statilib 0.1.0

A simple statistics library

statlib

A small statistics library providing common descriptive and inferential statistics functions, grouped-data helpers, and basic probability utilities. The package appears authored by Abdulrahman F. Alosaimi.

Project layout

• statlib/formula.py - main implementation of statistical functions
• statlib/unit_tests.py - unit tests for several core functions

Usage

Import the functions you need from statlib:

from statlib import formula

print(formula.mean([1,2,3,4,5], 5))

Key functions (summary)

The formula.py module implements many functions. Highlights:

• Data description

  • mean(x, n) — sample mean
  • medine(data) — median (handles even/odd lengths)
  • mid_range(highest, lowest)
  • mid_point(h_limit, l_limit)
  • mod(data) — mode (returns list with mode and its frequency when applicable)

• Grouped data helpers

  • mean_for_grouped_data(x, f, n) — mean for grouped data using class midpoints
  • medine_for_grouped_data(f, cumulative_f) — median for grouped data (expects class rows [lower, upper, freq, cumulative_freq])

• Variation & dispersion

  • rnge(high, low) — range
  • pop_variance, standard_deviation_pop
  • sample_variance, standard_deviation_sample
  • variance_grouped
  • coff_of_variation_sample, coff_of_variation_pop

• Percentiles & z-scores

  • percent, relative_frequncy, coumulative_frequncy_percentile, percentile, percentile_to_value
  • z_scores_pop, z_scores_sample

• Outliers & quartiles

  • quar_of(data) — returns [Q1, Q2, Q3]
  • outlier(data) — returns [lower_limit, upper_limit, list_of_outliers]

• Correlation & regression

  • correlation_coefficient(data, n)
  • regression_line(data, n, x) — predicts y for given x using linear regression

• Probability & counting

  • prob, emprical_prob, event_comp, Bayes_prob, independ_intersect_prob, depend_intersect_prob
  • permu_count, comb_count

Contributing

Feel free to open issues or PRs. Small improvements I'd prioritize:

• Fix arithmetic bugs and replace =+ with += where needed
• Add more unit tests and type hints
• Standardize naming and export a cleaner public API