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gst_data_prep2.py
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import pandas as pd
# import matplotlib.pyplot as plt
# import seaborn as sns
import numpy as np
import json
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
# Data Preparation to read Household Survey Files and generate csv files to use
"""
df_cons_summ_full = pd.read_stata('Summary of Consumer Expenditure - Block 12 - Level 11 - 68.dta')
print(df_cons_summ_full.dtypes)
df_cons_summ_full.to_csv('consumer_expend_summ_2011.csv')
df_cons_summ_all = df_cons_summ_full[["Srl_no", "Value", "HHID", "Sector"]]
df_cons_summ_all.to_csv('consumer_expend_summ_2011_short.csv', index=False)
household_bl3_l2_file = "Household Characteristics - Block 3 - Level 2 - 68.dta"
df_hh_bl3_l2_data = pd.read_stata(household_bl3_l2_file, preserve_dtypes=False)
print(df_hh_bl3_l2_data.dtypes)
df_hh_bl3_l2_data.to_csv('hh_characteristics_block3_level2_2011.csv', index=False)
df_hh_bl3_l2_data["URBAN"] = np.where(df_hh_bl3_l2_data["Sector"] == 2,
1, 0)
df_hh_bl3_l2_short = df_hh_bl3_l2_data[['HHID', 'HH_Size', 'URBAN',
'District', 'State_code', 'Combined_multiplier']]
df_hh_bl3_l2_short.to_csv('hh_characteristics_block3_level2_2011_short.csv')
# household_bl3_l3_file = "Household characteristics - Block 3 - Level 3.dta"
# df_hh_bl3_l3_data = pd.read_stata(household_bl3_l3_file, preserve_dtypes=False)
# print(df_hh_bl3_l3_data.dtypes)
# df_hh_bl3_l3_data.to_csv('hh_characteristics_block3_level3_2011.csv')
# df_hh_bl3_l3_short = df_hh_bl3_l3_data[['HHID', 'HH_Size',
# 'Combined_multiplier']]
"""
df_hh_bl3_l2_short = pd.read_csv('hh_characteristics_block3_level2_2011_short.csv')
df_hh_bl3_l2_short = df_hh_bl3_l2_short.drop('Unnamed: 0', axis=1)
df_cons_summ_all = pd.read_csv('consumer_expend_summ_2011_short.csv')
# df_rates = pd.read_csv('GST Rates India 2019-work.csv', encoding='cp1252')
df_rates = pd.read_csv('GST Rates India 2019-work.csv')
df_rates["Srl_no"] = df_rates["Srl_no"].fillna(0).astype(int)
df_rates_group = df_rates.groupby(['item_category_1'])['gst_rate'].mean()
df_rates_group = df_rates_group.to_frame()
df_rates_group = df_rates_group.reset_index()
df_rates_group = df_rates_group[~df_rates_group.item_category_1.str.contains(
"sub-total")]
df_item_category = df_rates[df_rates.item_category_1.str.contains(
"sub-total")]
df_item_category = df_item_category[['item_category_1', 'Srl_no', 'duration']]
df_item_category['item_category_1'] = df_item_category['item_category_1'].str.replace("sub-total: ", "")
df_item_rates_category = pd.merge(df_rates_group, df_item_category,
how="inner", on="item_category_1")
# df_item_rates_category.sort_values('Srl_no')
"""
Generate JSON File for Policy by looping through the variables
"""
df_item_rates_for_json = df_item_rates_category.pivot(columns='item_category_1', values='gst_rate')
df_item_rates_for_json.iloc[0,:]=df_item_rates_for_json[df_item_rates_for_json.columns].max()
df_item_rates_for_json = df_item_rates_for_json.iloc[0:1,:]
df_item_rates_for_json.columns= "_gst_rate_" + df_item_rates_for_json.columns
for i in range(0,17):
