compare_with_gospel.py

from datetime import datetime
import pandas as pd
import numpy as np
# Read merged data
df = pd.read_csv('https://api.covid19india.org/csv/latest/raw_data.csv')
df.head()

df['Date Announced'] = pd.to_datetime(df['Date Announced'])
df = df[df['Date Announced'] <= '2020-04-26']
df['District_Key'] = df['State code'] + "_" + df['Detected District']
df['Num cases'] = pd.to_numeric(df['Num cases'], errors='coerce')

dis_counts = pd.pivot_table(df,values = 'Num cases',
                           index = 'District_Key',
                           columns='Current Status',
                           aggfunc = np.sum).reset_index()

dis_counts.rename(columns={'Hospitalized':'Confirmed'},inplace=True)

# Read gospel
url = "https://raw.githubusercontent.com/covid19india/api/gh-pages/csv/latest/districts_26apr_gospel.csv"
gospel = pd.read_csv(url)

compare = pd.merge(gospel,dis_counts,on='District_Key', suffixes=("_gospel","_v1v2"))

compare.fillna(0,inplace=True)

compare['Conf_Diff'] = compare['Confirmed_gospel'] - compare['Confirmed_v1v2']
compare['Reco_Diff'] = compare['Recovered_gospel'] - compare['Recovered_v1v2']
compare['Dece_Diff'] = compare['Deceased_gospel'] - compare['Deceased_v1v2']

compare.to_csv("./compare_gospel_v1v2.csv",index=False)