Exploration of COVID19 dataset: Time-Series daily cases plot

import pandas as pd
import matplotlib.pyplot as plt
ASEAN_countries_list = ['Brunei', 'Cambodia', 'Indonesia', 'Laos', 'Malaysia', 'Burma', 'Philippines', 'Singapore', 'Vietnam']

link = 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv'
covid19_data = pd.read_csv(link)
asean_df = covid19_data[covid19_data['Country/Region'].isin(ASEAN_countries_list)]
asean_df.set_index('Country/Region', inplace = True)
asean_df_dropped = asean_df.drop(columns = ['Province/State', 'Lat', 'Long'])
asean_t = asean_df_dropped.T
asean_t

import numpy as np

#Make a copy so we don't actually change the previous dataframe
sg_s = asean_t['Singapore'].copy()

#Use this to shift the entire column down by 1
sg_s_F = sg_s.shift(1)

#Merge the two columns by using the same dates
sg_df = pd.concat([sg_s, sg_s_F], axis=1)

#Rename the columns for easier reference later
sg_df.columns = ['Cases_O', 'Cases_ShiftF']

#Creating a new column
sg_df['difference'] = sg_df['Cases_O'] - sg_df['Cases_ShiftF']
sg_df

def country_diff(df, country):

    #Make a copy so we don't actually change the previous dataframe
    df_s = df[country].copy()

    #Use this to shift the entire column down by 1
    df_s_F = df_s.shift(1)

    #Merge the two columns by using the same dates
    country_df = pd.concat([df_s, df_s_F], axis=1)

    #Rename the columns for easier reference later
    country_df.columns = [f'{country}_Cases_O', f'{country}_Cases_ShiftF']

    #Creating a new column
    country_df[f'{country}_difference'] = country_df[f'{country}_Cases_O'] - country_df[f'{country}_Cases_ShiftF']
    return country_df

#Test the function
#brunei_df = country_diff(asean_t, 'Brunei')

#Create a new dataframe
asean_daily_df = pd.DataFrame()

#Run a for loop across all countries
#Reminder:
#ASEAN_countries_list = ['Brunei', 'Cambodia', 'Indonesia', 'Laos', 
#                        'Malaysia', 'Burma', 'Philippines', 'Singapore', 'Vietnam']

for country in ASEAN_countries_list:
    #Run the function and storing it into a temporary dataframe
    #per country
    temp_df = country_diff(asean_t, country)
    
    #Attach this temporary df to the actual dataframe
    #While selecting for the "Difference" column only!
    asean_daily_df = pd.concat([asean_daily_df, temp_df[f'{country}_difference']],sort = False, axis = 1)

    
#Drop the very first row because it makes no sense to have a change between 0th day and -1th day
asean_daily_df = asean_daily_df.iloc[1:,]
asean_daily_df

# Yes thats it...
asean_daily_diff = asean_t.diff().iloc[1:,]
asean_daily_diff

fig, ax = plt.subplots(figsize = (10,10))

#Iterate through our countries so we can plot automatically plot them!
for country in ASEAN_countries_list:
    asean_daily_df[f'{country}_difference'].plot(ax=ax, label = country)

ax.set_ylabel('Confirmed Daily Cases')
ax.set_xlabel('Dates')
ax.legend()
plt.show()