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import numpy as np | |
import pandas as pd | |
import matplotlib.pyplot as plt | |
import plotly.graph_objects as go | |
import plotly.express as px | |
pd.set_option('display.max_rows', None) | |
import datetime | |
from plotly.subplots import make_subplots | |
from scipy.optimize import curve_fit | |
import warnings | |
warnings.filterwarnings("ignore") | |
latest = pd.read_csv('https://api.covid19india.org/csv/latest/state_wise.csv') | |
state_wise_daily = pd.read_csv('https://api.covid19india.org/csv/latest/state_wise_daily.csv') | |
state_wise_daily = state_wise_daily.melt(id_vars=['Date', 'Status'], value_vars=state_wise_daily.columns[2:], var_name='State', value_name='Count') | |
state_wise_daily = state_wise_daily.pivot_table(index=['Date', 'State'], columns=['Status'], values='Count').reset_index() | |
state_codes = {code:state for code, state in zip(latest['State_code'], latest['State'])} | |
state_codes['DD'] = 'Daman and Diu' | |
state_wise_daily['State_Name'] = state_wise_daily['State'].map(state_codes) | |
state_wise_daily=state_wise_daily[state_wise_daily.State_Name!="Total"] | |
state_wise_daily['Date'] = pd.to_datetime(state_wise_daily['Date'], dayfirst=True) | |
state_wise_daily.sort_values('Date', ascending=True,inplace=True) |
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