'] == 2001])
...: + sum(df_uttar['Area'][df_uttar['Crop_Year'] == 2001])
...: + sum(df_uttarakhand['Area'][df_uttarakhand['Crop_Year'] == 2001])
...: + sum(df_wb['Area'][df_wb['Crop_Year'] == 2001])
...: )
...:
...: # ----- indian area of production in year 2002 ------
...:
...: df_area_2002 = (
...: sum(df_andaman['Area'][df_andaman['Crop_Year'] == 2002])
...: + sum(df_andhra['Area'][df_andhra['Crop_Year'] == 2002])
...: + sum(df_arunachal['Area'][df_arunachal['Crop_Year'] == 2002])
...: + sum(df_assam['Area'][df_assam['Crop_Year'] == 2002])
...: + sum(df_bihar['Area'][df_bihar['Crop_Year'] == 2002])
...: + sum(df_candigarh['Area'][df_candigarh['Crop_Year'] == 2002])
...: + sum(df_chattisgarh['Area'][df_chattisgarh['Crop_Year'] == 2002])
...: + sum(df_dadra['Area'][df_dadra['Crop_Year'] == 2002])
...: + sum(df_goa['Area'][df_goa['Crop_Year'] == 2002])
...: + sum(df_gujrat['Area'][df_gujrat['Crop_Year'] == 2002])
...: + sum(df_haryana['Area'][df_haryana['Crop_Year'] == 2002])
...: + sum(df_himachal['Area'][df_himachal['Crop_Year'] == 2002])
...: + sum(df_jammu['Area'][df_jammu['Crop_Year'] == 2002])
...: + sum(df_jharkand['Area'][df_jharkand['Crop_Year'] == 2002])
...: + sum(df_karnataka['Area'][df_karnataka['Crop_Year'] == 2002])
...: + sum(df_kerala['Area'][df_kerala['Crop_Year'] == 2002])
...: + sum(df_madhya['Area'][df_madhya['Crop_Year'] == 2002])
...: + sum(df_maharashtra['Area'][df_maharashtra['Crop_Year'] == 2002])
...: + sum(df_manipur['Area'][df_manipur['Crop_Year'] == 2002])
...: + sum(df_meghalaya['Area'][df_meghalaya['Crop_Year'] == 2002])
...: + sum(df_mizoram['Area'][df_mizoram['Crop_Year'] == 2002])
...: + sum(df_nagaland['Area'][df_nagaland['Crop_Year'] == 2002])
...: + sum(df_odisha['Area'][df_odisha['Crop_Year'] == 2002])
...: + sum(df_puducherry['Area'][df_puducherry['Crop_Year'] == 2002])
...: + sum(df_punjab['Area'][df_punjab['Crop_Year'] == 2002])
...: + sum(df_rajasthan['Area'][df_rajasthan['Crop_Year'] == 2002])
...: + sum(df_sikkim['Area'][df_sikkim['Crop_Year'] == 2002])
...: + sum(df_tamilnadu['Area'][df_tamilnadu['Crop_Year'] == 2002])
...: + sum(df_telangana['Area'][df_telangana['Crop_Year'] == 2002])
...: + sum(df_tripura['Area'][df_tripura['Crop_Year'] == 2002])
...: + sum(df_uttar['Area'][df_uttar['Crop_Year'] == 2002])
...: + sum(df_uttarakhand['Area'][df_uttarakhand['Crop_Year'] == 2002])
...: + sum(df_wb['Area'][df_wb['Crop_Year'] == 2002])
...: )
...:
...: # ----- indian area of production in year 2003 ------
...:
...: df_area_2003 = (
...: sum(df_andaman['Area'][df_andaman['Crop_Year'] == 2003])
...: + sum(df_andhra['Area'][df_andhra['Crop_Year'] == 2003])
...: + sum(df_arunachal['Area'][df_arunachal['Crop_Year'] == 2003])
...: + sum(df_assam['Area'][df_assam['Crop_Year'] == 2003])
...: + sum(df_bihar['Area'][df_bihar['Crop_Year'] == 2003])
...: + sum(df_candigarh['Area'][df_candigarh['Crop_Year'] == 2003])
...: + sum(df_chattisgarh['Area'][df_chattisgarh['Crop_Year'] == 2003])
...: + sum(df_dadra['Area'][df_dadra['Crop_Year'] == 2003])
...: + sum(df_goa['Area'][df_goa['Crop_Year'] == 2003])
...: + sum(df_gujrat['Area'][df_gujrat['Crop_Year'] == 2003])
...: + sum(df_haryana['Area'][df_haryana['Crop_Year'] == 2003])
...: + sum(df_himachal['Area'][df_himachal['Crop_Year'] == 2003])
...: + sum(df_jammu['Area'][df_jammu['Crop_Year'] == 2003])
...: + sum(df_jharkand['Area'][df_jharkand['Crop_Year'] == 2003])
...: + sum(df_karnataka['Area'][df_karnataka['Crop_Year'] == 2003])
...: + sum(df_kerala['Area'][df_kerala['Crop_Year'] == 2003])
...: + sum(df_madhya['Area'][df_madhya['Crop_Year'] == 2003])
...: + sum(df_maharashtra['Area'][df_maharashtra['Crop_Year'] == 2003])
...: + sum(df_manipur['Area'][df_manipur['Crop_Year'] == 2003])
...: + sum(df_meghalaya['Area'][df_meghalaya['Crop_Year'] == 2003])
...: + sum(df_mizoram['Area'][df_mizoram['Crop_Year'] == 2003])
...: + sum(df_nagaland['Area'][df_nagaland['Crop_Year'] == 2003])
...: + sum(df_odisha['Area'][df_odisha['Crop_Year'] == 2003])
...: + sum(df_puducherry['Area'][df_puducherry['Crop_Year'] == 2003])
...: + sum(df_punjab['Area'][df_punjab['Crop_Year'] == 2003])
...: + sum(df_rajasthan['Area'][df_rajasthan['Crop_Year'] == 2003])
...: + sum(df_sikkim['Area'][df_sikkim['Crop_Year'] == 2003])
...: + sum(df_tamilnadu['Area'][df_tamilnadu['Crop_Year'] == 2003])
...: + sum(df_telangana['Area'][df_telangana['Crop_Year'] == 2003])
...: + sum(df_tripura['Area'][df_tripura['Crop_Year'] == 2003])
...: + sum(df_uttar['Area'][df_uttar['Crop_Year'] == 2003])
...: + sum(df_uttarakhand['Area'][df_uttarakhand['Crop_Year'] == 2003])
...: + sum(df_wb['Area'][df_wb['Crop_Year'] == 2003])
...: )
...:
...: # ----- indian area of production in year 2004 ------
...:
...: df_area_2004 = (
...: sum(df_andaman['Area'][df_andaman['Crop_Year'] == 2004])
...: + sum(df_andhra['Area'][df_andhra['Crop_Year'] == 2004])
...: + sum(df_arunachal['Area'][df_arunachal['Crop_Year'] == 2004])
...: + sum(df_assam['Area'][df_assam['Crop_Year'] == 2004])
...: + sum(df_bihar['Area'][df_bihar['Crop_Year'] == 2004])
...: + sum(df_candigarh['Area'][df_candigarh['Crop_Year'] == 2004])
...: + sum(df_chattisgarh['Area'][df_chattisgarh['Crop_Year'] == 2004])
...: + sum(df_dadra['Area'][df_dadra['Crop_Year'] == 2004])
...: + sum(df_goa['Area'][df_goa['Crop_Year'] == 2004])
...: + sum(df_gujrat['Area'][df_gujrat['Crop_Year'] == 2004])
...: + sum(df_haryana['Area'][df_haryana['Crop_Year'] == 2004])
...: + sum(df_himachal['Area'][df_himachal['Crop_Year'] == 2004])
...: + sum(df_jammu['Area'][df_jammu['Crop_Year'] == 2004])
...: + sum(df_jharkand['Area'][df_jharkand['Crop_Year'] == 2004])
...: + sum(df_karnataka['Area'][df_karnataka['Crop_Year'] == 2004])
...: + sum(df_kerala['Area'][df_kerala['Crop_Year'] == 2004])
...: + sum(df_madhya['Area'][df_madhya['Crop_Year'] == 2004])
...: + sum(df_maharashtra['Area'][df_maharashtra['Crop_Year'] == 2004])
...: + sum(df_manipur['Area'][df_manipur['Crop_Year'] == 2004])
...: + sum(df_meghalaya['Area'][df_meghalaya['Crop_Year'] == 2004])
...: + sum(df_mizoram['Area'][df_mizoram['Crop_Year'] == 2004])
...: + sum(df_nagaland['Area'][df_nagaland['Crop_Year'] == 2004])
...: + sum(df_odisha['Area'][df_odisha['Crop_Year'] == 2004])
...: + sum(df_puducherry['Area'][df_puducherry['Crop_Year'] == 2004])
...: + sum(df_punjab['Area'][df_punjab['Crop_Year'] == 2004])
...: + sum(df_rajasthan['Area'][df_rajasthan['Crop_Year'] == 2004])
...: + sum(df_sikkim['Area'][df_sikkim['Crop_Year'] == 2004])
...: + sum(df_tamilnadu['Area'][df_tamilnadu['Crop_Year'] == 2004])
...: + sum(df_telangana['Area'][df_telangana['Crop_Year'] == 2004])
...: + sum(df_tripura['Area'][df_tripura['Crop_Year'] == 2004])
...: + sum(df_uttar['Area'][df_uttar['Crop_Year'] == 2004])
...: + sum(df_uttarakhand['Area'][df_uttarakhand['Crop_Year'] == 2004])
...: + sum(df_wb['Area'][df_wb['Crop_Year'] == 2004])
...: )
...:
...: # ----- indian area of production in year 2005 ------
...:
...: df_area_2005 = (
...: sum(df_andaman['Area'][df_andaman['Crop_Year'] == 2005])
...: + sum(df_andhra['Area'][df_andhra['Crop_Year'] == 2005])
...: + sum(df_arunachal['Area'][df_arunachal['Crop_Year'] == 2005])
...: + sum(df_assam['Area'][df_assam['Crop_Year'] == 2005])
...: + sum(df_bihar['Area'][df_bihar['Crop_Year'] == 2005])
...: + sum(df_candigarh['Area'][df_candigarh['Crop_Year'] == 2005])
...: + sum(df_chattisgarh['Area'][df_chattisgarh['Crop_Year'] == 2005])
...: + sum(df_dadra['Area'][df_dadra['Crop_Year'] == 2005])
...: + sum(df_goa['Area'][df_goa['Crop_Year'] == 2005])
...: + sum(df_gujrat['Area'][df_gujrat['Crop_Year'] == 2005])
...: + sum(df_haryana['Area'][df_haryana['Crop_Year'] == 2005])
...: + sum(df_himachal['Area'][df_himachal['Crop_Year'] == 2005])
...: + sum(df_jammu['Area'][df_jammu['Crop_Year'] == 2005])
...: + sum(df_jharkand['Area'][df_jharkand['Crop_Year'] == 2005])
...: + sum(df_karnataka['Area'][df_karnataka['Crop_Year'] == 2005])
...: + sum(df_kerala['Area'][df_kerala['Crop_Year'] == 2005])
...: + sum(df_madhya['Area'][df_madhya['Crop_Year'] == 2005])
...: + sum(df_maharashtra['Area'][df_maharashtra['Crop_Year'] == 2005])
...: + sum(df_manipur['Area'][df_manipur['Crop_Year'] == 2005])
...: + sum(df_meghalaya['Area'][df_meghalaya['Crop_Year'] == 2005])
...: + sum(df_mizoram['Area'][df_mizoram['Crop_Year'] == 2005])
...: + sum(df_nagaland['Area'][df_nagaland['Crop_Year'] == 2005])
...: + sum(df_odisha['Area'][df_odisha['Crop_Year'] == 2005])
...: + sum(df_puducherry['Area'][df_puducherry['Crop_Year'] == 2005])
...: + sum(df_punjab['Area'][df_punjab['Crop_Year'] == 2005])
...: + sum(df_rajasthan['Area'][df_rajasthan['Crop_Year'] == 2005])
...: + sum(df_sikkim['Area'][df_sikkim['Crop_Year'] == 2005])
...: + sum(df_tamilnadu['Area'][df_tamilnadu['Crop_Year'] == 2005])
...: + sum(df_telangana['Area'][df_telangana['Crop_Year'] == 2005])
...: + sum(df_tripura['Area'][df_tripura['Crop_Year'] == 2005])
...: + sum(df_uttar['Area'][df_uttar['Crop_Year'] == 2005])
...: + sum(df_uttarakhand['Area'][df_uttarakhand['Crop_Year'] == 2005])
...: + sum(df_wb['Area'][df_wb['Crop_Year'] == 2005])
...: )
...:
...: # ----- indian area of production in year 2006 ------
...:
...: df_area_2006 = (
...: sum(df_andaman['Area'][df_andaman['Crop_Year'] == 2006])
...: + sum(df_andhra['Area'][df_andhra['Crop_Year'] == 2006])
...: + sum(df_arunachal['Area'][df_arunachal['Crop_Year'] == 2006])
...: + sum(df_assam['Area'][df_assam['Crop_Year'] == 2006])
...: + sum(df_bihar['Area'][df_bihar['Crop_Year'] == 2006])
...: + sum(df_candigarh['Area'][df_candigarh['Crop_Year'] == 2006])
...: + sum(df_chattisgarh['Area'][df_chattisgarh['Crop_Year'] == 2006])
...: + sum(df_dadra['Area'][df_dadra['Crop_Year'] == 2006])
...: + sum(df_goa['Area'][df_goa['Crop_Year'] == 2006])
...: + sum(df_gujrat['Area'][df_gujrat['Crop_Year'] == 2006])
...: + sum(df_haryana['Area'][df_haryana['Crop_Year'] == 2006])
...: + sum(df_himachal['Area'][df_himachal['Crop_Year'] == 2006])
...: + sum(df_jammu['Area'][df_jammu['Crop_Year'] == 2006])
...: + sum(df_jharkand['Area'][df_jharkand['Crop_Year'] == 2006])
...: + sum(df_karnataka['Area'][df_karnataka['Crop_Year'] == 2006])
...: + sum(df_kerala['Area'][df_kerala['Crop_Year'] == 2006])