df_item_rates_for_json = pd.concat([df_item_rates_for_json, pd.DataFrame([[np.nan] *
df_item_rates_for_json.shape[1]], columns=df_item_rates_for_json.columns)], ignore_index=True)
#df_item_rates_for_json.iloc[13,:] = '['+ str(df_item_rates_for_json.iloc[0,:]) + ']'
df_item_rates_for_json.iloc[13,:] = df_item_rates_for_json.iloc[0,:]/100
d = [[i] for i in df_item_rates_for_json.iloc[13,:]]
df_item_rates_for_json.loc[len(df_item_rates_for_json)]=d
df_item_rates_for_json.iloc[13,:] = df_item_rates_for_json.iloc[18,:]
df_item_rates_for_json = df_item_rates_for_json[:-1]
df_item_rates_for_json['gst_rate_benchmark']= ""
row_label_year = [["2017"] for i in df_item_rates_for_json.iloc[13,:]]
range_rate = [{"min": 0, "max": 1} for i in df_item_rates_for_json.iloc[13,:]]
df_item_rates_for_json['gst_rate_benchmark']= ""
df_item_rates_for_json.iloc[0,:] = df_item_rates_for_json.columns.str.replace('_gst_rate_','GST Rate for ')
df_item_rates_for_json.iloc[1,:] = df_item_rates_for_json.columns.str.replace('_gst_rate_','GST Rate relevant for consumption of ')
df_item_rates_for_json.iloc[2,:] = "GST Rules"
df_item_rates_for_json.iloc[3,:] = ""
df_item_rates_for_json.iloc[4,:] = "AYEAR"
df_item_rates_for_json.iloc[5,:] = row_label_year
df_item_rates_for_json.iloc[6,:] = 2017
df_item_rates_for_json.iloc[7,:] = False
df_item_rates_for_json.iloc[8,:] = False
df_item_rates_for_json.iloc[9,:] = ""
df_item_rates_for_json.iloc[10,:] = ""
df_item_rates_for_json.iloc[11,:] = False
df_item_rates_for_json.iloc[12,:] = False
df_item_rates_for_json.iloc[14,:] = range_rate
df_item_rates_for_json.iloc[15,:] = ""
df_item_rates_for_json.iloc[16,:] = ""
df_item_rates_for_json.iloc[17,:] = "stop"
d=[0.18]
df_item_rates_for_json['gst_rate_benchmark']= ""
df_item_rates_for_json.iloc[0,df_item_rates_for_json.columns.get_loc('gst_rate_benchmark')]= "GST Benchmark Rate"
df_item_rates_for_json.iloc[1,df_item_rates_for_json.columns.get_loc('gst_rate_benchmark')]= "GST Benchmark Rate to calculate Policy Gap"
df_item_rates_for_json.iloc[2,df_item_rates_for_json.columns.get_loc('gst_rate_benchmark')]= "Benchmark Rate"
df_item_rates_for_json.iloc[3,df_item_rates_for_json.columns.get_loc('gst_rate_benchmark')]= ""
df_item_rates_for_json.iloc[4,df_item_rates_for_json.columns.get_loc('gst_rate_benchmark')]= "AYEAR"
df_item_rates_for_json.iloc[5,df_item_rates_for_json.columns.get_loc('gst_rate_benchmark')]= row_label_year
df_item_rates_for_json.iloc[6,df_item_rates_for_json.columns.get_loc('gst_rate_benchmark')]= 2017
df_item_rates_for_json.iloc[7,df_item_rates_for_json.columns.get_loc('gst_rate_benchmark')]= False
df_item_rates_for_json.iloc[8,df_item_rates_for_json.columns.get_loc('gst_rate_benchmark')]= False
df_item_rates_for_json.iloc[9,df_item_rates_for_json.columns.get_loc('gst_rate_benchmark')]= ""
df_item_rates_for_json.iloc[10,df_item_rates_for_json.columns.get_loc('gst_rate_benchmark')]= ""
df_item_rates_for_json.iloc[11,df_item_rates_for_json.columns.get_loc('gst_rate_benchmark')]= False
df_item_rates_for_json.iloc[12,df_item_rates_for_json.columns.get_loc('gst_rate_benchmark')]= False
df_item_rates_for_json.iloc[13,df_item_rates_for_json.columns.get_loc('gst_rate_benchmark')]= d
