numpy-statistics

name	numpy-statistics
description	Standard and NaN-robust statistical functions for data analysis, histograms, and correlation matrices. Triggers: statistics, mean, nanmean, histogram, corrcoef, percentile, std.

Overview

NumPy provides a suite of statistical functions for summarizing data. Key capabilities include calculating central tendencies, dispersion, and relationships between variables, with specific handling for missing values (NaNs).

When to Use

• Summarizing experimental data (mean, median, standard deviation).
• Visualizing data distributions via histogram counts and binning.
• Identifying relationships between multiple variables using correlation matrices.
• Analyzing datasets with missing values where standard aggregations would fail.

Decision Tree

1. Does your data contain NaN?
   • Yes: Use nan prefixed functions (e.g., np.nanmean).
   • No: Use standard functions (e.g., np.mean).
2. Creating a histogram?
   • Need normalized area? Set density=True.
   • Fixed bin widths? Provide an integer for bins or an array for custom edges.
3. Checking correlation?
   • Use np.corrcoef. Note: output may require clipping if float errors occur.

Workflows

1. Robust Mean Calculation

   • Identify an array with potential missing values (NaNs).
   • Calculate the mean using np.nanmean(arr).
   • Optionally use np.nanstd(arr) to find the standard deviation of the valid subset.

2. Custom Histogram Creation

   • Define a set of non-uniform bin edges [0, 5, 10, 50, 100].
   • Pass the data and edges to np.histogram(data, bins=edges).
   • Retrieve the counts and the validated edges for plotting.

3. Inter-Variable Correlation Analysis

   • Stack multiple data variables into a 2D array (rows as variables).
   • Execute np.corrcoef(data).
   • Inspect the off-diagonal elements for Pearson correlation strengths.

Non-Obvious Insights

• NaN Sensitivity: Standard statistical functions return NaN if even one element is missing; the nan versions are essential for real-world messy data.
• Histogram Density: The density=True flag ensures the integral over the histogram is 1, not that the sum of the counts is 1 (unless bin widths are 1).
• Precision Clipping: Correlation coefficients can occasionally drift outside [-1, 1] due to floating-point rounding; NumPy automatically mitigates this in corrcoef results.

Evidence

• "nanmean... Compute the arithmetic mean along the specified axis, ignoring NaNs." Source
• "Note that the sum of the histogram values will not be equal to 1 unless bins of unity width are chosen; it is not a probability mass function." Source

Scripts

• scripts/numpy-statistics_tool.py: Computes robust statistics and custom histograms.
• scripts/numpy-statistics_tool.js: Basic mean calculator.

Dependencies

• numpy (Python)

References

• references/README.md