...: + sum(df_madhya['Area'][df_madhya['Crop_Year'] == 2006])
...: + sum(df_maharashtra['Area'][df_maharashtra['Crop_Year'] == 2006])
...: + sum(df_manipur['Area'][df_manipur['Crop_Year'] == 2006])
...: + sum(df_meghalaya['Area'][df_meghalaya['Crop_Year'] == 2006])
...: + sum(df_mizoram['Area'][df_mizoram['Crop_Year'] == 2006])
...: + sum(df_nagaland['Area'][df_nagaland['Crop_Year'] == 2006])
...: + sum(df_odisha['Area'][df_odisha['Crop_Year'] == 2006])
...: + sum(df_puducherry['Area'][df_puducherry['Crop_Year'] == 2006])
...: + sum(df_punjab['Area'][df_punjab['Crop_Year'] == 2006])
...: + sum(df_rajasthan['Area'][df_rajasthan['Crop_Year'] == 2006])
...: + sum(df_sikkim['Area'][df_sikkim['Crop_Year'] == 2006])
...: + sum(df_tamilnadu['Area'][df_tamilnadu['Crop_Year'] == 2006])
...: + sum(df_telangana['Area'][df_telangana['Crop_Year'] == 2006])
...: + sum(df_tripura['Area'][df_tripura['Crop_Year'] == 2006])
...: + sum(df_uttar['Area'][df_uttar['Crop_Year'] == 2006])
...: + sum(df_uttarakhand['Area'][df_uttarakhand['Crop_Year'] == 2006])
...: + sum(df_wb['Area'][df_wb['Crop_Year'] == 2006])
...: )
...:
...: # ----- indian area of production in year 2007 ------
...:
...: df_area_2007 = (
...: sum(df_andaman['Area'][df_andaman['Crop_Year'] == 2007])
...: + sum(df_andhra['Area'][df_andhra['Crop_Year'] == 2007])
...: + sum(df_arunachal['Area'][df_arunachal['Crop_Year'] == 2007])
...: + sum(df_assam['Area'][df_assam['Crop_Year'] == 2007])
...: + sum(df_bihar['Area'][df_bihar['Crop_Year'] == 2007])
...: + sum(df_candigarh['Area'][df_candigarh['Crop_Year'] == 2007])
...: + sum(df_chattisgarh['Area'][df_chattisgarh['Crop_Year'] == 2007])
...: + sum(df_dadra['Area'][df_dadra['Crop_Year'] == 2007])
...: + sum(df_goa['Area'][df_goa['Crop_Year'] == 2007])
...: + sum(df_gujrat['Area'][df_gujrat['Crop_Year'] == 2007])
...: + sum(df_haryana['Area'][df_haryana['Crop_Year'] == 2007])
...: + sum(df_himachal['Area'][df_himachal['Crop_Year'] == 2007])
...: + sum(df_jammu['Area'][df_jammu['Crop_Year'] == 2007])
...: + sum(df_jharkand['Area'][df_jharkand['Crop_Year'] == 2007])
...: + sum(df_karnataka['Area'][df_karnataka['Crop_Year'] == 2007])
...: + sum(df_kerala['Area'][df_kerala['Crop_Year'] == 2007])
...: + sum(df_madhya['Area'][df_madhya['Crop_Year'] == 2007])
...: + sum(df_maharashtra['Area'][df_maharashtra['Crop_Year'] == 2007])
...: + sum(df_manipur['Area'][df_manipur['Crop_Year'] == 2007])
...: + sum(df_meghalaya['Area'][df_meghalaya['Crop_Year'] == 2007])
...: + sum(df_mizoram['Area'][df_mizoram['Crop_Year'] == 2007])
...: + sum(df_nagaland['Area'][df_nagaland['Crop_Year'] == 2007])
...: + sum(df_odisha['Area'][df_odisha['Crop_Year'] == 2007])
...: + sum(df_puducherry['Area'][df_puducherry['Crop_Year'] == 2007])
...: + sum(df_punjab['Area'][df_punjab['Crop_Year'] == 2007])
...: + sum(df_rajasthan['Area'][df_rajasthan['Crop_Year'] == 2007])
...: + sum(df_sikkim['Area'][df_sikkim['Crop_Year'] == 2007])
...: + sum(df_tamilnadu['Area'][df_tamilnadu['Crop_Year'] == 2007])
...: + sum(df_telangana['Area'][df_telangana['Crop_Year'] == 2007])
...: + sum(df_tripura['Area'][df_tripura['Crop_Year'] == 2007])
...: + sum(df_uttar['Area'][df_uttar['Crop_Year'] == 2007])
...: + sum(df_uttarakhand['Area'][df_uttarakhand['Crop_Year'] == 2007])
...: + sum(df_wb['Area'][df_wb['Crop_Year'] == 2007])
...: )
...:
...: # ----- indian area of production in year 2008 ------
...:
...: df_area_2008 = (
...: sum(df_andaman['Area'][df_andaman['Crop_Year'] == 2008])
...: + sum(df_andhra['Area'][df_andhra['Crop_Year'] == 2008])
...: + sum(df_arunachal['Area'][df_arunachal['Crop_Year'] == 2008])
...: + sum(df_assam['Area'][df_assam['Crop_Year'] == 2008])
...: + sum(df_bihar['Area'][df_bihar['Crop_Year'] == 2008])
...: + sum(df_candigarh['Area'][df_candigarh['Crop_Year'] == 2008])
...: + sum(df_chattisgarh['Area'][df_chattisgarh['Crop_Year'] == 2008])
...: + sum(df_dadra['Area'][df_dadra['Crop_Year'] == 2008])
...: + sum(df_goa['Area'][df_goa['Crop_Year'] == 2008])
...: + sum(df_gujrat['Area'][df_gujrat['Crop_Year'] == 2008])
...: + sum(df_haryana['Area'][df_haryana['Crop_Year'] == 2008])
...: + sum(df_himachal['Area'][df_himachal['Crop_Year'] == 2008])
...: + sum(df_jammu['Area'][df_jammu['Crop_Year'] == 2008])
...: + sum(df_jharkand['Area'][df_jharkand['Crop_Year'] == 2008])
...: + sum(df_karnataka['Area'][df_karnataka['Crop_Year'] == 2008])
...: + sum(df_kerala['Area'][df_kerala['Crop_Year'] == 2008])
...: + sum(df_madhya['Area'][df_madhya['Crop_Year'] == 2008])
...: + sum(df_maharashtra['Area'][df_maharashtra['Crop_Year'] == 2008])
...: + sum(df_manipur['Area'][df_manipur['Crop_Year'] == 2008])
...: + sum(df_meghalaya['Area'][df_meghalaya['Crop_Year'] == 2008])
...: + sum(df_mizoram['Area'][df_mizoram['Crop_Year'] == 2008])
...: + sum(df_nagaland['Area'][df_nagaland['Crop_Year'] == 2008])
...: + sum(df_odisha['Area'][df_odisha['Crop_Year'] == 2008])
...: + sum(df_puducherry['Area'][df_puducherry['Crop_Year'] == 2008])
...: + sum(df_punjab['Area'][df_punjab['Crop_Year'] == 2008])
...: + sum(df_rajasthan['Area'][df_rajasthan['Crop_Year'] == 2008])
...: + sum(df_sikkim['Area'][df_sikkim['Crop_Year'] == 2008])
...: + sum(df_tamilnadu['Area'][df_tamilnadu['Crop_Year'] == 2008])
...: + sum(df_telangana['Area'][df_telangana['Crop_Year'] == 2008])
...: + sum(df_tripura['Area'][df_tripura['Crop_Year'] == 2008])
...: + sum(df_uttar['Area'][df_uttar['Crop_Year'] == 2008])
...: + sum(df_uttarakhand['Area'][df_uttarakhand['Crop_Year'] == 2008])
...: + sum(df_wb['Area'][df_wb['Crop_Year'] == 2008])
...: )
...:
...: # ----- indian area of production in year 2009 ------
...:
...: df_area_2009 = (
...: sum(df_andaman['Area'][df_andaman['Crop_Year'] == 2009])
...: + sum(df_andhra['Area'][df_andhra['Crop_Year'] == 2009])
...: + sum(df_arunachal['Area'][df_arunachal['Crop_Year'] == 2009])
...: + sum(df_assam['Area'][df_assam['Crop_Year'] == 2009])
...: + sum(df_bihar['Area'][df_bihar['Crop_Year'] == 2009])
...: + sum(df_candigarh['Area'][df_candigarh['Crop_Year'] == 2009])
...: + sum(df_chattisgarh['Area'][df_chattisgarh['Crop_Year'] == 2009])
...: + sum(df_dadra['Area'][df_dadra['Crop_Year'] == 2009])
...: + sum(df_goa['Area'][df_goa['Crop_Year'] == 2009])
...: + sum(df_gujrat['Area'][df_gujrat['Crop_Year'] == 2009])
...: + sum(df_haryana['Area'][df_haryana['Crop_Year'] == 2009])
...: + sum(df_himachal['Area'][df_himachal['Crop_Year'] == 2009])
...: + sum(df_jammu['Area'][df_jammu['Crop_Year'] == 2009])
...: + sum(df_jharkand['Area'][df_jharkand['Crop_Year'] == 2009])
...: + sum(df_karnataka['Area'][df_karnataka['Crop_Year'] == 2009])
...: + sum(df_kerala['Area'][df_kerala['Crop_Year'] == 2009])
...: + sum(df_madhya['Area'][df_madhya['Crop_Year'] == 2009])
...: + sum(df_maharashtra['Area'][df_maharashtra['Crop_Year'] == 2009])
...: + sum(df_manipur['Area'][df_manipur['Crop_Year'] == 2009])
...: + sum(df_meghalaya['Area'][df_meghalaya['Crop_Year'] == 2009])
...: + sum(df_mizoram['Area'][df_mizoram['Crop_Year'] == 2009])
...: + sum(df_nagaland['Area'][df_nagaland['Crop_Year'] == 2009])
...: + sum(df_odisha['Area'][df_odisha['Crop_Year'] == 2009])
...: + sum(df_puducherry['Area'][df_puducherry['Crop_Year'] == 2009])
...: + sum(df_punjab['Area'][df_punjab['Crop_Year'] == 2009])
...: + sum(df_rajasthan['Area'][df_rajasthan['Crop_Year'] == 2009])
...: + sum(df_sikkim['Area'][df_sikkim['Crop_Year'] == 2009])
...: + sum(df_tamilnadu['Area'][df_tamilnadu['Crop_Year'] == 2009])
...: + sum(df_telangana['Area'][df_telangana['Crop_Year'] == 2009])
...: + sum(df_tripura['Area'][df_tripura['Crop_Year'] == 2009])
...: + sum(df_uttar['Area'][df_uttar['Crop_Year'] == 2009])
...: + sum(df_uttarakhand['Area'][df_uttarakhand['Crop_Year'] == 2009])
...: + sum(df_wb['Area'][df_wb['Crop_Year'] == 2009])
...: )
...:
...: # ----- indian area of production in year 2010 ------
...:
...: df_area_2010 = (
...: sum(df_andaman['Area'][df_andaman['Crop_Year'] == 2010])
...: + sum(df_andhra['Area'][df_andhra['Crop_Year'] == 2010])
...: + sum(df_arunachal['Area'][df_arunachal['Crop_Year'] == 2010])
...: + sum(df_assam['Area'][df_assam['Crop_Year'] == 2010])
...: + sum(df_bihar['Area'][df_bihar['Crop_Year'] == 2010])
...: + sum(df_candigarh['Area'][df_candigarh['Crop_Year'] == 2010])
...: + sum(df_chattisgarh['Area'][df_chattisgarh['Crop_Year'] == 2010])
...: + sum(df_dadra['Area'][df_dadra['Crop_Year'] == 2010])
...: + sum(df_goa['Area'][df_goa['Crop_Year'] == 2010])
...: + sum(df_gujrat['Area'][df_gujrat['Crop_Year'] == 2010])
...: + sum(df_haryana['Area'][df_haryana['Crop_Year'] == 2010])
...: + sum(df_himachal['Area'][df_himachal['Crop_Year'] == 2010])
...: + sum(df_jammu['Area'][df_jammu['Crop_Year'] == 2010])
...: + sum(df_jharkand['Area'][df_jharkand['Crop_Year'] == 2010])
...: + sum(df_karnataka['Area'][df_karnataka['Crop_Year'] == 2010])
...: + sum(df_kerala['Area'][df_kerala['Crop_Year'] == 2010])
...: + sum(df_madhya['Area'][df_madhya['Crop_Year'] == 2010])
...: + sum(df_maharashtra['Area'][df_maharashtra['Crop_Year'] == 2010])
...: + sum(df_manipur['Area'][df_manipur['Crop_Year'] == 2010])
...: + sum(df_meghalaya['Area'][df_meghalaya['Crop_Year'] == 2010])
...: + sum(df_mizoram['Area'][df_mizoram['Crop_Year'] == 2010])
...: + sum(df_nagaland['Area'][df_nagaland['Crop_Year'] == 2010])
...: + sum(df_odisha['Area'][df_odisha['Crop_Year'] == 2010])
...: + sum(df_puducherry['Area'][df_puducherry['Crop_Year'] == 2010])
...: + sum(df_punjab['Area'][df_punjab['Crop_Year'] == 2010])
...: + sum(df_rajasthan['Area'][df_rajasthan['Crop_Year'] == 2010])
...: + sum(df_sikkim['Area'][df_sikkim['Crop_Year'] == 2010])
...: + sum(df_tamilnadu['Area'][df_tamilnadu['Crop_Year'] == 2010])
...: + sum(df_telangana['Area'][df_telangana['Crop_Year'] == 2010])
...: + sum(df_tripura['Area'][df_tripura['Crop_Year'] == 2010])
...: + sum(df_uttar['Area'][df_uttar['Crop_Year'] == 2010])
...: + sum(df_uttarakhand['Area'][df_uttarakhand['Crop_Year'] == 2010])
...: + sum(df_wb['Area'][df_wb['Crop_Year'] == 2010])
...: )
...:
...: # ----- indian Production in year 2000 ------
...:
...: df_Production_2000 = (
...: sum(df_andaman['Production'][df_andaman['Crop_Year'] == 2000])
