scraping data from wikipedia table

Question

I\'m just trying to scrape data from a wikipedia table into a panda dataframe.

I need to reproduce the three columns: \"Postcode, Borough, Neighbourhood\".

Answer 1

Basedig provides a platform to download Wikipedia tables as Excel, CSV or JSON files directly. Here is a link to the Wikipedia source: https://www.basedig.com/wikipedia/

If you do not find the dataset you are looking for on Basedig, send them the link to your article and they'll parse it for you. Hope this helps

Answer 2

You may be overthinking the problem, if you only want the script to pull one table from the page. One import, one line, no loops:

import pandas as pd
url='https://en.wikipedia.org/wiki/List_of_postal_codes_of_Canada:_M'

df=pd.read_html(url, header=0)[0]

df.head()

    Postcode    Borough         Neighbourhood
0   M1A         Not assigned    Not assigned
1   M2A         Not assigned    Not assigned
2   M3A         North York      Parkwoods
3   M4A         North York      Victoria Village
4   M5A         Downtown Toronto    Harbourfront

Answer 3

You need to iterate over each row in the table and store the data row by row, not just in one giant list. Try something like this:

import pandas
import requests
from bs4 import BeautifulSoup
website_text = requests.get('https://en.wikipedia.org/wiki/List_of_postal_codes_of_Canada:_M').text
soup = BeautifulSoup(website_text,'xml')

table = soup.find('table',{'class':'wikitable sortable'})
table_rows = table.find_all('tr')

data = []
for row in table_rows:
    data.append([t.text.strip() for t in row.find_all('td')])

df = pandas.DataFrame(data, columns=['PostalCode', 'Borough', 'Neighbourhood'])
df = df[~df['PostalCode'].isnull()]  # to filter out bad rows

then

>>> df.head()

  PostalCode           Borough     Neighbourhood
1        M1A      Not assigned      Not assigned
2        M2A      Not assigned      Not assigned
3        M3A        North York         Parkwoods
4        M4A        North York  Victoria Village
5        M5A  Downtown Toronto      Harbourfront