Use small Python scripts to handle repetitive file tasks.
Step by step, with examples
Scan subfolders and record file paths, extensions, sizes, and modification times in a table. Start with 4 small example files while keeping the originals and existing results intact.
Merge CSV files with the same column structure in order and add a source_file column. Use small datasets to check item names containing commas, missing columns, and existing output.
Compare old and new names in a table and check for conflicts. Only an explicit --apply creates copies in a new folder, keeping the originals.
Convert the data to a table while distinguishing missing keys, null values, and empty strings, and create a separate issue report.
Check the result of a file copy by comparing hashes of the file bytes rather than relying only on names or sizes.
Split a CSV by data-record count, repeat its header in every output file, and leave the original unchanged. Use a seven-record synthetic example to check the boundaries and verify that all records survive in their original order.
Filter files by size, compare SHA-256 digests, and write a review-only CSV without changing the source files. Use a seven-file synthetic example to check which paths belong in the report and which do not.
Resize multiple images into a new folder without stretching them or replacing the source files. A small synthetic image set makes the expected output dimensions easy to calculate by hand.
Create a dated ZIP archive from a folder without changing the originals, then verify that every source file appears in the archive with matching content. A small synthetic folder makes the expected paths, sizes, and totals easy to check by hand.
Try a short search term such as CSV or AI.
All examples are for practice. Automotive data is synthetic and used for illustration.