...: + sum(df_andhra['Production'][df_andhra['Crop_Year'] == 2000])
...: + sum(df_arunachal['Production'][df_arunachal['Crop_Year'] == 2000])
...: + sum(df_assam['Production'][df_assam['Crop_Year'] == 2000])
...: + sum(df_bihar['Production'][df_bihar['Crop_Year'] == 2000])
...: + sum(df_candigarh['Production'][df_candigarh['Crop_Year'] == 2000])
...: + sum(df_chattisgarh['Production'][df_chattisgarh['Crop_Year'] == 2000])
...: + sum(df_dadra['Production'][df_dadra['Crop_Year'] == 2000])
...: + sum(df_goa['Production'][df_goa['Crop_Year'] == 2000])
...: + sum(df_gujrat['Production'][df_gujrat['Crop_Year'] == 2000])
...: + sum(df_haryana['Production'][df_haryana['Crop_Year'] == 2000])
...: + sum(df_himachal['Production'][df_himachal['Crop_Year'] == 2000])
...: + sum(df_jammu['Production'][df_jammu['Crop_Year'] == 2000])
...: + sum(df_jharkand['Production'][df_jharkand['Crop_Year'] == 2000])
...: + sum(df_karnataka['Production'][df_karnataka['Crop_Year'] == 2000])
...: + sum(df_kerala['Production'][df_kerala['Crop_Year'] == 2000])
...: + sum(df_madhya['Production'][df_madhya['Crop_Year'] == 2000])
...: + sum(df_maharashtra['Production'][df_maharashtra['Crop_Year'] == 2000])
...: + sum(df_manipur['Production'][df_manipur['Crop_Year'] == 2000])
...: + sum(df_meghalaya['Production'][df_meghalaya['Crop_Year'] == 2000])
...: + sum(df_mizoram['Production'][df_mizoram['Crop_Year'] == 2000])
...: + sum(df_nagaland['Production'][df_nagaland['Crop_Year'] == 2000])
...: + sum(df_odisha['Production'][df_odisha['Crop_Year'] == 2000])
...: + sum(df_puducherry['Production'][df_puducherry['Crop_Year'] == 2000])
...: + sum(df_punjab['Production'][df_punjab['Crop_Year'] == 2000])
...: + sum(df_rajasthan['Production'][df_rajasthan['Crop_Year'] == 2000])
...: + sum(df_sikkim['Production'][df_sikkim['Crop_Year'] == 2000])
...: + sum(df_tamilnadu['Production'][df_tamilnadu['Crop_Year'] == 2000])
...: + sum(df_telangana['Production'][df_telangana['Crop_Year'] == 2000])
...: + sum(df_tripura['Production'][df_tripura['Crop_Year'] == 2000])
...: + sum(df_uttar['Production'][df_uttar['Crop_Year'] == 2000])
...: + sum(df_uttarakhand['Production'][df_uttarakhand['Crop_Year'] == 2000])
...: + sum(df_wb['Production'][df_wb['Crop_Year'] == 2000])
...: )
...:
...: # ----- indian Production in year 2001 ------
...:
...: df_Production_2001 = (
...: sum(df_andaman['Production'][df_andaman['Crop_Year'] == 2001])
...: + sum(df_andhra['Production'][df_andhra['Crop_Year'] == 2001])
...: + sum(df_arunachal['Production'][df_arunachal['Crop_Year'] == 2001])
...: + sum(df_assam['Production'][df_assam['Crop_Year'] == 2001])
...: + sum(df_bihar['Production'][df_bihar['Crop_Year'] == 2001])
...: + sum(df_candigarh['Production'][df_candigarh['Crop_Year'] == 2001])
...: + sum(df_chattisgarh['Production'][df_chattisgarh['Crop_Year'] == 2001])
...: + sum(df_dadra['Production'][df_dadra['Crop_Year'] == 2001])
...: + sum(df_goa['Production'][df_goa['Crop_Year'] == 2001])
...: + sum(df_gujrat['Production'][df_gujrat['Crop_Year'] == 2001])
...: + sum(df_haryana['Production'][df_haryana['Crop_Year'] == 2001])
...: + sum(df_himachal['Production'][df_himachal['Crop_Year'] == 2001])
...: + sum(df_jammu['Production'][df_jammu['Crop_Year'] == 2001])
...: + sum(df_jharkand['Production'][df_jharkand['Crop_Year'] == 2001])
...: + sum(df_karnataka['Production'][df_karnataka['Crop_Year'] == 2001])
...: + sum(df_kerala['Production'][df_kerala['Crop_Year'] == 2001])
...: + sum(df_madhya['Production'][df_madhya['Crop_Year'] == 2001])
...: + sum(df_maharashtra['Production'][df_maharashtra['Crop_Year'] == 2001])
...: + sum(df_manipur['Production'][df_manipur['Crop_Year'] == 2001])
...: + sum(df_meghalaya['Production'][df_meghalaya['Crop_Year'] == 2001])
...: + sum(df_mizoram['Production'][df_mizoram['Crop_Year'] == 2001])
...: + sum(df_nagaland['Production'][df_nagaland['Crop_Year'] == 2001])
...: + sum(df_odisha['Production'][df_odisha['Crop_Year'] == 2001])
...: + sum(df_puducherry['Production'][df_puducherry['Crop_Year'] == 2001])
...: + sum(df_punjab['Production'][df_punjab['Crop_Year'] == 2001])
...: + sum(df_rajasthan['Production'][df_rajasthan['Crop_Year'] == 2001])
...: + sum(df_sikkim['Production'][df_sikkim['Crop_Year'] == 2001])
...: + sum(df_tamilnadu['Production'][df_tamilnadu['Crop_Year'] == 2001])
...: + sum(df_telangana['Production'][df_telangana['Crop_Year'] == 2001])
...: + sum(df_tripura['Production'][df_tripura['Crop_Year'] == 2001])
...: + sum(df_uttar['Production'][df_uttar['Crop_Year'] == 2001])
...: + sum(df_uttarakhand['Production'][df_uttarakhand['Crop_Year'] == 2001])
...: + sum(df_wb['Production'][df_wb['Crop_Year'] == 2001])
...: )
...:
...: # ----- indian Production in year 2002 ------
...:
...: df_Production_2002 = (
...: sum(df_andaman['Production'][df_andaman['Crop_Year'] == 2002])
...: + sum(df_andhra['Production'][df_andhra['Crop_Year'] == 2002])
...: + sum(df_arunachal['Production'][df_arunachal['Crop_Year'] == 2002])
...: + sum(df_assam['Production'][df_assam['Crop_Year'] == 2002])
...: + sum(df_bihar['Production'][df_bihar['Crop_Year'] == 2002])
...: + sum(df_candigarh['Production'][df_candigarh['Crop_Year'] == 2002])
...: + sum(df_chattisgarh['Production'][df_chattisgarh['Crop_Year'] == 2002])
...: + sum(df_dadra['Production'][df_dadra['Crop_Year'] == 2002])
...: + sum(df_goa['Production'][df_goa['Crop_Year'] == 2002])
...: + sum(df_gujrat['Production'][df_gujrat['Crop_Year'] == 2002])
...: + sum(df_haryana['Production'][df_haryana['Crop_Year'] == 2002])
...: + sum(df_himachal['Production'][df_himachal['Crop_Year'] == 2002])
...: + sum(df_jammu['Production'][df_jammu['Crop_Year'] == 2002])
...: + sum(df_jharkand['Production'][df_jharkand['Crop_Year'] == 2002])
...: + sum(df_karnataka['Production'][df_karnataka['Crop_Year'] == 2002])
...: + sum(df_kerala['Production'][df_kerala['Crop_Year'] == 2002])
...: + sum(df_madhya['Production'][df_madhya['Crop_Year'] == 2002])
...: + sum(df_maharashtra['Production'][df_maharashtra['Crop_Year'] == 2002])
...: + sum(df_manipur['Production'][df_manipur['Crop_Year'] == 2002])
...: + sum(df_meghalaya['Production'][df_meghalaya['Crop_Year'] == 2002])
...: + sum(df_mizoram['Production'][df_mizoram['Crop_Year'] == 2002])
...: + sum(df_nagaland['Production'][df_nagaland['Crop_Year'] == 2002])
...: + sum(df_odisha['Production'][df_odisha['Crop_Year'] == 2002])
...: + sum(df_puducherry['Production'][df_puducherry['Crop_Year'] == 2002])
...: + sum(df_punjab['Production'][df_punjab['Crop_Year'] == 2002])
...: + sum(df_rajasthan['Production'][df_rajasthan['Crop_Year'] == 2002])
...: + sum(df_sikkim['Production'][df_sikkim['Crop_Year'] == 2002])
...: + sum(df_tamilnadu['Production'][df_tamilnadu['Crop_Year'] == 2002])
...: + sum(df_telangana['Production'][df_telangana['Crop_Year'] == 2002])
...: + sum(df_tripura['Production'][df_tripura['Crop_Year'] == 2002])
...: + sum(df_uttar['Production'][df_uttar['Crop_Year'] == 2002])
...: + sum(df_uttarakhand['Production'][df_uttarakhand['Crop_Year'] == 2002])
...: + sum(df_wb['Production'][df_wb['Crop_Year'] == 2002])
...: )
...:
...: # ----- indian Production in year 2003 ------
...:
...: df_Production_2003 = (
...: sum(df_andaman['Production'][df_andaman['Crop_Year'] == 2003])
...: + sum(df_andhra['Production'][df_andhra['Crop_Year'] == 2003])
...: + sum(df_arunachal['Production'][df_arunachal['Crop_Year'] == 2003])
...: + sum(df_assam['Production'][df_assam['Crop_Year'] == 2003])
...: + sum(df_bihar['Production'][df_bihar['Crop_Year'] == 2003])
...: + sum(df_candigarh['Production'][df_candigarh['Crop_Year'] == 2003])
...: + sum(df_chattisgarh['Production'][df_chattisgarh['Crop_Year'] == 2003])
...: + sum(df_dadra['Production'][df_dadra['Crop_Year'] == 2003])
...: + sum(df_goa['Production'][df_goa['Crop_Year'] == 2003])
...: + sum(df_gujrat['Production'][df_gujrat['Crop_Year'] == 2003])
...: + sum(df_haryana['Production'][df_haryana['Crop_Year'] == 2003])
...: + sum(df_himachal['Production'][df_himachal['Crop_Year'] == 2003])
...: + sum(df_jammu['Production'][df_jammu['Crop_Year'] == 2003])
...: + sum(df_jharkand['Production'][df_jharkand['Crop_Year'] == 2003])
...: + sum(df_karnataka['Production'][df_karnataka['Crop_Year'] == 2003])
...: + sum(df_kerala['Production'][df_kerala['Crop_Year'] == 2003])
...: + sum(df_madhya['Production'][df_madhya['Crop_Year'] == 2003])
...: + sum(df_maharashtra['Production'][df_maharashtra['Crop_Year'] == 2003])
...: + sum(df_manipur['Production'][df_manipur['Crop_Year'] == 2003])
...: + sum(df_meghalaya['Production'][df_meghalaya['Crop_Year'] == 2003])
...: + sum(df_mizoram['Production'][df_mizoram['Crop_Year'] == 2003])
...: + sum(df_nagaland['Production'][df_nagaland['Crop_Year'] == 2003])
...: + sum(df_odisha['Production'][df_odisha['Crop_Year'] == 2003])
...: + sum(df_puducherry['Production'][df_puducherry['Crop_Year'] == 2003])
...: + sum(df_punjab['Production'][df_punjab['Crop_Year'] == 2003])
...: + sum(df_rajasthan['Production'][df_rajasthan['Crop_Year'] == 2003])
...: + sum(df_sikkim['Production'][df_sikkim['Crop_Year'] == 2003])
...: + sum(df_tamilnadu['Production'][df_tamilnadu['Crop_Year'] == 2003])
...: + sum(df_telangana['Production'][df_telangana['Crop_Year'] == 2003])
...: + sum(df_tripura['Production'][df_tripura['Crop_Year'] == 2003])
...: + sum(df_uttar['Production'][df_uttar['Crop_Year'] == 2003])
...: + sum(df_uttarakhand['Production'][df_uttarakhand['Crop_Year'] == 2003])
...: + sum(df_wb['Production'][df_wb['Crop_Year'] == 2003])
...: )
...:
...: # ----- indian Production in year 2004 ------
...:
...: df_Production_2004 = (
...: sum(df_andaman['Production'][df_andaman['Crop_Year'] == 2004])
...: + sum(df_andhra['Production'][df_andhra['Crop_Year'] == 2004])
...: + sum(df_arunachal['Production'][df_arunachal['Crop_Year'] == 2004])
...: + sum(df_assam['Production'][df_assam['Crop_Year'] == 2004])
...: + sum(df_bihar['Production'][df_bihar['Crop_Year'] == 2004])
...: + sum(df_candigarh['Production'][df_candigarh['Crop_Year'] == 2004])
...: + sum(df_chattisgarh['Production'][df_chattisgarh['Crop_Year'] == 2004])
...: + sum(df_dadra['Production'][df_dadra['Crop_Year'] == 2004])
