I've written a parser to scrape data from Canadian Statistics Bureau.

import re
import pandas as pd
import requests
from bs4 import BeautifulSoup

def get_number_of_sources() -> int:
    Retrieves Number of STATCAN Sources
        Number of STATCAN Sources.
    URL = 'https://www150.statcan.gc.ca/n1/en/type/data'
    page = requests.get(URL)
    soup = BeautifulSoup(page.text, 'lxml')
    result = re.search(r'\((.*?)\)', soup.summary.get_text()).group(1)
    return int(result.replace(',', ''))

def main():
    Builds Resulting DataFrame and Dumps It To Excel File
    FILE_NAME = 'stat_can_all.xlsx'
    number_of_sources = get_number_of_sources()
    data_list = []
    for _ in range(1 + number_of_sources // 100):
        GENERIC_URL = 'https://www150.statcan.gc.ca/n1/en/type/data?count=100&p={}-All#all'
        page = requests.get(GENERIC_URL.format(_))
        print(f'Parsing Page {1+_:3} Out of {1+number_of_sources // 100}')
        soup = BeautifulSoup(page.text, 'lxml')
        details_soup = soup.find('details', id='all')
        items = details_soup.find_all('li', {'class': 'ndm-item'})
        for item in items:
            tag_description = item.find('div', class_='ndm-result-description')
            tag_former_id = item.find('div', class_='ndm-result-formerid')
            tag_frequency = item.find('div', class_='ndm-result-freq')
            tag_geo = item.find('div', class_='ndm-result-geo')

                    'title': item.find('div', class_='ndm-result-title').get_text(),
                    'product_id': item.find('div', class_='ndm-result-productid').get_text(),
                    'former_id': None if tag_former_id is None else tag_former_id.get_text(),
                    'geo': None if tag_geo is None else tag_geo.get_text(),
                    'frequency': None if tag_frequency is None else tag_frequency.get_text(),
                    'description': None if tag_description is None else tag_description.get_text(),
                    'release_date': item.find('span', class_='ndm-result-date').get_text(),
                    'type': item.find(
                    'ref': item.a.get('href'),

    data = pd.DataFrame.from_dict(data_list)
    data[['id', 'title_only']] = data.iloc[:, 0].str.split(
        pat='. ',
    data['id'] = pd.to_numeric(data['id'].str.replace(',', ''))
    data.fillna('None').to_excel(FILE_NAME, index=False)

if __name__ == '__main__':

Was wondering if there is a way to rephrase the following and alike lines of code representing ternary operator:

'former_id': None if tag_former_id is None else tag_former_id.get_text()

to have it more elegant and concise.

You can see that if tag_former_id is an instance of class bs4.element.Tag, one can use .get_text() method to retrieve str.

Otherwise, tag_former_id may be None and no further action is required.

Please could you review this piece and point at departures from the best practices?

Any other suggestions for improvements are also quite welcome, e.g. to bring more functional approach into the code etc.


1 Answer 1


For your particular question about the ternary operator, you can use and:

'former_id': tag_former_id and tag_former_id.get_text(),

I don't think it's super pretty/obvious, but it's more compact than the full ternary operator.

Instead of CONSTANT variables, e.g. FILE_NAME. I'd say to replace these with parameters with defaults:

def main(

Docstrings should use """ instead of '''. Also, while your format is very pretty, you'll have better luck (better IDE integration, parsing by Sphinx, etc.) if you use a standard format. The Google Style is a very popular format to use.

  • \$\begingroup\$ Thank you very much! This is exactly was I was looking for and many more to it. Processing your comments with thanks! Maybe shall leave this question open for the time being for further pitches, maybe some usage of dataclasses may be applicable here as well. \$\endgroup\$
    – alphamu
    Oct 6, 2022 at 7:38
  • 1
    \$\begingroup\$ Sure, you could use a dataclass for each row you're saving. I've recently been enjoying using pydantic instead of dataclasses... it's not built-in, but it's a very popular project and avoids a lot of dataclass headaches. Implementing validation to do your tag_former_id and tag_former_id.get_text() stuff automatically would be pretty straightforward that way (just attaching a validator to all the relevant fields), then converting back to a dict is a one-liner. \$\endgroup\$
    – scnerd
    Oct 6, 2022 at 17:06
  • \$\begingroup\$ Thank you very much for your pitches so far! Taking this thing with and operator alone already made the code look more pythonic. Absorbed the other things from your answer into commit, shall explore how to use more things from your below comment. Shall there be no other answers within a week, shall hit "Accept" button. Very grateful for the positive feedback \$\endgroup\$
    – alphamu
    Oct 6, 2022 at 17:24

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