Python - adding multiple tables into a single CSV with Panda

Question

I'm wondering how to get parsed tables from panda into a single CSV, I have managed to get each table into a separate CSV for each one, but would like them all on one CSV. This is my current code to get multiple CSVs:

import pandas as pd
import csv

url = "https://fasttrack.grv.org.au/RaceField/ViewRaces/228697009? 
raceId=318809897"

data = pd.read_html(url, attrs = {'class': 'ReportRaceDogFormDetails'} )

for i, datas in enumerate(data):

    datas.to_csv("new{}.csv".format(i), header = False, index = False)

Answer 1

I think need concat only, because data is list of DataFrames:

df = pd.concat(data, ignore_index=True)
df.to_csv(file, header=False, index=False)

Answer 2

You have 2 options:

1. You can tell pandas to append data while writing to the CSV file.

   data = pd.read_html(url, attrs = {'class': 'ReportRaceDogFormDetails'} )
   for datas in data:
       datas.to_csv("new.csv", header=False, index=False, mode='a')
   

2. Merge all the tables into one DataFrame and then write that into the CSV file.

   data = pd.read_html(url, attrs = {'class': 'ReportRaceDogFormDetails'} )
   df = pd.concat(data, ignore_index=True)
   df.to_csv("new.csv", header=False, index=False)
   

---

Edit

To still separate the dataframes on the csv file, we shall have to stick with option #1 but with a few additions

data = pd.read_html(url, attrs = {'class': 'ReportRaceDogFormDetails'} )
with open('new.csv', 'a') as csv_stream:
    for datas in data:
        datas.to_csv(csv_stream, header=False, index=False)
        csv_stream.write('\n')

Answer 3

all_dfs = []

for i, datas in enumerate(data):
    all_dfs.append(datas.to_csv("new{}.csv".format(i), header = False, index = False))

result = pd.concat(all_dfs)