...: + sum(df_goa['Production'][df_goa['Crop_Year'] == 2004])
...: + sum(df_gujrat['Production'][df_gujrat['Crop_Year'] == 2004])
...: + sum(df_haryana['Production'][df_haryana['Crop_Year'] == 2004])
...: + sum(df_himachal['Production'][df_himachal['Crop_Year'] == 2004])
...: + sum(df_jammu['Production'][df_jammu['Crop_Year'] == 2004])
...: + sum(df_jharkand['Production'][df_jharkand['Crop_Year'] == 2004])
...: + sum(df_karnataka['Production'][df_karnataka['Crop_Year'] == 2004])
...: + sum(df_kerala['Production'][df_kerala['Crop_Year'] == 2004])
...: + sum(df_madhya['Production'][df_madhya['Crop_Year'] == 2004])
...: + sum(df_maharashtra['Production'][df_maharashtra['Crop_Year'] == 2004])
...: + sum(df_manipur['Production'][df_manipur['Crop_Year'] == 2004])
...: + sum(df_meghalaya['Production'][df_meghalaya['Crop_Year'] == 2004])
...: + sum(df_mizoram['Production'][df_mizoram['Crop_Year'] == 2004])
...: + sum(df_nagaland['Production'][df_nagaland['Crop_Year'] == 2004])
...: + sum(df_odisha['Production'][df_odisha['Crop_Year'] == 2004])
...: + sum(df_puducherry['Production'][df_puducherry['Crop_Year'] == 2004])
...: + sum(df_punjab['Production'][df_punjab['Crop_Year'] == 2004])
...: + sum(df_rajasthan['Production'][df_rajasthan['Crop_Year'] == 2004])
...: + sum(df_sikkim['Production'][df_sikkim['Crop_Year'] == 2004])
...: + sum(df_tamilnadu['Production'][df_tamilnadu['Crop_Year'] == 2004])
...: + sum(df_telangana['Production'][df_telangana['Crop_Year'] == 2004])
...: + sum(df_tripura['Production'][df_tripura['Crop_Year'] == 2004])
...: + sum(df_uttar['Production'][df_uttar['Crop_Year'] == 2004])
...: + sum(df_uttarakhand['Production'][df_uttarakhand['Crop_Year'] == 2004])
...: + sum(df_wb['Production'][df_wb['Crop_Year'] == 2004])
...: )
...:
...: # ----- indian Production in year 2005 ------
...:
...: df_Production_2005 = (
...: sum(df_andaman['Production'][df_andaman['Crop_Year'] == 2005])
...: + sum(df_andhra['Production'][df_andhra['Crop_Year'] == 2005])
...: + sum(df_arunachal['Production'][df_arunachal['Crop_Year'] == 2005])
...: + sum(df_assam['Production'][df_assam['Crop_Year'] == 2005])
...: + sum(df_bihar['Production'][df_bihar['Crop_Year'] == 2005])
...: + sum(df_candigarh['Production'][df_candigarh['Crop_Year'] == 2005])
...: + sum(df_chattisgarh['Production'][df_chattisgarh['Crop_Year'] == 2005])
...: + sum(df_dadra['Production'][df_dadra['Crop_Year'] == 2005])
...: + sum(df_goa['Production'][df_goa['Crop_Year'] == 2005])
...: + sum(df_gujrat['Production'][df_gujrat['Crop_Year'] == 2005])
...: + sum(df_haryana['Production'][df_haryana['Crop_Year'] == 2005])
...: + sum(df_himachal['Production'][df_himachal['Crop_Year'] == 2005])
...: + sum(df_jammu['Production'][df_jammu['Crop_Year'] == 2005])
...: + sum(df_jharkand['Production'][df_jharkand['Crop_Year'] == 2005])
...: + sum(df_karnataka['Production'][df_karnataka['Crop_Year'] == 2005])
...: + sum(df_kerala['Production'][df_kerala['Crop_Year'] == 2005])
...: + sum(df_madhya['Production'][df_madhya['Crop_Year'] == 2005])
...: + sum(df_maharashtra['Production'][df_maharashtra['Crop_Year'] == 2005])
...: + sum(df_manipur['Production'][df_manipur['Crop_Year'] == 2005])
...: + sum(df_meghalaya['Production'][df_meghalaya['Crop_Year'] == 2005])
...: + sum(df_mizoram['Production'][df_mizoram['Crop_Year'] == 2005])
...: + sum(df_nagaland['Production'][df_nagaland['Crop_Year'] == 2005])
...: + sum(df_odisha['Production'][df_odisha['Crop_Year'] == 2005])
...: + sum(df_puducherry['Production'][df_puducherry['Crop_Year'] == 2005])
...: + sum(df_punjab['Production'][df_punjab['Crop_Year'] == 2005])
...: + sum(df_rajasthan['Production'][df_rajasthan['Crop_Year'] == 2005])
...: + sum(df_sikkim['Production'][df_sikkim['Crop_Year'] == 2005])
...: + sum(df_tamilnadu['Production'][df_tamilnadu['Crop_Year'] == 2005])
...: + sum(df_telangana['Production'][df_telangana['Crop_Year'] == 2005])
...: + sum(df_tripura['Production'][df_tripura['Crop_Year'] == 2005])
...: + sum(df_uttar['Production'][df_uttar['Crop_Year'] == 2005])
...: + sum(df_uttarakhand['Production'][df_uttarakhand['Crop_Year'] == 2005])
...: + sum(df_wb['Production'][df_wb['Crop_Year'] == 2005])
...: )
...:
...: # ----- indian Production in year 2006 ------
...:
...: df_Production_2006 = (
...: sum(df_andaman['Production'][df_andaman['Crop_Year'] == 2006])
...: + sum(df_andhra['Production'][df_andhra['Crop_Year'] == 2006])
...: + sum(df_arunachal['Production'][df_arunachal['Crop_Year'] == 2006])
...: + sum(df_assam['Production'][df_assam['Crop_Year'] == 2006])
...: + sum(df_bihar['Production'][df_bihar['Crop_Year'] == 2006])
...: + sum(df_candigarh['Production'][df_candigarh['Crop_Year'] == 2006])
...: + sum(df_chattisgarh['Production'][df_chattisgarh['Crop_Year'] == 2006])
...: + sum(df_dadra['Production'][df_dadra['Crop_Year'] == 2006])
...: + sum(df_goa['Production'][df_goa['Crop_Year'] == 2006])
...: + sum(df_gujrat['Production'][df_gujrat['Crop_Year'] == 2006])
...: + sum(df_haryana['Production'][df_haryana['Crop_Year'] == 2006])
...: + sum(df_himachal['Production'][df_himachal['Crop_Year'] == 2006])
...: + sum(df_jammu['Production'][df_jammu['Crop_Year'] == 2006])
...: + sum(df_jharkand['Production'][df_jharkand['Crop_Year'] == 2006])
...: + sum(df_karnataka['Production'][df_karnataka['Crop_Year'] == 2006])
...: + sum(df_kerala['Production'][df_kerala['Crop_Year'] == 2006])
...: + sum(df_madhya['Production'][df_madhya['Crop_Year'] == 2006])
...: + sum(df_maharashtra['Production'][df_maharashtra['Crop_Year'] == 2006])
...: + sum(df_manipur['Production'][df_manipur['Crop_Year'] == 2006])
...: + sum(df_meghalaya['Production'][df_meghalaya['Crop_Year'] == 2006])
...: + sum(df_mizoram['Production'][df_mizoram['Crop_Year'] == 2006])
...: + sum(df_nagaland['Production'][df_nagaland['Crop_Year'] == 2006])
...: + sum(df_odisha['Production'][df_odisha['Crop_Year'] == 2006])
...: + sum(df_puducherry['Production'][df_puducherry['Crop_Year'] == 2006])
...: + sum(df_punjab['Production'][df_punjab['Crop_Year'] == 2006])
...: + sum(df_rajasthan['Production'][df_rajasthan['Crop_Year'] == 2006])
...: + sum(df_sikkim['Production'][df_sikkim['Crop_Year'] == 2006])
...: + sum(df_tamilnadu['Production'][df_tamilnadu['Crop_Year'] == 2006])
...: + sum(df_telangana['Production'][df_telangana['Crop_Year'] == 2006])
...: + sum(df_tripura['Production'][df_tripura['Crop_Year'] == 2006])
...: + sum(df_uttar['Production'][df_uttar['Crop_Year'] == 2006])
...: + sum(df_uttarakhand['Production'][df_uttarakhand['Crop_Year'] == 2006])
...: + sum(df_wb['Production'][df_wb['Crop_Year'] == 2006])
...: )
...:
...: # ----- indian Production in year 2007 ------
...:
...: df_Production_2007 = (
...: sum(df_andaman['Production'][df_andaman['Crop_Year'] == 2007])
...: + sum(df_andhra['Production'][df_andhra['Crop_Year'] == 2007])
...: + sum(df_arunachal['Production'][df_arunachal['Crop_Year'] == 2007])
...: + sum(df_assam['Production'][df_assam['Crop_Year'] == 2007])
...: + sum(df_bihar['Production'][df_bihar['Crop_Year'] == 2007])
...: + sum(df_candigarh['Production'][df_candigarh['Crop_Year'] == 2007])
...: + sum(df_chattisgarh['Production'][df_chattisgarh['Crop_Year'] == 2007])
...: + sum(df_dadra['Production'][df_dadra['Crop_Year'] == 2007])
...: + sum(df_goa['Production'][df_goa['Crop_Year'] == 2007])
...: + sum(df_gujrat['Production'][df_gujrat['Crop_Year'] == 2007])
...: + sum(df_haryana['Production'][df_haryana['Crop_Year'] == 2007])
...: + sum(df_himachal['Production'][df_himachal['Crop_Year'] == 2007])
...: + sum(df_jammu['Production'][df_jammu['Crop_Year'] == 2007])
...: + sum(df_jharkand['Production'][df_jharkand['Crop_Year'] == 2007])
...: + sum(df_karnataka['Production'][df_karnataka['Crop_Year'] == 2007])
...: + sum(df_kerala['Production'][df_kerala['Crop_Year'] == 2007])
...: + sum(df_madhya['Production'][df_madhya['Crop_Year'] == 2007])
...: + sum(df_maharashtra['Production'][df_maharashtra['Crop_Year'] == 2007])
...: + sum(df_manipur['Production'][df_manipur['Crop_Year'] == 2007])
...: + sum(df_meghalaya['Production'][df_meghalaya['Crop_Year'] == 2007])
...: + sum(df_mizoram['Production'][df_mizoram['Crop_Year'] == 2007])
...: + sum(df_nagaland['Production'][df_nagaland['Crop_Year'] == 2007])
...: + sum(df_odisha['Production'][df_odisha['Crop_Year'] == 2007])
...: + sum(df_puducherry['Production'][df_puducherry['Crop_Year'] == 2007])
...: + sum(df_punjab['Production'][df_punjab['Crop_Year'] == 2007])
...: + sum(df_rajasthan['Production'][df_rajasthan['Crop_Year'] == 2007])
...: + sum(df_sikkim['Production'][df_sikkim['Crop_Year'] == 2007])
...: + sum(df_tamilnadu['Production'][df_tamilnadu['Crop_Year'] == 2007])
...: + sum(df_telangana['Production'][df_telangana['Crop_Year'] == 2007])
...: + sum(df_tripura['Production'][df_tripura['Crop_Year'] == 2007])
...: + sum(df_uttar['Production'][df_uttar['Crop_Year'] == 2007])
...: + sum(df_uttarakhand['Production'][df_uttarakhand['Crop_Year'] == 2007])
...: + sum(df_wb['Production'][df_wb['Crop_Year'] == 2007])
...: )
...:
...: # ----- indian Production in year 2008 ------
...:
...: df_Production_2008 = (
...: sum(df_andaman['Production'][df_andaman['Crop_Year'] == 2008])
...: + sum(df_andhra['Production'][df_andhra['Crop_Year'] == 2008])
...: + sum(df_arunachal['Production'][df_arunachal['Crop_Year'] == 2008])
...: + sum(df_assam['Production'][df_assam['Crop_Year'] == 2008])
...: + sum(df_bihar['Production'][df_bihar['Crop_Year'] == 2008])
...: + sum(df_candigarh['Production'][df_candigarh['Crop_Year'] == 2008])
...: + sum(df_chattisgarh['Production'][df_chattisgarh['Crop_Year'] == 2008])
...: + sum(df_dadra['Production'][df_dadra['Crop_Year'] == 2008])
...: + sum(df_goa['Production'][df_goa['Crop_Year'] == 2008])
...: + sum(df_gujrat['Production'][df_gujrat['Crop_Year'] == 2008])
...: + sum(df_haryana['Production'][df_haryana['Crop_Year'] == 2008])
...: + sum(df_himachal['Production'][df_himachal['Crop_Year'] == 2008])
...: + sum(df_jammu['Production'][df_jammu['Crop_Year'] == 2008])
...: + sum(df_jharkand['Production'][df_jharkand['Crop_Year'] == 2008])
...: + sum(df_karnataka['Production'][df_karnataka['Crop_Year'] == 2008])
...: + sum(df_kerala['Production'][df_kerala['Crop_Year'] == 2008])
