csv-summary

# Summarize a CSV Profiles a CSV so you can describe it before analysing it: per column, how many rows are filled, whether the values are numeric or text, and either min/max/mean or the most frequent values. Standard library only — nothing to install. ## Steps 1. Confirm the file path with the user if you are not certain of it. 2. Run the companion script: ```bash python /workspace/skills/csv-summary/scripts/summarize.py <path-to-csv> ``` Useful flags: `--delimiter ';'` for European exports, `--json` when you want to post-process the result, `--max-rows N` to profile only the first N rows of a very large file. 3. Report the findings in prose, not as a dump of the script output. Lead with what the file *is* (one row per what?), then call out anything that will matter for the next step: columns with missing values, a column that looks numeric but is stored as text, a categorical column with only a handful of distinct values. 4. If the script reports a column as `text` that the user expects to be numeric, say so explicitly — it usually means stray units, thousands separators, or a placeholder like `N/A`. ## Notes - The script never modifies the input file. - A column counts as numeric only when *every* non-empty value parses as a number, so a single `N/A` flips a column to text. That is deliberate: it surfaces dirty data instead of silently ignoring it.