covid_data_national.py

"""
This is the 'covid_data_national' module.

The covid_data_national is a helper data store which incorporates a cleaned,
filtered version of the Covid-19 data categorized by countries. First, it pulls
raw csv data from The World Health Organization and applies various forms of
data cleaning procedures such as conditional filtering and altering of rows as
well as changing data types of some columns to a more compatible data type. An
example of column refactoring would be changing the data type of Date_reported
field from string to Pandas datetime data type in order to enable the use of
advanced filtering and sorting functionality.
"""

import pandas as pd
from country_codes import codes
from country_names import names
from regions import regions
# Import World Health Organization's Covid 19 data into a DataFrame
df = pd.read_csv('https://covid19.who.int/WHO-COVID-19-global-data.csv',
                 low_memory=False)
# Convert the data type of the Date_reported column to datatype
df['Date_reported'] = pd.to_datetime(df['Date_reported'])
df = df[df['WHO_region'] != 'Other']
df = df[df['New_cases'] >= 0]
df = df[df['New_deaths'] >= 0]
df['WHO_region'] = df['WHO_region'].apply(
    lambda abbrevation: regions[abbrevation])
df = df.rename(columns={'WHO_region': 'Region'})
df['Country'].replace(names, inplace=True)
df.loc[df['Country'] == 'Namibia', 'Country_code'] = 'NA'
#covid_df['Country_code'].replace(iso_codes, inplace = True)
df['Country_code'] = df['Country_code'].apply(lambda code: codes[code])

# df = df[df['Date_reported'] == pd.Timestamp.today().normalize() - pd.Timedelta(days = 1)]
# print(df[df['Country'] == 'United States'])