...: + sum(df_madhya['Production'][df_madhya['Crop_Year'] == 2008])
...: + sum(df_maharashtra['Production'][df_maharashtra['Crop_Year'] == 2008])
...: + sum(df_manipur['Production'][df_manipur['Crop_Year'] == 2008])
...: + sum(df_meghalaya['Production'][df_meghalaya['Crop_Year'] == 2008])
...: + sum(df_mizoram['Production'][df_mizoram['Crop_Year'] == 2008])
...: + sum(df_nagaland['Production'][df_nagaland['Crop_Year'] == 2008])
...: + sum(df_odisha['Production'][df_odisha['Crop_Year'] == 2008])
...: + sum(df_puducherry['Production'][df_puducherry['Crop_Year'] == 2008])
...: + sum(df_punjab['Production'][df_punjab['Crop_Year'] == 2008])
...: + sum(df_rajasthan['Production'][df_rajasthan['Crop_Year'] == 2008])
...: + sum(df_sikkim['Production'][df_sikkim['Crop_Year'] == 2008])
...: + sum(df_tamilnadu['Production'][df_tamilnadu['Crop_Year'] == 2008])
...: + sum(df_telangana['Production'][df_telangana['Crop_Year'] == 2008])
...: + sum(df_tripura['Production'][df_tripura['Crop_Year'] == 2008])
...: + sum(df_uttar['Production'][df_uttar['Crop_Year'] == 2008])
...: + sum(df_uttarakhand['Production'][df_uttarakhand['Crop_Year'] == 2008])
...: + sum(df_wb['Production'][df_wb['Crop_Year'] == 2008])
...: )
...:
...: # ----- indian Production in year 2009 ------
...:
...: df_Production_2009 = (
...: sum(df_andaman['Production'][df_andaman['Crop_Year'] == 2009])
...: + sum(df_andhra['Production'][df_andhra['Crop_Year'] == 2009])
...: + sum(df_arunachal['Production'][df_arunachal['Crop_Year'] == 2009])
...: + sum(df_assam['Production'][df_assam['Crop_Year'] == 2009])
...: + sum(df_bihar['Production'][df_bihar['Crop_Year'] == 2009])
...: + sum(df_candigarh['Production'][df_candigarh['Crop_Year'] == 2009])
...: + sum(df_chattisgarh['Production'][df_chattisgarh['Crop_Year'] == 2009])
...: + sum(df_dadra['Production'][df_dadra['Crop_Year'] == 2009])
...: + sum(df_goa['Production'][df_goa['Crop_Year'] == 2009])
...: + sum(df_gujrat['Production'][df_gujrat['Crop_Year'] == 2009])
...: + sum(df_haryana['Production'][df_haryana['Crop_Year'] == 2009])
...: + sum(df_himachal['Production'][df_himachal['Crop_Year'] == 2009])
...: + sum(df_jammu['Production'][df_jammu['Crop_Year'] == 2009])
...: + sum(df_jharkand['Production'][df_jharkand['Crop_Year'] == 2009])
...: + sum(df_karnataka['Production'][df_karnataka['Crop_Year'] == 2009])
...: + sum(df_kerala['Production'][df_kerala['Crop_Year'] == 2009])
...: + sum(df_madhya['Production'][df_madhya['Crop_Year'] == 2009])
...: + sum(df_maharashtra['Production'][df_maharashtra['Crop_Year'] == 2009])
...: + sum(df_manipur['Production'][df_manipur['Crop_Year'] == 2009])
...: + sum(df_meghalaya['Production'][df_meghalaya['Crop_Year'] == 2009])
...: + sum(df_mizoram['Production'][df_mizoram['Crop_Year'] == 2009])
...: + sum(df_nagaland['Production'][df_nagaland['Crop_Year'] == 2009])
...: + sum(df_odisha['Production'][df_odisha['Crop_Year'] == 2009])
...: + sum(df_puducherry['Production'][df_puducherry['Crop_Year'] == 2009])
...: + sum(df_punjab['Production'][df_punjab['Crop_Year'] == 2009])
...: + sum(df_rajasthan['Production'][df_rajasthan['Crop_Year'] == 2009])
...: + sum(df_sikkim['Production'][df_sikkim['Crop_Year'] == 2009])
...: + sum(df_tamilnadu['Production'][df_tamilnadu['Crop_Year'] == 2009])
...: + sum(df_telangana['Production'][df_telangana['Crop_Year'] == 2009])
...: + sum(df_tripura['Production'][df_tripura['Crop_Year'] == 2009])
...: + sum(df_uttar['Production'][df_uttar['Crop_Year'] == 2009])
...: + sum(df_uttarakhand['Production'][df_uttarakhand['Crop_Year'] == 2009])
...: + sum(df_wb['Production'][df_wb['Crop_Year'] == 2009])
...: )
...:
...: # ----- indian Production in year 2010 ------
...:
...: df_Production_2010 = (
...: sum(df_andaman['Production'][df_andaman['Crop_Year'] == 2010])
...: + sum(df_andhra['Production'][df_andhra['Crop_Year'] == 2010])
...: + sum(df_arunachal['Production'][df_arunachal['Crop_Year'] == 2010])
...: + sum(df_assam['Production'][df_assam['Crop_Year'] == 2010])
...: + sum(df_bihar['Production'][df_bihar['Crop_Year'] == 2010])
...: + sum(df_candigarh['Production'][df_candigarh['Crop_Year'] == 2010])
...: + sum(df_chattisgarh['Production'][df_chattisgarh['Crop_Year'] == 2010])
...: + sum(df_dadra['Production'][df_dadra['Crop_Year'] == 2010])
...: + sum(df_goa['Production'][df_goa['Crop_Year'] == 2010])
...: + sum(df_gujrat['Production'][df_gujrat['Crop_Year'] == 2010])
...: + sum(df_haryana['Production'][df_haryana['Crop_Year'] == 2010])
...: + sum(df_himachal['Production'][df_himachal['Crop_Year'] == 2010])
...: + sum(df_jammu['Production'][df_jammu['Crop_Year'] == 2010])
...: + sum(df_jharkand['Production'][df_jharkand['Crop_Year'] == 2010])
...: + sum(df_karnataka['Production'][df_karnataka['Crop_Year'] == 2010])
...: + sum(df_kerala['Production'][df_kerala['Crop_Year'] == 2010])
...: + sum(df_madhya['Production'][df_madhya['Crop_Year'] == 2010])
...: + sum(df_maharashtra['Production'][df_maharashtra['Crop_Year'] == 2010])
...: + sum(df_manipur['Production'][df_manipur['Crop_Year'] == 2010])
...: + sum(df_meghalaya['Production'][df_meghalaya['Crop_Year'] == 2010])
...: + sum(df_mizoram['Production'][df_mizoram['Crop_Year'] == 2010])
...: + sum(df_nagaland['Production'][df_nagaland['Crop_Year'] == 2010])
...: + sum(df_odisha['Production'][df_odisha['Crop_Year'] == 2010])
...: + sum(df_puducherry['Production'][df_puducherry['Crop_Year'] == 2010])
...: + sum(df_punjab['Production'][df_punjab['Crop_Year'] == 2010])
...: + sum(df_rajasthan['Production'][df_rajasthan['Crop_Year'] == 2010])
...: + sum(df_sikkim['Production'][df_sikkim['Crop_Year'] == 2010])
...: + sum(df_tamilnadu['Production'][df_tamilnadu['Crop_Year'] == 2010])
...: + sum(df_telangana['Production'][df_telangana['Crop_Year'] == 2010])
...: + sum(df_tripura['Production'][df_tripura['Crop_Year'] == 2010])
...: + sum(df_uttar['Production'][df_uttar['Crop_Year'] == 2010])
...: + sum(df_uttarakhand['Production'][df_uttarakhand['Crop_Year'] == 2010])
...: + sum(df_wb['Production'][df_wb['Crop_Year'] == 2010])
...: )
...:
...: def production_area_data():
...:
...: print("\n----------------------------------------------------")
...: print("\tArea of production in India (2000 to 2010)")
...: print("-------------------------------------------------------\n")
...: print("[01] Area of production in year 2000 =",df_area_2000)
...: print("[02] Area of production in year 2001 =",df_area_2001)
...: print("[03] Area of production in year 2002 =",df_area_2002)
...: print("[04] Area of production in year 2003 =",df_area_2003)
...: print("[05] Area of production in year 2004 =",df_area_2004)
...: print("[06] Area of production in year 2005 =",df_area_2005)
...: print("[07] Area of production in year 2006 =",df_area_2006)
...: print("[08] Area of production in year 2007 =",df_area_2007)
...: print("[09] Area of production in year 2008 =",df_area_2008)
...: print("[10] Area of production in year 2009 =",df_area_2009)
...: print("[11] Area of production in year 2010 =",df_area_2010)
...: print("-------------------------------------------------------\n")
...:
...:
...: def production_data():
...:
...: print("\n----------------------------------------------------")
...: print("\tProduction in India (2000 to 2010)")
...: print("-------------------------------------------------------\n")
...: print("[01] Production in year 2000 =",df_Production_2000)
...: print("[02] Production in year 2001 =",df_Production_2001)
...: print("[03] Production in year 2002 =",df_Production_2002)
...: print("[04] Production in year 2003 =",df_Production_2003)
...: print("[05] Production in year 2004 =",df_Production_2004)
...: print("[06] Production in year 2005 =",df_Production_2005)
...: print("[07] Production in year 2006 =",df_Production_2006)
...: print("[08] Production in year 2007 =",df_Production_2007)
...: print("[09] Production in year 2008 =",df_Production_2008)
...: print("[10] Production in year 2009 =",df_Production_2009)
...: print("[11] Production in year 2010 =",df_Production_2010)
...: print("-------------------------------------------------------\n")
...:
...:
...: df_year = (2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010)
...: df_area_2000_10 = (df_area_2000//pow(10,5),
...: df_area_2001//pow(10,5),
...: df_area_2002//pow(10,5),
...: df_area_2003//pow(10,5),
...: df_area_2004//pow(10,5),
...: df_area_2005//pow(10,5),
...: df_area_2005//pow(10,5),
...: df_area_2005//pow(10,5),
...: df_area_2005//pow(10,5),
...: df_area_2009//pow(10,5),
...: df_area_2010//pow(10,5))
...:
...: df_Production_2000_10 = (
...: df_Production_2000//pow(10,6),
...: df_Production_2001//pow(10,6),
...: df_Production_2002//pow(10,6),
...: df_Production_2003//pow(10,6),
...: df_Production_2004//pow(10,6),
...: df_Production_2006//pow(10,6),
...: df_Production_2006//pow(10,6),
...: df_Production_2006//pow(10,6),
...: df_Production_2006//pow(10,6),
...: df_Production_2009//pow(10,6),
...: df_Production_2010//pow(10,6))
...:
...: def area_and_production_plot():
...:
...: plt.title("Crop_year vs. (production_area & production)[2000 to 2010]")
...: plt.plot(df_year,df_area_2000_10,linestyle="dashed",label="Area * 10^5")
...: plt.plot(df_year,df_Production_2000_10,label="Production * 10^6")
...: plt.legend()
...: plt.show()
...:
...:
...:
...: # [area of production] predection model design using linear regression
...:
...: def estimate_coef(x, y):
...:
...: n = np.size(x) # size of data set
...: mean_x = np.mean(x)
...: mean_y = np.mean(y)
...: xy = np.sum(y*x) - n*(mean_y)*(mean_x)
...: xx = np.sum(x*x) - n*(mean_x)*(mean_x)
...: b_1 = xy / xx
...: b_0 = (mean_y) - b_1*(mean_x)
...: return(b_0,b_1)
...:
...: def production_area_prediction_2023(x,y,b,bm,a):
...:
...: # Create perdiction upto 2023
...: # Using Equation : y = b_0 + (b_1 * x)
...:
...: df_area_2010_1 = df_area_2010//pow(10,5)
...: df_area_2011 = bm + (a * 2011)
...: df_area_2012 = bm + (a * 2012)
...: df_area_2013 = bm + (a * 2013)
...: df_area_2014 = bm + (a * 2014)
...: df_area_2015 = bm + (a * 2015)
...: df_area_2016 = bm + (a * 2016)
...: df_area_2017 = bm + (a * 2017)
...: df_area_2018 = bm + (a * 2018)
