example_stats

You can compute descriptive statistics directly on plain Python lists to get mean, standard deviation, median, and quantiles without requiring numpy or pandas as dependencies. This approach provides fast access to basic summary statistics for data analysis tasks.

Yes, you can perform z-score normalization on a plain Python number list to standardize your data values. This built-in functionality computes the standardized scores directly without needing external libraries like numpy or pandas for your data analysis workflow.

The best way to compute the Pearson correlation coefficient is by using a lightweight Skill designed for plain Python lists. It calculates the correlation directly between two datasets without requiring heavy external dependencies like numpy or pandas.

Yes, this statistics tool works entirely without installing pandas or numpy. It is specifically designed to handle summary statistics, quantiles, and z-score normalization on plain Python lists, making it ideal for environments with restricted dependencies.

You compute arbitrary quantiles for a dataset by passing a plain Python number list and a value between zero and one to the quantile function. It calculates any specific quantile directly without relying on numpy or pandas libraries.

You should avoid numpy for basic data analysis when you only need summary statistics, quantiles, or Pearson correlation on simple number lists. Using a pure Python approach eliminates unnecessary dependencies while still providing fast access to core descriptive statistics.