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stapy

Python package containing functions implemented for descriptive and inferential statistics.

Presently, this package is a work in progress. I will add to this package as I further my study in statistics.

As of now, I have more-or-less covered all topics on Descriptive Statistics, though room for improvement is ever-existent. I have finally touched upon Inferential Statistics, and currently am working to finish it soon.

Modules in this package

descriptive.py

Contains functions related to Descriptive Statistics, comprising:

  • visualizing categorical data into a table
  • one function each for mean, median and mode
  • one function for all central tendencies
  • population variance
  • population standard deviation
  • one function of sample variance and sample SD
  • range, quartiles, IQR of a distribution
  • outlier checking and elimination in a distribution
  • boxplot summary printing
  • Z-scores of a distribution
  • proportion using z-table
  • randomly selecting all the samples from a population
  • sampling distrubution of a population
  • standard error of a population
  • sample z-score.

In order to use this, you may import the entire module:

from stapy import descriptive

Or, you may include a particular function:

from stapy.descriptive import get_Z_scores

And use them as you like:

>>> sample = [205,137,20,90]
>>> get_Z_scores(sample, mean=120, SD=40)
[2.125, 0.425, -2.5, -0.75]

More on the use of this module is provided in the testsD.py file.

inferential.py

Contains functions related to Inferential Statistics, comprising:

  • approximate margin of error
  • confidence intervals
  • hypothses testing
  • t-statistics
  • t-critical value
  • Cohen's d
  • margin of error
  • t-tests
  • r2 effective size measure
  • dependent samples
  • independent samples
  • analysis of variance (ANOVA)
  • correlation
  • regression
  • chi-squared tests

Usage

Import entire module

from inferential import *

Or, a function in the module

from inferential import get_confidence_interval

And use by calling the functions.

>>> xbar, sigma, n, z_star = 40, 16.04, 35, 1.96
>>> get_confidence_interval(xbar, sigma, n, z_star)
(34.6859404956286, 45.3140595043714)

More on the use of this module is provided in the testI.py file.

License

MIT License