...: df_area_2019 = bm + (a * 2019)
...: df_area_2020 = bm + (a * 2020)
...: df_area_2021 = bm + (a * 2021)
...: df_area_2022 = bm + (a * 2022)
...: df_area_2023 = bm + (a * 2023)
...: df_area_2024 = bm + (a * 2024)
...: df_area_2025 = bm + (a * 2025)
...:
...: df_2000_2023 =(df_area_2010_1,
...: df_area_2011,df_area_2012,df_area_2013,
...: df_area_2014,df_area_2015,df_area_2016,
...: df_area_2017,df_area_2018,df_area_2019,
...: df_area_2020,df_area_2021,df_area_2022,
...: df_area_2023,df_area_2024,df_area_2025 )
...:
...: df_year1 = (2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021,
...: 2022,2023,2024,2025)
...:
...: # plot regression line
...:
...: plt.plot(x, y, color = "m",marker = "o",label="Actual production area * 10^5")
...: y_pred = b[0] + b[1]*x
...: plt.plot(x, y_pred, color = "g",label="Regression line")
...: plt.xlabel('year -- >')
...: plt.ylabel('Area of production --->')
...:
...: # plot prediction line
...:
...: plt.title("Prediction for production area from 2011 to 2025 ")
...: plt.plot(df_year1,df_2000_2023,linestyle="dashed",label="Predicted production area * 10^5")
...: plt.legend()
...: plt.show()
...:
...: mean_y = np.mean(y)
...: area_growth_2011 = (((df_area_2011)-(mean_y))/(mean_y))*100
...: area_growth_2012 = (((df_area_2012)-(mean_y))/(mean_y))*100
...: area_growth_2013 = (((df_area_2013)-(mean_y))/(mean_y))*100
...: area_growth_2014 = (((df_area_2014)-(mean_y))/(mean_y))*100
...: area_growth_2015 = (((df_area_2015)-(mean_y))/(mean_y))*100
...: area_growth_2016 = (((df_area_2016)-(mean_y))/(mean_y))*100
...: area_growth_2017 = (((df_area_2017)-(mean_y))/(mean_y))*100
...: area_growth_2018 = (((df_area_2018)-(mean_y))/(mean_y))*100
...: area_growth_2019 = (((df_area_2019)-(mean_y))/(mean_y))*100
...: area_growth_2020 = (((df_area_2020)-(mean_y))/(mean_y))*100
...: area_growth_2021 = (((df_area_2021)-(mean_y))/(mean_y))*100
...: area_growth_2022 = (((df_area_2022)-(mean_y))/(mean_y))*100
...: area_growth_2023 = (((df_area_2023)-(mean_y))/(mean_y))*100
...: area_growth_2024 = (((df_area_2024)-(mean_y))/(mean_y))*100
...: area_growth_2025 = (((df_area_2025)-(mean_y))/(mean_y))*100
...:
...:
...: print("\n----------------------------------------------------------")
...: print("production area growth according to prediction model")
...: print("--------------------------------------------------------\n")
...: print("Production area gowth in 2011 = ",area_growth_2011,"%")
...: print("Production area gowth in 2012 = ",area_growth_2012,"%")
...: print("Production area gowth in 2013 = ",area_growth_2013,"%")
...: print("Production area gowth in 2014 = ",area_growth_2014,"%")
...: print("Production area gowth in 2015 = ",area_growth_2015,"%")
...: print("Production area gowth in 2016 = ",area_growth_2016,"%")
...: print("Production area gowth in 2017 = ",area_growth_2017,"%")
...: print("Production area gowth in 2018 = ",area_growth_2018,"%")
...: print("Production area gowth in 2019 = ",area_growth_2019,"%")
...: print("Production area gowth in 2020 = ",area_growth_2020,"%")
...: print("Production area gowth in 2021 = ",area_growth_2021,"%")
...: print("Production area gowth in 2022 = ",area_growth_2022,"%")
...: print("Production area gowth in 2023 = ",area_growth_2023,"%")
...: print("Production area gowth in 2024 = ",area_growth_2024,"%")
...: print("Production area gowth in 2025 = ",area_growth_2025,"%")
...: print("--------------------------------------------------------\n")
...:
...:
...: #---- Production prediction model -----
...:
...: def estimate_coef1(x, y):
...:
...: n = np.size(x) # size of data set
...: mean_x = np.mean(x)
...: mean_y = np.mean(y)
...: xy = np.sum(y*x) - n*(mean_y)*(mean_x)
...: xx = np.sum(x*x) - n*(mean_x)*(mean_x)
...: b_1 = xy / xx
...: b_0 = (mean_y) - b_1*(mean_x)
...: return(b_0,b_1)
...:
...: def Production_prediction_2023(x,y,b,bm,a):
...:
...: # Create perdiction upto 2023
...: # Using Equation : y = b_0 + (b_1 * x)
...:
...: df_Production_2010_1 = df_Production_2010//pow(10,6)
...: df_Production_2011 = bm + (a * 2011)
...: df_Production_2012 = bm + (a * 2012)
...: df_Production_2013 = bm + (a * 2013)
...: df_Production_2014 = bm + (a * 2014)
...: df_Production_2015 = bm + (a * 2015)
...: df_Production_2016 = bm + (a * 2016)
...: df_Production_2017 = bm + (a * 2017)
...: df_Production_2018 = bm + (a * 2018)
...: df_Production_2019 = bm + (a * 2019)
...: df_Production_2020 = bm + (a * 2020)
...: df_Production_2021 = bm + (a * 2021)
...: df_Production_2022 = bm + (a * 2022)
...: df_Production_2023 = bm + (a * 2023)
...: df_Production_2024 = bm + (a * 2024)
...: df_Production_2025 = bm + (a * 2025)
...:
...: df_production_2000_2023 =(df_Production_2010_1,
...: df_Production_2011,df_Production_2012,df_Production_2013,
...: df_Production_2014,df_Production_2015,df_Production_2016,
...: df_Production_2017,df_Production_2018,df_Production_2019,
...: df_Production_2020,df_Production_2021,df_Production_2022,
...: df_Production_2023,df_Production_2024,df_Production_2025 )
...:
...: df_year1 = (2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021,
...: 2022,2023,2024,2025)
...:
...: # plot regression line
...:
...: plt.plot(x, y, color = "m",marker = "o",label="Actual production * 10^6")
...: y_pred = b[0] + b[1]*x
...: plt.plot(x, y_pred, color = "g",label="Regression line")
...: plt.xlabel('year -- >')
...: plt.ylabel('production --->')
...:
...: # plot prediction line
...:
...: plt.title("Prediction for Crop Production from 2011 to 2025 ")
...: plt.plot(df_year1,df_production_2000_2023,linestyle="dashed",label="Predicted Production * 10^6")
...: plt.legend()
...: plt.show()
...:
...: mean_y = np.mean(y)
...: Production_growth_2011 = (((df_Production_2011)-(mean_y))/(mean_y))*100
...: Production_growth_2012 = (((df_Production_2012)-(mean_y))/(mean_y))*100
...: Production_growth_2013 = (((df_Production_2013)-(mean_y))/(mean_y))*100
...: Production_growth_2014 = (((df_Production_2014)-(mean_y))/(mean_y))*100
...: Production_growth_2015 = (((df_Production_2015)-(mean_y))/(mean_y))*100
...: Production_growth_2016 = (((df_Production_2016)-(mean_y))/(mean_y))*100
...: Production_growth_2017 = (((df_Production_2017)-(mean_y))/(mean_y))*100
...: Production_growth_2018 = (((df_Production_2018)-(mean_y))/(mean_y))*100
...: Production_growth_2019 = (((df_Production_2019)-(mean_y))/(mean_y))*100
...: Production_growth_2020 = (((df_Production_2020)-(mean_y))/(mean_y))*100
...: Production_growth_2021 = (((df_Production_2021)-(mean_y))/(mean_y))*100
...: Production_growth_2022 = (((df_Production_2022)-(mean_y))/(mean_y))*100
...: Production_growth_2023 = (((df_Production_2023)-(mean_y))/(mean_y))*100
...: Production_growth_2024 = (((df_Production_2024)-(mean_y))/(mean_y))*100
...: Production_growth_2025 = (((df_Production_2025)-(mean_y))/(mean_y))*100
...:
...: print("\n----------------------------------------------------------")
...: print("Production growth according to prediction model")
...: print("--------------------------------------------------------\n")
...: print("Production gowth in 2011 = ",Production_growth_2011,"%")
...: print("Production gowth in 2012 = ",Production_growth_2012,"%")
...: print("Production gowth in 2013 = ",Production_growth_2013,"%")
...: print("Production gowth in 2014 = ",Production_growth_2014,"%")
...: print("Production gowth in 2015 = ",Production_growth_2015,"%")
...: print("Production gowth in 2016 = ",Production_growth_2016,"%")
...: print("Production gowth in 2017 = ",Production_growth_2017,"%")
...: print("Production gowth in 2018 = ",Production_growth_2018,"%")
...: print("Production gowth in 2019 = ",Production_growth_2019,"%")
...: print("Production gowth in 2020 = ",Production_growth_2020,"%")
...: print("Production gowth in 2021 = ",Production_growth_2021,"%")
...: print("Production gowth in 2022 = ",Production_growth_2022,"%")
...: print("Production gowth in 2023 = ",Production_growth_2023,"%")
...: print("Production gowth in 2024 = ",Production_growth_2024,"%")
...: print("Production gowth in 2025 = ",Production_growth_2025,"%")
...: print("--------------------------------------------------------\n")
...:
...:
...: def main():
...:
...: ch = 1
...: while (ch == 1):
...: print("-----------------------------------------")
...: print("\tMain menu (Agriculture Data Aanlysis) :")
...: print("-----------------------------------------\n")
...: print("[1] Print dimension of the data file")
...: print("[2] Print column names of the data file")
...: print("[3] Print available states in the data")
...: print("[4] Print districts of all states")
...: print("[5] Print production data")
...: print("[6] Print production area data")
...: print("[7] Plot area and production (without prediction)")
...: print("[8] Area prediction model and print growth rate")
...: print("[9] Production prediction model and print growth rate")
...: print("---------------------------------------")
...:
...: op = input("enter your option : ")
...: op1 = int(op,10)
...:
...: if op1 == 1:
...: dimension()
...:
...: elif op1 == 2:
...: column_names()
...:
...: elif op1 == 3:
...: states()
...:
...: elif op1 == 4 :
...: district_of_all_states()
...:
...: elif op1 == 5:
...: production_data()
...:
...: elif op1 == 6:
...: production_area_data()
...:
...: elif op1 == 7:
...: area_and_production_plot()
...: break
...:
...: elif op1 == 8:
...: x = np.array(df_year)
...: y = np.array(df_area_2000_10)
...: b = estimate_coef(x, y)
...: print("\n----------------------")
...: print("Estimated coefficients for area:\n----------------------\n")
...: print("b_0 = {}\nb_1 = {}".format(b[0], b[1]))
...: print("----------------------\n")
...: production_area_prediction_2023(x,y,b,b[0],b[1])
...: break
...:
...: elif op1 == 9 :
...: x = np.array(df_year)
...: y = np.array(df_Production_2000_10)
...: b = estimate_coef1(x, y)
...: print("\n----------------------")
...: print("Estimated coefficients for production:\n----------------------\n")
...: print("b_0 = {}\nb_1 = {}".format(b[0], b[1]))
...: print("----------------------\n")
...: Production_prediction_2023(x,y,b,b[0],b[1])
...: break
...:
...:
...: else:
...: print("wrong input !!")
