Basic Web Scraping with Python Tutorial

The listing is provided with similar layout cards with 10 cards per page, and 44 pages.

We can then repeat this process for all the pages. Conveniently, the pagination just changes the number at the end of the URL, so we can loop our scraper over the number of pages. Let’s put a slight delay in before moving to the next page.

Now that we have the two raw lists of names and addresses, we need to do a little cleanup. The names list seems to be fine, but the each address has a bunch of blank spaces, new lines, missing data fields, and other noise. Let’s go through the list and split each address into Street, City, State, and Zip.

First we clean up the address string, getting rid of the blank spaces in the beginning and splitting by new lines:

The first line is the Street:

The second line before the comma is the City (and eliminating the blank spaces before it):

States are two capital letters, located after the comma on the second line. We can search there using a regular expression, and return nothing if two capital letters aren’t found.

Zipcodes are five digits, also located after the comma on the second line. We can search there using another regular expression, and return nothing if five digits aren’t found.

Now that we have all our cleaned data for this one hospital, let’s output it to a CSV. We can write in the headers first:

And as we’re looping through each address cleaning it up, we can output that to the next row:

And we’re done! The final code:

Happy scraping!

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