csv-summary

name	csv-summary
description	Use when the user hands over a CSV or spreadsheet export and wants to know what is in it — column types, missing values, ranges, and the most common values.
tags	["data"]

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:

   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.