csv-data-summarizer

Load the file with pandas read_csv, then use describe() for numeric statistics, isnull().sum() for missing values, and value_counts() for categorical columns. This Skill wraps those steps in a single summarize_csv function that also generates charts.

The analysis generates correlation heatmaps when multiple numeric columns exist, time-series plots when date or time columns are detected, distribution histograms for numeric columns, and bar charts for categorical columns.

Yes. The analysis counts total missing values, reports the percentage of missing data overall, and breaks down missing counts per column. Datasets with no missing values are reported as complete.

It scans column names for 'date' or 'time' and converts the first match with pandas to_datetime. If a valid date column exists, it reports the date range and plots numeric metrics grouped by date.

The script requires Python 3.8 or later with pandas 2.0+, matplotlib 3.7+, and seaborn 0.12+. These are listed in requirements.txt and can be installed with pip.

Date detection relies on column names containing 'date' or 'time', so unusually named date columns may be missed. Categorical analysis is limited to the first five non-ID columns, and charts cover at most the first few numeric or categorical fields.