Installation

v0.4.0

Get started with StatsLibX in minutes

Quick Install

Install StatsLibX from PyPI using pip:

bash

python

pip install statslibx

Verify the installation by importing the library and running the welcome function:

python

import statslibx
statslibx.welcome()

If you see a welcome message, StatsLibX is ready to use.

Requirements

StatsLibX requires the following dependencies:

Package	Minimum Version
Python	>= 3.9
pandas	>= 1.3
numpy	>= 1.21
scipy	>= 1.7
matplotlib	>= 3.4
seaborn	>= 0.11
plotly	>= 5.0
polars	>= 0.20
scikit-learn	>= 1.0 (optional extra)
statsmodels	>= 0.13 (optional extra)
viewx	>= 0.2.3 (optional extra)

Optional Dependencies

StatsLibX supports optional extras for specialized functionality:

viewx

Export results to HTML, PDF, or Presentation. Core analysis works without this extra.

all

viewx + statsmodels + scikit-learn + openpyxl + polars.

Install with extras:

bash

python

pip install statslibx[viewx]

bash

python

pip install statslibx[statsmodels,sklearn]

bash

python

pip install statslibx[all]

Development Install

If you want to contribute or try the latest unreleased version, clone the repository and install in editable mode:

bash

python

git clone https://github.com/GhostAnalyst30/StatsLibX.git
cd StatsLibX
pip install -e .

This installs StatsLibX in development mode, so changes to the source code take effect immediately.

Quick Start

Once installed, run a complete analysis in just a few lines:

python

from statslibx import DescriptiveStats
from statslibx.datasets import load_iris

data = load_iris()
ds = DescriptiveStats(data)
summary = ds.summary()
print(summary)
print(ds.mean('sepal_length'))

First Steps

Here is a typical workflow to get you started with StatsLibX:

Load Data

Start by loading data from built-in datasets with load_iris(), load_penguins(), or load your own CSV/Excel files using pandas.

Explore with DescriptiveStats

Pass your data to DescriptiveStats and call methods like summary(), correlation(), and outliers() to explore your dataset.

Test Hypotheses with InferentialStats

Use InferentialStats to run t-tests, ANOVA, chi-square tests, and compute confidence intervals on your data.

Visualize with UtilsStats

Leverage UtilsStats for data transformation, validation, and visualization helpers to present your findings.

Troubleshooting

Common issues you might encounter and their solutions:

Command not found: pip

Ensure Python and pip are installed and added to your PATH. Try using python -m pip install statslibx instead.

ImportError: No module named 'statslibx'

The package may not be installed correctly. Re-run pip install statslibx and verify with pip list.

Version conflicts with dependencies

Create a fresh virtual environment to avoid conflicts: python -m venv venv, then activate it and install statslibx.

Permission denied during installation

Use pip install --user statslibx to install for your user only, or use a virtual environment.

Installation hangs or times out

Try using a mirror: pip install statslibx -i https://pypi.org/simple. You can also increase the timeout with --timeout 120.