How to Clean Scraped Job Data with Python for Analysis

How to Clean Scraped Job Data with Python for Analysis You've scraped Amazon's careers page and now have a mess of duplicate entries, broken HTML, and inconsistent date formats. The job listings are scattered across multiple rows, descriptions are full of <br> tags and strange line breaks, and some dates are in MM/DD/YYYY while others are DD-MM-YYYY . You need clean data for analysis, but the raw scrape is unusable as-is. The Manual Way (And Why It Breaks) Most developers try to clean this by hand — copying and pasting into spreadsheets, deleting rows manually, or writing quick scripts in Excel or Notepad++. This is slow, error-prone, and time-consuming. When scraping at scale, you quickly hit API rate limits or get blocked, so you end up with a massive file and no real way to automate the cleanup. You might spend hours cleaning data that could’ve been done in minutes with a tool. The Python Approach Here’s a simplified version of what a developer might write to clean a few rows of job