scrapping_pdTable.py

def scrap_coronaData():
    try:
        file_name='nCOV'+datetime.now().strftime("%d-%m-%Y")+'.csv'
#         corona_data=pd.read_html('https://www.worldometers.info/coronavirus/#countries')[0]#,header=None)[0]
        header = {
              "User-Agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.75 Safari/537.36",
              "X-Requested-With": "XMLHttpRequest"
             }

        r = requests.get('https://www.worldometers.info/coronavirus/#countries', headers=header, timeout=10)
#         soup = bs(r.text, 'html.parser')
#         return soup
        
        '''We also can directly read the data from the get request and parse using Pandas table'''
        covid_data=pd.read_html(r.text)[0]
         #print(covid_data)
        covid_data.fillna(0,inplace=True)
        covid_data.to_csv(file_name, sep=',', index=False)
        return covid_data #.sort_values('Country,Other')
    except Exception as exc:
        print(f'{exc}')
        
corona_data=scrap_coronaData()
    
def search_country(country=None,df=corona_data):


    if country == None:
        print(" Updating for all")
        df=scrap_coronaData()
        return df
    
    else:
        
        country=' '.join([i.capitalize() for i in country.split()])

        # if any(str(elem) in ['Iran'] for elem in df['Country,Other'].tolist()):
        #     print('yes present')
        # else:
        #     print("The Country is not Availble")

        if country in df['Country,Other'].tolist():
            covid_outbreak=df.loc[df['Country,Other']==country]
            return covid_outbreak

        else:
            print("Nope The given Country information is not Avaialable")
            return f'The given Country information is not Avaialable'

    
#     return data