Collection of lesser-known statistical functions
Project description
obscure_stats
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Highlights:
obscure_stats
is a small Python package that includes a lot of useful but lesser-known statistical functions and builds on top of numpy
and scipy
.
Current API list
- Collection of measures of central tendency -
obscure_stats/central_tendency
:- Contraharmonic Mean;
- Half-Sample Mode;
- Hodges-Lehmann-Sen Location;
- Midhinge;
- Midmean;
- Midrange;
- Standard Trimmed Harrell-Davis Quantile;
- Trimean.
- Collection of measures of dispersion -
obscure_stats/dispersion
:- Coefficient of Variation;
- Dispersion Ratio;
- Linear Coefficient of Variation;
- Lloyds Index;
- Morisita Index;
- Quartile Coefficient of Dispersion;
- Robust Coefficient of Variation;
- Standard Quantile Absolute Deviation;
- Studentized Range.
- Collection of measures of skewness -
obscure_stats/skewness
:- Area Under the Skewness Curve (weighted and unweighted);
- Bickel Mode Skewness Coefficient;
- Bowley Skewness Coefficient;
- Forhad-Shorna Rank Skewness Coefficient;
- Groeneveld Skewness Coefficient;
- Hossain-Adnan Skewness Coefficient;
- Kelly Skewness Coefficient;
- Medeen Skewness Coefficient;
- Pearson Median Skewness Coefficient;
- Pearson Mode Skewness Coefficient.
- Collection of measures of kurtosis -
obscure_stats/kurtosis
:- Crow-Siddiqui Kurtosis;
- Hogg Kurtosis;
- Moors Kurtosis;
- Moors Octile Kurtosis;
- Reza-Ma Kurtosis.
- Collection of measures of association -
obscure_stats/association
:- Chatterjee Xi correlation Coefficient (original and symmetric versions);
- Concordance Correlation Coefficient;
- Concordance Rate;
- Tanimoto Similarity;
- Zhang I Correlation Coefficient.
- Collection of measures of qualitative variation -
obscure_stats/variation
:- AVDev;
- B Index;
- Extropy;
- Gibbs M1;
- Gibbs M2;
- ModVR;
- RanVR.
Installation
pip install obscure_stats
Usage Example
>>> from obscure_stats.central_tendency import standard_trimmed_harrell_davis_quantile
>>> from obscure_stats.dispersion import standard_quantile_absolute_deviation
>>> data = [1.83, 1.01, 100.12, 1.20, 0.99, 0.87, 1.13, 100.01, 0.75, 1.03]
>>> central_tendency = standard_trimmed_harrell_davis_quantile(data)
>>> dispersion = standard_quantile_absolute_deviation(data)
>>> print(f"Robust measure of central tendency = {central_tendency:.2f}, Robust measure of dispersion = {dispersion:.2f}")
Out[1]:
Robust measure of central tendency = 1.09, Robust measure of dispersion = 0.42
License
The content of this repository is licensed under a MIT license.
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