df_item_rates_for_json.iloc[14,df_item_rates_for_json.columns.get_loc('gst_rate_benchmark')]= range_rate
df_item_rates_for_json.iloc[15,df_item_rates_for_json.columns.get_loc('gst_rate_benchmark')]= ""
df_item_rates_for_json.iloc[16,df_item_rates_for_json.columns.get_loc('gst_rate_benchmark')]= ""
df_item_rates_for_json.iloc[17,df_item_rates_for_json.columns.get_loc('gst_rate_benchmark')]= "stop"
df_item_rates_for_json['ind'] = ""
df_item_rates_for_json.iloc[0, df_item_rates_for_json.columns.get_loc('ind')] = "long_name"
df_item_rates_for_json.iloc[1, df_item_rates_for_json.columns.get_loc('ind')] = "description"
df_item_rates_for_json.iloc[2, df_item_rates_for_json.columns.get_loc('ind')] = "itr_ref"
df_item_rates_for_json.iloc[3, df_item_rates_for_json.columns.get_loc('ind')] = "notes"
df_item_rates_for_json.iloc[4, df_item_rates_for_json.columns.get_loc('ind')] = "row_var"
df_item_rates_for_json.iloc[5, df_item_rates_for_json.columns.get_loc('ind')] = "row_label"
df_item_rates_for_json.iloc[6, df_item_rates_for_json.columns.get_loc('ind')] = "start_year"
df_item_rates_for_json.iloc[7, df_item_rates_for_json.columns.get_loc('ind')] = "cpi_inflatable"
df_item_rates_for_json.iloc[8, df_item_rates_for_json.columns.get_loc('ind')] = "cpi_inflated"
df_item_rates_for_json.iloc[9, df_item_rates_for_json.columns.get_loc('ind')] = "col_var"
df_item_rates_for_json.iloc[10, df_item_rates_for_json.columns.get_loc('ind')] = "col_label"
df_item_rates_for_json.iloc[11, df_item_rates_for_json.columns.get_loc('ind')] = "boolean_value"
df_item_rates_for_json.iloc[12, df_item_rates_for_json.columns.get_loc('ind')] = "integer_value"
df_item_rates_for_json.iloc[13, df_item_rates_for_json.columns.get_loc('ind')] = "value"
df_item_rates_for_json.iloc[14, df_item_rates_for_json.columns.get_loc('ind')] = "range"
df_item_rates_for_json.iloc[15, df_item_rates_for_json.columns.get_loc('ind')] = "out_of_range_minmsg"
df_item_rates_for_json.iloc[16, df_item_rates_for_json.columns.get_loc('ind')] = "out_of_range_maxmsg"
df_item_rates_for_json.iloc[17, df_item_rates_for_json.columns.get_loc('ind')] = "out_of_range_action"
df_item_rates_for_json.set_index('ind', inplace=True)
item_rates_dict_for_json = df_item_rates_for_json.to_dict()
with open('current_law_policy_pit_cit.json', 'r') as f:
current_law_policy_dict = json.load(f)
current_law_policy_dict.update(item_rates_dict_for_json)
with open("current_law_policy.json", "w") as f:
json.dump(current_law_policy_dict, f, indent=4, sort_keys=False)
# df_item_rates_for_json.to_json('gst_policy1.json')
"""
Generate gst.csv which contains consumption information from Summary table
Block 12 - One record for each household
"""
df_cons_summ = df_cons_summ_all[['HHID','Srl_no','Value']]
df_cons_summ = pd.merge(df_cons_summ, df_item_rates_category,
how="inner", on="Srl_no")
"""
Adjusting monthly consumption to yearly consumption for
certian items of monthly recall period
"""
df_cons_summ['Value'] = np.where(df_cons_summ['duration']=="monthly",
df_cons_summ['Value']*(365/30),
df_cons_summ['Value'])
"""
Gross Private Final Consumption Expenditure in 2011 and in
