Summarize and visualize data distributions
descriptive-statisticsskillsetup L1★64
Tibsfox/gsd-skill-creator ↗What it does
Summarize and visualize data through center, spread, shape, and position measures
Best for
Data exploration when hypothesis testing is premature and you need to understand raw data distribution first
Inputs
- · dataset (list or pandas Series)
- · visualization preference (histogram, boxplot, etc.)
Outputs
- · mean, median, mode, range, IQR, variance, SD, skewness, kurtosis values
- · visualization plots
Requires
- · numpy
- · scipy
- · matplotlib
- · pandas
Preconditions
Data is numerical (or categorical for mode); sample size sufficient for meaningful statistics
Failure modes
Outliers distort mean; skewed data makes mean/median/mode diverge; small sample variance estimates unreliable
Trust signals
- · Covers all standard summary statistics
- · Visualizations aid interpretation
- · Works with pandas DataFrames