csv-workbench

Use the csv.DictReader from the Python standard library to iterate over rows as dictionaries. First preview the schema with head or a few rows, then compute aggregates like sums or counts in a short script.

Use collections.defaultdict to accumulate totals per key while iterating rows with csv.DictReader. Convert numeric strings with float() and print rounded results per group.

No, this workflow relies only on the Python standard library for portability. The csv and collections modules handle row counting, grouping, and summation without installing pandas.

The workflow states assumptions clearly when data is missing or malformed rather than failing silently. Inspect the schema first to catch issues like missing columns or non-numeric values before aggregating.

Stdlib csv processing is row-by-row and lacks built-in statistics, joins, or type inference. For large datasets or complex transformations, a dataframe library would be more appropriate.