Optimizing Web Scraping: How to Fix Common CSV File Appending Issues

When building web scrapers that save data to CSV files, even small mistakes can lead to significant problems. A recent analysis of a web scraping script revealed several inefficiencies that prevented proper data collection across multiple pages.

The primary issue occurred when opening the CSV file during each iteration of the scraping loop. Using the ‘write’ method instead of ‘append’ caused the script to overwrite previous data rather than adding to it.

The Simple Fix: Changing File Mode

The first improvement was straightforward: changing the file mode from ‘write’ to ‘append’:

By using the append mode (‘a’ instead of ‘w’), the script could now collect quotes from all pages without overwriting previous data. This allowed extending the number of pages to scrape from 5 to any desired value.

Further Optimizations

While the append fix resolved the immediate issue, several other improvements were implemented:

1. Moving the base URL definition outside the function for better organization
2. Restructuring the nested loops for efficiency
3. Improving URL formatting with the format() method
4. Converting lists of tags to comma-separated strings using the join() method

Avoiding Duplications

An important observation was made: when using ‘append’ mode while opening the file outside of the loop, each run of the script would add duplicate data to the existing file. The solution was to revert to ‘write’ mode since the file was now opened before any iterations began.

This change prevented duplication while still capturing all the quotes from every page in a single execution.

Range Function Adjustment

Another subtle fix involved the range function. Since Python’s range doesn’t include the upper bound, adding +1 to the final page number (range(1, pages+1)) ensured all desired pages were scraped.

Final Result

After implementing these optimizations, the script successfully collected quotes from all ten pages without any duplication. The data was properly formatted and saved to a clean CSV file, ready for analysis.

These small but critical adjustments demonstrate how attention to file handling details can significantly improve web scraping efficiency and data quality.