Assessment Year 2017 or financial year 2016 -
Source: Annual Estimate of GDP at Current Prices base 2011-12
Ministry of Statistics and Program Implementation MOSPI
mospi.nic.in/data
"""
HHS_TOTAL_WEIGHT = 2210659
GPFCE_2011 = 4910447
GPFCE_2016 = 9004904
GPFCE_2017 = 10083000
GPFCE_2018 = 11333000
INFLATOR_2011 = (GPFCE_2011/HHS_TOTAL_WEIGHT)
"""
Extraploating 2011 data to assessment year 2017
"""
#df_cons_summ['Value'] = df_cons_summ['Value'] * (GPFCE_2016/GPFCE_2011)
df_cons_summ['Value'] = df_cons_summ['Value'] * INFLATOR_2011
df_cons_summ['Value'] = df_cons_summ['Value'] * (GPFCE_2016/GPFCE_2011)
df_cons_summ['item_category_1'] = "cons_" + df_cons_summ['item_category_1']
df_cons_summ_trans = df_cons_summ.pivot(index='HHID', columns='item_category_1', values='Value')
df_cons_summ_trans = df_cons_summ_trans.fillna(0)
df_cons_summ_trans = df_cons_summ_trans.reset_index()
df_cons_summ_trans = pd.merge(df_cons_summ_trans, df_hh_bl3_l2_short,
how="inner", on="HHID")
df_cons_summ_trans.columns = df_cons_summ_trans.columns.str.upper()
df_cons_summ_trans = df_cons_summ_trans.rename(columns={'HHID': 'ID_NO'})
df_cons_summ_trans = df_cons_summ_trans.rename(columns={'COMBINED_MULTIPLIER': 'WEIGHT'})
df_cons_summ_trans['ASSESSMENT_YEAR'] = 2017
df_cons_summ_trans.to_csv('gst.csv', index=False)
"""
Generate JSON File for the gst record variables which declares all variables
used in gst.csv
"""
df_gst_for_json_read = df_cons_summ_trans.drop(df_cons_summ_trans.index)
df_gst_for_json_read = pd.concat([df_gst_for_json_read, pd.DataFrame([[np.nan] * df_gst_for_json_read.shape[1]], columns=df_gst_for_json_read.columns)], ignore_index=True)
df_gst_for_json_read = pd.concat([df_gst_for_json_read, pd.DataFrame([[np.nan] * df_gst_for_json_read.shape[1]], columns=df_gst_for_json_read.columns)], ignore_index=True)
df_gst_for_json_read = pd.concat([df_gst_for_json_read, pd.DataFrame([[np.nan] * df_gst_for_json_read.shape[1]], columns=df_gst_for_json_read.columns)], ignore_index=True)
form_cons_data = [{"2017": "Household Survey 48th Round Block 12"} for i in df_gst_for_json_read.iloc[0,:]]
form_hh_data = [{"2017": "Household Survey 48th Round Block 3 Level 2"} for i in df_gst_for_json_read.iloc[0,:]]
df_gst_for_json_read.loc[len(df_gst_for_json_read)]=form_hh_data
df_gst_for_json_read.loc[0, df_gst_for_json_read.columns.str.startswith('CONS_')]="float"
df_gst_for_json_read.loc[1, :] = df_gst_for_json_read.columns.str.replace('CONS_','CONSUMPTION OF ')
df_gst_for_json_read.iloc[2, :] = form_cons_data
df_gst_for_json_read = df_gst_for_json_read.rename(columns={'WEIGHT': 'weight'})
df_gst_for_json_read.iloc[0,df_gst_for_json_read.columns.get_loc('weight')]= "float"
df_gst_for_json_read.iloc[1,df_gst_for_json_read.columns.get_loc('weight')]= "Household unit sampling weight"
df_gst_for_json_read.at[2,'weight'] = df_gst_for_json_read.at[3,'weight']
df_gst_for_json_read.iloc[0,df_gst_for_json_read.columns.get_loc('ID_NO')]= "int"
df_gst_for_json_read.iloc[1,df_gst_for_json_read.columns.get_loc('ID_NO')]= "Household ID HHID"
df_gst_for_json_read.iloc[0,df_gst_for_json_read.columns.get_loc('HH_SIZE')]= "int"
