Web Scraping by BS4

Question

I am working on the following codes to scrape a dynamic content website. I have been helped to improve my code to scrape one page of the content. Now, I would like to add a FOR loop to scrape multiple pages and add the related name as a new column to distinguish each page.

Id	Name
HK_2019_D105	Name1
HK_2018_C509	Name2

The output is combined all pages into one dataframe. Please suggest how to improve the following code.

import pandas as pd
import requests
Id = df['Id']

cookies = {
    'BotMitigationCookie_9518109003995423458': '343775001600940465b2KTzJpwY5pXpiVNIRRi97Z3ELk='
}

for j in Id:
    def main(url):
        r = requests.post(url, cookies=cookies)
        df = pd.read_html(r.content, header=0, attrs={'class':'table_bd f_tal f_fs13'})
        new = pd.concat(df, ignore_index=True)
        data = pd.DataFrame(new, columns=['Date','Type','Racecourse/Track','Workouts','Gear'])
        data.to_csv('data'+str(j)+'.csv')
    
    main('https://racing.hkjc.com/racing/information/English/Trackwork/TrackworkResult.aspx?HorseId='+str(j)+'')

Output:

header1	header2	Name
First	row	Name1
Second	row	Name1
First	row	Name2
Second	row	Name2

Answer 1

Assuming you are trying to combine each request into a single CSV file, you can use the .append() function. The Id and Name columns can be added before doing the append:

import pandas as pd
import requests

df_ids = pd.read_csv('ids.csv')
cookies = {'BotMitigationCookie_9518109003995423458': '343775001600940465b2KTzJpwY5pXpiVNIRRi97Z3ELk='}
df_output = pd.DataFrame(columns=['Date', 'Type', 'Racecourse/Track', 'Workouts', 'Gear', 'Id', 'Name'])

for id, name in df_ids.itertuples(index=False):
    print(f'Getting: {id} for {name}')
    
    url = f'https://racing.hkjc.com/racing/information/English/Trackwork/TrackworkResult.aspx?HorseId={id}'
    r = requests.post(url, cookies=cookies)
    df = pd.read_html(r.content, header=0, attrs={'class':'table_bd f_tal f_fs13'})[0]
    df['Id'] = id
    df["Name"] = name
    
    df_output = df_output.append(df, ignore_index=True)

df_output.to_csv('output.csv', index=False)

Giving you an output.csv file starting:

Date,Type,Racecourse/Track,Workouts,Gear,Id,Name
10/02/2021,Trotting,Sha Tin SmT,SmT 1 Round - Fast (R.B.),H,HK_2019_D105,Name1
09/02/2021,Swimming,Sha Tin,,,HK_2019_D105,Name1
09/02/2021,Trotting,Sha Tin SmT,SmT 1 Round - Fast (R.B.),H,HK_2019_D105,Name1
08/02/2021,Swimming,Sha Tin,,,HK_2019_D105,Name1