Quickstart

Installation

StaTDS is compatible with Python>=3.8. We recommend installation on a Anaconda or Miniconda environment or a virtual env.

Installing using pip

You can install the library directly from pip.

For the latest version:

git clone https://github.com/kdis-lab/StaTDS
python setup.py bdist_wheel
pip install /path/to/wheelfile.whl

For the stable version:

• If you only want to use the statistical tests:

pip install statds[pdf]

• If you also want to generate PDFs:

pip install statds[pdf]

• If you want all the features:

pip install statds[full-app]

Example scripts

The github repository hosts example scripts and notebooks on how to use the library for different use cases, such as parametrics and non-parametrics test. Here you can see some examples:

Normality tests: Shapiro Test

from statds.normality import shapiro_wilk_normality
dataset = pd.read_csv("dataset.csv")
alpha = 0.05
columns = list(dataset.columns)
results = []

for i in range(1, len(columns)):
    results.append(shapiro_wilk_normality(dataset[columns[i]].to_numpy(), alpha))

statistic_list, p_value_list, cv_value_list, hypothesis_list = zip(*results)

results_test = pd.DataFrame({"Algorithm": columns[1:], "Statistic": statistic_list, "p-value": p_value_list, "Results": hypothesis_list})
print(results_test)

Homoscedasticy tests: Levene

from statds.homoscedasticity import levene_test
dataset = pd.read_csv("dataset.csv")
alpha = 0.05
columns = list(dataset.columns)
statistic, p_value, rejected_value, hypothesis = levene_test(dataset, alpha, center='mean')
print(hypothesis)
print(f"Statistic {statistic}, Rejected Value {rejected_value}")

Parametrics tests: T-test

from statds.parametrics import t_test_paired
dataset = pd.read_csv("dataset.csv")
alpha = 0.05
columns = list(dataset.columns)
selected_columns = [columns[1], columns[2]]
statistic, rejected_value, p_value, hypothesis = t_test_paired(dataset[selected_columns], alpha)
print(hypothesis)
print(f"Statistic {statistic}, Rejected Value {rejected_value}, p-value {p_value}")

Parametrics tests: ANOVA

from statds.parametrics import anova_test
dataset = pd.read_csv("dataset.csv")
alpha = 0.05
columns = list(dataset.columns)
statistic, p_value, rejected_value, hypothesis = anova_test(dataset, alpha)
print(hypothesis)
print(f"Statistic {statistic}, Rejected Value {rejected_value}, p-value {p_value}")

Non-parametrics tests: Wilcoxon

from statds.no_parametrics import wilconxon
dataset = pd.read_csv("dataset.csv")
alpha = 0.05
columns = list(dataset.columns)
selected_columns = [columns[1], columns[2]]
statistic, p_value, rejected_value, hypothesis = wilconxon(dataset[selected_columns], alpha)
print(hypothesis)
print(f"Statistic {statistic}, Rejected Value {rejected_value}, p-value {p_value}")

Non-parametrics tests: Friedman Test

import pandas as pd
from statds.no_parametrics import friedman

dataset = pd.read_csv("dataset.csv")
alpha = 0.05
columns = list(dataset.columns)
rankings, statistic, p_value, critical_value, hypothesis = friedman(dataset, alpha, criterion=False)
print(hypothesis)
print(ff"Statistic {statistic}, Rejected Value {rejected_value}, p-value {p_value}")
print(rankings)

Post-hoc tests: Bonferroni

from statds.no_parametrics import friedman, bonferroni
dataset = pd.read_csv("dataset.csv")
alpha = 0.05
columns = list(dataset.columns)
rankings, statistic, p_value, critical_value, hypothesis = friedman(dataset, alpha, criterion=False)
print(hypothesis)
print(f"Statistic {statistic}, Rejected Value {rejected_value}, p-value {p_value}")
print(rankings)
num_cases = dataset.shape[0]
results, figure = bonferroni(rankings, num_cases, alpha, control = None, type_rank = "Friedman")
print(results)
figure.show()

Post-hoc tests: Nemenyi

from statds.no_parametrics import friedman, nemenyi
dataset = pd.read_csv("dataset.csv")
alpha = 0.05
columns = list(dataset.columns)
rankings, statistic, p_value, critical_value, hypothesis = friedman(dataset, alpha, criterion=False)
print(hypothesis)
print(f"Statistic {statistic}, Rejected Value {rejected_value}, p-value {p_value}")
print(rankings)
num_cases = dataset.shape[0]
ranks_values, critical_distance_nemenyi, figure = nemenyi(rankings, num_cases, alpha)
print(ranks_values)
print(critical_distance_nemenyi)
figure.show()

Citing

If you use StaTDS for your research, please consider citing the library

Bibtex entry:

@InProceedings{statds,
  author={Christian Luna Escudero, Antonio Rafael Moya Martín-Castaño, José María Luna Ariza, Sebastián Ventura Soto},
  title={{StaTDS}: Statistical Tests for Data Science (name article and journal)},
  booktitle={journal},
  year={2023}
}