df_gst_for_json_read.iloc[1,df_gst_for_json_read.columns.get_loc('HH_SIZE')]= "Household Size"
df_gst_for_json_read.at[2,'HH_SIZE'] = df_gst_for_json_read.at[3,'HH_SIZE']
df_gst_for_json_read.iloc[0,df_gst_for_json_read.columns.get_loc('URBAN')]= "int"
df_gst_for_json_read.iloc[1,df_gst_for_json_read.columns.get_loc('URBAN')]= "URBAN=1, RURAL=0"
df_gst_for_json_read.at[2,'URBAN'] = df_gst_for_json_read.at[3,'URBAN']
df_gst_for_json_read.iloc[0,df_gst_for_json_read.columns.get_loc('DISTRICT')]= "int"
df_gst_for_json_read.iloc[1,df_gst_for_json_read.columns.get_loc('DISTRICT')]= "District Code"
df_gst_for_json_read.at[2,'DISTRICT'] = df_gst_for_json_read.at[3,'DISTRICT']
df_gst_for_json_read.iloc[0,df_gst_for_json_read.columns.get_loc('STATE_CODE')]= "int"
df_gst_for_json_read.iloc[1,df_gst_for_json_read.columns.get_loc('STATE_CODE')]= "State Code"
df_gst_for_json_read.at[2,'STATE_CODE'] = df_gst_for_json_read.at[3,'STATE_CODE']
df_gst_for_json_read.iloc[0,df_gst_for_json_read.columns.get_loc('ASSESSMENT_YEAR')]= "int"
df_gst_for_json_read.iloc[1,df_gst_for_json_read.columns.get_loc('ASSESSMENT_YEAR')]= "Year of Consumption"
df_gst_for_json_read = df_gst_for_json_read[:-1]
df_gst_for_json_read['ind'] = "type"
df_gst_for_json_read.iloc[1, df_gst_for_json_read.columns.get_loc('ind')] = "desc"
df_gst_for_json_read.iloc[2, df_gst_for_json_read.columns.get_loc('ind')] = "form"
df_gst_for_json_read.set_index('ind', inplace=True)
# Create json ditionary for read variables
dict_gst_read = df_gst_for_json_read.to_dict()
df_gst_for_json_calc = df_gst_for_json_read
calc_cols = df_gst_for_json_calc.columns[df_gst_for_json_calc.columns.str.startswith('CONS_')]
df_gst_for_json_calc = df_gst_for_json_calc[calc_cols]
calc_cols = calc_cols.str.replace('CONS_', 'gst_').str.lower()
df_gst_for_json_calc.columns = calc_cols
form_calc_data = [{"2017": "Calculated"} for i in df_gst_for_json_calc.iloc[0,:]]
df = pd.DataFrame(columns = calc_cols)
df1 = pd.concat([df, pd.DataFrame([[np.nan] *
len(calc_cols)], columns=calc_cols)], ignore_index=True)
df1.iloc[0,:] = "float"
df1 = pd.concat([df1, pd.DataFrame([[np.nan] *
len(calc_cols)], columns=calc_cols)], ignore_index=True)
cols = df1.columns.str.upper()
df1.iloc[1,:] = cols.str.replace('GST_','GST paid by Household on consumption of ')
df1 = pd.concat([df1, pd.DataFrame([[np.nan] *
len(calc_cols)], columns=calc_cols)], ignore_index=True)
df1.iloc[2,:] = form_calc_data
df2 = pd.DataFrame({'ind':["type", "desc", "form"],
'total consumption':["float","Total Consumption by Household during the year in Rupees", {"2017": "Calculated"}],
'gst':["float","Potential GST paid by Household during the year in Rupees", {"2017": "Calculated"}]})
df_gst_for_json_calc = pd.concat([df1,df2], axis=1)
df_gst_for_json_calc.set_index('ind', inplace=True)
# Create json ditionary for calc variables
dict_gst_calc = df_gst_for_json_calc.to_dict()
# Merging the two dictionary along with adding "read" and "calc"
dict_gst_rec = {"read": dict_gst_read, "calc": dict_gst_calc}
# Pretty Print dictionary into json file
with open("gstrecords_variables.json", "w") as f:
json.dump(dict_gst_rec, f, indent=4, sort_keys=True)