...:
...: #------- exit process design --------
...:
...: ch1 = input("enter:\n[0] for exit\n[1] for continue : ")
...: ch =int(ch1,10)
...:
...: if __name__ == "__main__":
...: main()
...:
...:
------- output data :-----------
Agriculture data analysis:
-----------------------------------------
Main menu (Agriculture Data Aanlysis) :
-----------------------------------------
[1] Print dimension of the data file
[2] Print column names of the data file
[3] Print available states in the data
[4] Print districts of all states
[5] Print production data
[6] Print production area data
[7] Plot area and production (without prediction)
[8] Area prediction model and print growth rate
[9] Production prediction model and print growth rate
---------------------------------------
enter your option : 1
---------------------------------------------
Dimension of the data frame = (246091, 7)
---------------------------------------------
enter:
[0] for exit
[1] for continue : 1
-----------------------------------------
Main menu (Agriculture Data Aanlysis) :
-----------------------------------------
[1] Print dimension of the data file
[2] Print column names of the data file
[3] Print available states in the data
[4] Print districts of all states
[5] Print production data
[6] Print production area data
[7] Plot area and production (without prediction)
[8] Area prediction model and print growth rate
[9] Production prediction model and print growth rate
---------------------------------------
enter your option : 2
Column names as follows :
---------------------------------------------
1 --> State_Name
2 --> District_Name
3 --> Crop_Year
4 --> Season
5 --> Crop
6 --> Area
7 --> Production
---------------------------------------------
enter:
[0] for exit
[1] for continue : 1
-----------------------------------------
Main menu (Agriculture Data Aanlysis) :
-----------------------------------------
[1] Print dimension of the data file
[2] Print column names of the data file
[3] Print available states in the data
[4] Print districts of all states
[5] Print production data
[6] Print production area data
[7] Plot area and production (without prediction)
[8] Area prediction model and print growth rate
[9] Production prediction model and print growth rate
---------------------------------------
enter your option : 3
states names :
-------------------------------
District_Name
State_Name
Andaman and Nicobar Islands 203
Andhra Pradesh 9628
Arunachal Pradesh 2546
Assam 14628
Bihar 18885
Chandigarh 90
Chhattisgarh 10709
Dadra and Nagar Haveli 263
Goa 208
Gujarat 8436
Haryana 5875
Himachal Pradesh 2494
Jammu and Kashmir 1634
Jharkhand 1266
Karnataka 21122
Kerala 4261
Madhya Pradesh 22943
Maharashtra 12628
Manipur 1267
Meghalaya 2867
Mizoram 957
Nagaland 3906
Odisha 13575
Puducherry 876
Punjab 3173
Rajasthan 12514
Sikkim 714
Tamil Nadu 13547
Telangana 5649
Tripura 1412
Uttar Pradesh 33306
Uttarakhand 4896
West Bengal 9613
-------------------------------
total no. of states = 33
-----------------------------
enter:
[0] for exit
[1] for continue : 1
-----------------------------------------
Main menu (Agriculture Data Aanlysis) :
-----------------------------------------
[1] Print dimension of the data file
[2] Print column names of the data file
[3] Print available states in the data
[4] Print districts of all states
[5] Print production data
[6] Print production area data
[7] Plot area and production (without prediction)
[8] Area prediction model and print growth rate
[9] Production prediction model and print growth rate
---------------------------------------
enter your option : 4
Crop_Year
District_Name
NICOBARS 79
NORTH AND MIDDLE ANDAMAN 50
SOUTH ANDAMANS 74
-----------------------------
total no. of district in state - 1 : Andaman and Nicobar Islands = 3
-----------------------------
Crop_Year
District_Name
ANANTAPUR 788
CHITTOOR 789
EAST GODAVARI 765
GUNTUR 687
KADAPA 824
KRISHNA 636
KURNOOL 828
PRAKASAM 808
SPSR NELLORE 682
SRIKAKULAM 689
VISAKHAPATANAM 812
VIZIANAGARAM 741
WEST GODAVARI 579
-----------------------------
total no. of district in state -2 : Andhra Pradesh = 13
-----------------------------
Crop_Year
District_Name
ANJAW 102
CHANGLANG 189
DIBANG VALLEY 147
EAST KAMENG 158
EAST SIANG 190
KURUNG KUMEY 99
LOHIT 190
LONGDING 13
LOWER DIBANG VALLEY 130
LOWER SUBANSIRI 170
NAMSAI 1
PAPUM PARE 184
TAWANG 159
TIRAP 160
UPPER SIANG 146
UPPER SUBANSIRI 169
WEST KAMENG 153
WEST SIANG 186
-----------------------------
total no. of district in state -3 : Arunachal Pradesh = 18
-----------------------------
Crop_Year
District_Name
BAKSA 351
BARPETA 592
BONGAIGAON 586
CACHAR 575
CHIRANG 349
DARRANG 579
DHEMAJI 590
DHUBRI 572
DIBRUGARH 539
DIMA HASAO 574
GOALPARA 590
GOLAGHAT 580
HAILAKANDI 535
JORHAT 552
KAMRUP 591
KAMRUP METRO 348
KARBI ANGLONG 588
KARIMGANJ 515
KOKRAJHAR 591
LAKHIMPUR 590
MARIGAON 592
NAGAON 591
NALBARI 591
SIVASAGAR 564
SONITPUR 590
TINSUKIA 571
UDALGURI 342
-----------------------------
total no. of district in state -4 : Assam = 27
-----------------------------
Crop_Year
District_Name
ARARIA 566
ARWAL 297
AURANGABAD 447
BANKA 437
BEGUSARAI 590
BHAGALPUR 611
BHOJPUR 497
BUXAR 413
DARBHANGA 500
GAYA 576
GOPALGANJ 523
JAMUI 378
JEHANABAD 478
KAIMUR (BHABUA) 403
KATIHAR 579
KHAGARIA 578
KISHANGANJ 549
LAKHISARAI 275
MADHEPURA 539
MADHUBANI 587
MUNGER 408
MUZAFFARPUR 554
NALANDA 460
NAWADA 518
PASHCHIM CHAMPARAN 574
PATNA 453
PURBI CHAMPARAN 550
PURNIA 546
ROHTAS 498
SAHARSA 505
SAMASTIPUR 577
SARAN 506
SHEIKHPURA 297
SHEOHAR 417
SITAMARHI 530
SIWAN 551
SUPAUL 546
VAISHALI 572
-----------------------------
total no. of district in state -5 : Bihar = 38
-----------------------------
Crop_Year
District_Name
CHANDIGARH 90
-----------------------------
total no. of district in state -6 : Chandigarh = 1
-----------------------------
Crop_Year
District_Name
BALOD 135
BALODA BAZAR 150
BALRAMPUR 161
BASTAR 583
BEMETARA 139
BIJAPUR 246
BILASPUR 591
DANTEWADA 497
DHAMTARI 559
DURG 568
GARIYABAND 155
JANJGIR-CHAMPA 559
JASHPUR 568
KABIRDHAM 509
KANKER 548
KONDAGAON 145
KORBA 555
KOREA 504
MAHASAMUND 537
MUNGELI 127
NARAYANPUR 236
RAIGARH 583
RAIPUR 584
RAJNANDGAON 617
SUKMA 117
SURAJPUR 151
SURGUJA 585
-----------------------------
total no. of district in state -7 : Chhattisgarh = 27
-----------------------------
Crop_Year
District_Name
DADRA AND NAGAR HAVELI 263
-----------------------------
total no. of district in state -8 : Dadra and Nagar Haveli = 1
-----------------------------
Crop_Year
District_Name
NORTH GOA 107
SOUTH GOA 101
-----------------------------
total no. of district in state -9 : Goa = 2
-----------------------------
Crop_Year
District_Name
AHMADABAD 372
AMRELI 348
ANAND 247
BANAS KANTHA 396
BHARUCH 410
BHAVNAGAR 345
DANG 238
DOHAD 218
GANDHINAGAR 329
JAMNAGAR 369
JUNAGADH 365
KACHCHH 316
KHEDA 454
MAHESANA 422
NARMADA 200
NAVSARI 210
PANCH MAHALS 482
PATAN 199
PORBANDAR 166
RAJKOT 346
SABAR KANTHA 395
SURAT 406
SURENDRANAGAR 330
TAPI 118
VADODARA 461
VALSAD 294
-----------------------------
total no. of district in state -10: Gujarat = 26
-----------------------------
Crop_Year
District_Name
AMBALA 320
BHIWANI 306
FARIDABAD 277
FATEHABAD 302
GURGAON 285
HISAR 313
JHAJJAR 270
JIND 265
KAITHAL 305
KARNAL 334
KURUKSHETRA 290
MAHENDRAGARH 246
MEWAT 135
PALWAL 78
PANCHKULA 325
PANIPAT 316
REWARI 285
ROHTAK 278
SIRSA 312
SONIPAT 307
YAMUNANAGAR 326
-----------------------------
total no. of district in state -11: Haryana = 21
-----------------------------
Crop_Year
District_Name
BILASPUR 224
CHAMBA 220
HAMIRPUR 196
KANGRA 262
KINNAUR 113
KULLU 211
LAHUL AND SPITI 79
MANDI 260
SHIMLA 249
SIRMAUR 258
SOLAN 219
UNA 203
-----------------------------
total no. of district in state -12: Himachal Pradesh = 12
-----------------------------
Empty DataFrame
Columns: [Crop_Year]
Index: []
-----------------------------
total no. of district in state -13: Jammu and Kashmir = 0
-----------------------------
Crop_Year
District_Name
BOKARO 45
CHATRA 59
DEOGHAR 50
DHANBAD 38
DUMKA 59
EAST SINGHBUM 45
GARHWA 66
GIRIDIH 58
GODDA 68
GUMLA 67
HAZARIBAGH 77
JAMTARA 44
KHUNTI 6
KODERMA 56
LATEHAR 61
LOHARDAGA 61
PAKUR 57
PALAMU 72
RAMGARH 6
RANCHI 72
SAHEBGANJ 52
SARAIKELA KHARSAWAN 42
SIMDEGA 54
WEST SINGHBHUM 51
-----------------------------
total no. of district in state -14: Jharkhand = 24
-----------------------------
Crop_Year
District_Name
BAGALKOT 733
BANGALORE RURAL 794
BELGAUM 925
BELLARY 887
BENGALURU URBAN 672
BIDAR 781
BIJAPUR 699
CHAMARAJANAGAR 844
CHIKBALLAPUR 328
CHIKMAGALUR 820
CHITRADURGA 840
DAKSHIN KANNAD 366
DAVANGERE 886
DHARWAD 825
GADAG 755
GULBARGA 833
HASSAN 895
HAVERI 870
KODAGU 423
KOLAR 629
KOPPAL 678
MANDYA 780
MYSORE 832
RAICHUR 691
RAMANAGARA 310
SHIMOGA 825
TUMKUR 936
UDUPI 376
UTTAR KANNAD 677
YADGIR 212
-----------------------------
total no. of district in state -15: Karnataka = 30
-----------------------------
Empty DataFrame
Columns: [Crop_Year]
Index: []
-----------------------------
total no. of district in state -16: Kerala = 0
-----------------------------
Crop_Year
District_Name
AGAR MALWA 28
ALIRAJPUR 178
ANUPPUR 342
ASHOKNAGAR 290
BALAGHAT 572
BARWANI 501
BETUL 505
BHIND 404
BHOPAL 410
BURHANPUR 348
CHHATARPUR 516
CHHINDWARA 573
DAMOH 498
DATIA 436
DEWAS 473
DHAR 562
DINDORI 405
GUNA 445
GWALIOR 401
HARDA 486
HOSHANGABAD 543
INDORE 427
JABALPUR 500
JHABUA 495
KATNI 469
KHANDWA 577
KHARGONE 517
MANDLA 482
MANDSAUR 455
MORENA 395
NARSINGHPUR 493
NEEMUCH 416
PANNA 485
RAISEN 485
RAJGARH 433
RATLAM 515
REWA 510
SAGAR 535
SATNA 504
SEHORE 448
SEONI 544
SHAHDOL 514
SHAJAPUR 456
SHEOPUR 370
SHIVPURI 490
SIDHI 498
SINGRAULI 183
TIKAMGARH 485
UJJAIN 458
UMARIA 441
VIDISHA 447
-----------------------------
total no. of district in state -17: Madhya Pradesh = 51
-----------------------------
Crop_Year
District_Name
AHMEDNAGAR 495
AKOLA 355
AMRAVATI 400
AURANGABAD 432
BEED 464
BHANDARA 288
BULDHANA 403
CHANDRAPUR 325
DHULE 418
GADCHIROLI 306
GONDIA 229
HINGOLI 377
JALGAON 402
JALNA 435
KOLHAPUR 409
LATUR 466
MUMBAI 1
NAGPUR 388
NANDED 461
NANDURBAR 377
NASHIK 459
OSMANABAD 461
PALGHAR 11
PARBHANI 467
PUNE 495
RAIGAD 236
RATNAGIRI 213
SANGLI 452
SATARA 478
SINDHUDURG 207
SOLAPUR 458
THANE 268
WARDHA 328
WASHIM 307
YAVATMAL 357
-----------------------------
total no. of district in state -18: Maharashtra = 35
-----------------------------
Crop_Year
District_Name
BISHNUPUR 150
CHANDEL 163
CHURACHANDPUR 150
IMPHAL EAST 129
IMPHAL WEST 124
SENAPATI 164
TAMENGLONG 118
THOUBAL 130
UKHRUL 139
-----------------------------
total no. of district in state -19: Manipur = 9
-----------------------------
Crop_Year
District_Name
EAST GARO HILLS 471
EAST JAINTIA HILLS 329
EAST KHASI HILLS 307
NORTH GARO HILLS 57
RI BHOI 305
SOUTH GARO HILLS 437
SOUTH WEST GARO HILLS 59
SOUTH WEST KHASI HILLS 35
WEST GARO HILLS 485
WEST JAINTIA HILLS 37
WEST KHASI HILLS 345
-----------------------------
total no. of district in state -20: Meghalaya = 11
-----------------------------
Crop_Year
District_Name
AIZAWL 179
CHAMPHAI 141
KOLASIB 178
LAWNGTLAI 58
LUNGLEI 184
MAMIT 107
SAIHA 69
SERCHHIP 41
-----------------------------
total no. of district in state -21: Mizoram = 8
-----------------------------
Crop_Year
District_Name
DIMAPUR 414
KIPHIRE 174
KOHIMA 430
LONGLENG 156
MOKOKCHUNG 435
MON 439
PEREN 178
PHEK 421
TUENSANG 393
WOKHA 455
ZUNHEBOTO 411
-----------------------------
total no. of district in state -22: Nagaland = 11
-----------------------------
Crop_Year
District_Name
ANUGUL 497
BALANGIR 518
BALESHWAR 383
BARGARH 462
BHADRAK 342
BOUDH 472
CUTTACK 478
DEOGARH 470
DHENKANAL 471
GAJAPATI 457
GANJAM 526
JAGATSINGHAPUR 351
JAJAPUR 418
JHARSUGUDA 393
KALAHANDI 497
KANDHAMAL 442
KENDRAPARA 342
KENDUJHAR 517
KHORDHA 438
KORAPUT 510
MALKANGIRI 380
MAYURBHANJ 526
NABARANGPUR 454
NAYAGARH 463
NUAPADA 477
PURI 342
RAYAGADA 518
SAMBALPUR 481
SONEPUR 436
SUNDARGARH 514
-----------------------------
total no. of district in state -23: Odisha = 30
-----------------------------
Crop_Year
District_Name
KARAIKAL 214
MAHE 89
PONDICHERRY 447
YANAM 126
-----------------------------
total no. of district in state -24: Puducherry = 4
-----------------------------
Crop_Year
District_Name
AMRITSAR 194
BARNALA 67
BATHINDA 184
FARIDKOT 143
FATEHGARH SAHIB 112
FAZILKA 25
FIROZEPUR 192
GURDASPUR 169
HOSHIARPUR 205
JALANDHAR 171
KAPURTHALA 123
LUDHIANA 185
MANSA 181
MOGA 149
MUKTSAR 184
NAWANSHAHR 127
PATHANKOT 26
PATIALA 186
RUPNAGAR 173
S.A.S NAGAR 76
SANGRUR 214
TARN TARAN 87
-----------------------------
total no. of district in state -25: Punjab = 22
-----------------------------
Crop_Year
District_Name
AJMER 409
ALWAR 436
BANSWARA 416
BARAN 417
BARMER 271
BHARATPUR 429
BHILWARA 446
BIKANER 337
BUNDI 422
CHITTORGARH 455
CHURU 272
DAUSA 384
DHOLPUR 391
DUNGARPUR 386
GANGANAGAR 402
HANUMANGARH 368
JAIPUR 409
JAISALMER 266
JALORE 379
JHALAWAR 419
JHUNJHUNU 340
JODHPUR 323
KARAULI 399
KOTA 388
NAGAUR 356
PALI 402
PRATAPGARH 90
RAJSAMAND 415
SAWAI MADHOPUR 419
SIKAR 363
SIROHI 411
TONK 436
UDAIPUR 458
-----------------------------
total no. of district in state -26: Rajasthan = 33
-----------------------------
Crop_Year
District_Name
EAST DISTRICT 178
NORTH DISTRICT 171
SOUTH DISTRICT 183
WEST DISTRICT 182
-----------------------------
total no. of district in state -27: Sikkim = 4
-----------------------------
Crop_Year
District_Name
ARIYALUR 130
COIMBATORE 542
CUDDALORE 464
DHARMAPURI 543
DINDIGUL 600
ERODE 571
KANCHIPURAM 405
KANNIYAKUMARI 254
KARUR 462
KRISHNAGIRI 341
MADURAI 495
NAGAPATTINAM 321
NAMAKKAL 529
PERAMBALUR 480
PUDUKKOTTAI 434
RAMANATHAPURAM 377
SALEM 571
SIVAGANGA 422
THANJAVUR 417
THE NILGIRIS 310
THENI 520
THIRUVALLUR 396
THIRUVARUR 300
TIRUCHIRAPPALLI 507
TIRUNELVELI 533
TIRUPPUR 143
TIRUVANNAMALAI 496
TUTICORIN 476
VELLORE 521
VILLUPURAM 494
VIRUDHUNAGAR 493
-----------------------------
total no. of district in state -28: Tamil Nadu = 31
-----------------------------
Empty DataFrame
Columns: [Crop_Year]
Index: []
-----------------------------
total no. of district in state -29: Telangana = 0
-----------------------------
Crop_Year
District_Name
DHALAI 293
GOMATI 61
KHOWAI 61
NORTH TRIPURA 294
SEPAHIJALA 57
SOUTH TRIPURA 293
UNAKOTI 62
WEST TRIPURA 291
-----------------------------
total no. of district in state -30: Tripura = 8
-----------------------------
Crop_Year
District_Name
AGRA 468
ALIGARH 479
ALLAHABAD 470
AMBEDKAR NAGAR 456
AMETHI 135
AMROHA 364
AURAIYA 465
AZAMGARH 525
BAGHPAT 372
BAHRAICH 533
BALLIA 474
BALRAMPUR 421
BANDA 472
BARABANKI 518
BAREILLY 496
BASTI 454
BIJNOR 433
BUDAUN 526
BULANDSHAHR 467
CHANDAULI 432
CHITRAKOOT 434
DEORIA 474
ETAH 502
ETAWAH 451
FAIZABAD 453
FARRUKHABAD 489
FATEHPUR 524
FIROZABAD 491
GAUTAM BUDDHA NAGAR 345
GHAZIABAD 381
... ...
KHERI 533
KUSHI NAGAR 507
LALITPUR 463
LUCKNOW 497
MAHARAJGANJ 480
MAHOBA 443
MAINPURI 471
MATHURA 427
MAU 450
MEERUT 410
MIRZAPUR 481
MORADABAD 483
MUZAFFARNAGAR 436
PILIBHIT 461
PRATAPGARH 467
RAE BARELI 531
RAMPUR 468
SAHARANPUR 420
SAMBHAL 83
SANT KABEER NAGAR 449
SANT RAVIDAS NAGAR 395
SHAHJAHANPUR 529
SHAMLI 66
SHRAVASTI 444
SIDDHARTH NAGAR 402
SITAPUR 543
SONBHADRA 473
SULTANPUR 506
UNNAO 526
VARANASI 452
[75 rows x 1 columns]
-----------------------------
total no. of district in state -31: Uttar Pradesh = 75
-----------------------------
Crop_Year
District_Name
ALMORA 350
BAGESHWAR 343
CHAMOLI 351
CHAMPAWAT 415
DEHRADUN 453
HARIDWAR 357
NAINITAL 437
PAURI GARHWAL 379
PITHORAGARH 376
RUDRA PRAYAG 316
TEHRI GARHWAL 366
UDAM SINGH NAGAR 396
UTTAR KASHI 357
-----------------------------
total no. of district in state -32: Uttarakhand = 13
-----------------------------
Crop_Year
District_Name
24 PARAGANAS NORTH 460
24 PARAGANAS SOUTH 431
BANKURA 626
BARDHAMAN 603
BIRBHUM 568
COOCHBEHAR 573
DARJEELING 573
DINAJPUR DAKSHIN 397
DINAJPUR UTTAR 597
HOOGHLY 425
HOWRAH 371
JALPAIGURI 633
MALDAH 561
MEDINIPUR EAST 347
MEDINIPUR WEST 637
MURSHIDABAD 591
NADIA 580
PURULIA 640
-----------------------------
total no. of district in state -33: West Bengal = 18
-----------------------------
enter:
[0] for exit
[1] for continue : 1
-----------------------------------------
Main menu (Agriculture Data Aanlysis) :
-----------------------------------------
[1] Print dimension of the data file
[2] Print column names of the data file
[3] Print available states in the data
[4] Print districts of all states
[5] Print production data
[6] Print production area data
[7] Plot area and production (without prediction)
[8] Area prediction model and print growth rate
[9] Production prediction model and print growth rate
---------------------------------------
enter your option : 5
----------------------------------------------------
Production in India (2000 to 2010)
-------------------------------------------------------
[01] Production in year 2000 = 1883519069.1
[02] Production in year 2001 = 1955689097.27
[03] Production in year 2002 = 1958751431.84
[04] Production in year 2003 = 2010500154.33
[05] Production in year 2004 = 2161040382.86
[06] Production in year 2005 = 1689035541.54
[07] Production in year 2006 = 2598469881.78
[08] Production in year 2007 = 1216119801.48
[09] Production in year 2008 = 1887112471.66
[10] Production in year 2009 = 1969812788.81
[11] Production in year 2010 = 1000088480.41
-------------------------------------------------------
enter:
[0] for exit
[1] for continue : 1
-----------------------------------------
Main menu (Agriculture Data Aanlysis) :
-----------------------------------------
[1] Print dimension of the data file
[2] Print column names of the data file
[3] Print available states in the data
[4] Print districts of all states
[5] Print production data
[6] Print production area data
[7] Plot area and production (without prediction)
[8] Area prediction model and print growth rate
[9] Production prediction model and print growth rate
---------------------------------------
enter your option : 6
----------------------------------------------------
Area of production in India (2000 to 2010)
-------------------------------------------------------
[01] Area of production in year 2000 = 157470764.0
[02] Area of production in year 2001 = 157316957.67
[03] Area of production in year 2002 = 149786100.21
[04] Area of production in year 2003 = 164353080.54
[05] Area of production in year 2004 = 160911265.73
[06] Area of production in year 2005 = 155857115.32
[07] Area of production in year 2006 = 164304587.65
[08] Area of production in year 2007 = 145658432.3
[09] Area of production in year 2008 = 164283774.0
[10] Area of production in year 2009 = 159606419.0
[11] Area of production in year 2010 = 169646509.92
-------------------------------------------------------
enter:
[0] for exit
[1] for continue : 1
-----------------------------------------
Main menu (Agriculture Data Aanlysis) :
-----------------------------------------
[1] Print dimension of the data file
[2] Print column names of the data file
[3] Print available states in the data
[4] Print districts of all states
[5] Print production data
[6] Print production area data
[7] Plot area and production (without prediction)
[8] Area prediction model and print growth rate
[9] Production prediction model and print growth rate
---------------------------------------
enter your option : 7
In [10]: main()
-----------------------------------------
Main menu (Agriculture Data Aanlysis) :
-----------------------------------------
[1] Print dimension of the data file
[2] Print column names of the data file
[3] Print available states in the data
[4] Print districts of all states
[5] Print production data
[6] Print production area data
[7] Plot area and production (without prediction)
[8] Area prediction model and print growth rate
[9] Production prediction model and print growth rate
---------------------------------------
enter your option : 8
----------------------
Estimated coefficients for area:
----------------------
b_0 = -10519.272727272726
b_1 = 6.036363636363636
----------------------
----------------------------------------------------------
production area growth according to prediction model
--------------------------------------------------------
Production area gowth in 2011 = 2.28702640643 %
Production area gowth in 2012 = 2.66819747417 %
Production area gowth in 2013 = 3.04936854191 %
Production area gowth in 2014 = 3.43053960964 %
Production area gowth in 2015 = 3.81171067738 %
Production area gowth in 2016 = 4.19288174512 %
Production area gowth in 2017 = 4.57405281286 %
Production area gowth in 2018 = 4.9552238806 %
Production area gowth in 2019 = 5.33639494834 %
Production area gowth in 2020 = 5.71756601607 %
Production area gowth in 2021 = 6.09873708381 %
Production area gowth in 2022 = 6.47990815155 %
Production area gowth in 2023 = 6.86107921929 %
Production area gowth in 2024 = 7.24225028703 %
Production area gowth in 2025 = 7.62342135476 %
--------------------------------------------------------
In [11]: main()
-----------------------------------------
Main menu (Agriculture Data Aanlysis) :
-----------------------------------------
[1] Print dimension of the data file
[2] Print column names of the data file
[3] Print available states in the data
[4] Print districts of all states
[5] Print production data
[6] Print production area data
[7] Plot area and production (without prediction)
[8] Area prediction model and print growth rate
[9] Production prediction model and print growth rate
---------------------------------------
enter your option : 9
----------------------
Estimated coefficients for production:
----------------------
b_0 = 17176.454545318742
b_1 = -7.509090909023176
----------------------
----------------------------------------------------------
Production growth according to prediction model
--------------------------------------------------------
Production gowth in 2011 = -2.12448559669 %
Production gowth in 2012 = -2.47856652947 %
Production gowth in 2013 = -2.83264746225 %
Production gowth in 2014 = -3.18672839503 %
Production gowth in 2015 = -3.54080932781 %
Production gowth in 2016 = -3.8948902606 %
Production gowth in 2017 = -4.24897119338 %
Production gowth in 2018 = -4.60305212616 %
Production gowth in 2019 = -4.95713305894 %
Production gowth in 2020 = -5.31121399172 %
Production gowth in 2021 = -5.6652949245 %
Production gowth in 2022 = -6.01937585728 %
Production gowth in 2023 = -6.37345679007 %
Production gowth in 2024 = -6.72753772285 %
Production gowth in 2025 = -7.08161865563 %
--------------------------------------------------------
In [12]: