Analytics-EDA 0.1.18

A lightweight Python library that streamlines EDA with fast, automated analysis to uncover data issues, validate assumptions, and support modeling decisions.

analytics-eda

Exploratory Data Analysis (EDA) is critical but time-consuming and often inconsistent across projects.

analytics-eda is a lightweight Python library that streamlines EDA with fast, automated analysis to uncover data issues, validate assumptions, and support modeling decisions.

Table of Contents

• Installation
• Quickstart
• Features
• API Reference
• Contributing Guidelines
• License

Installation

Requires: Python 3.11 or later

pip install analytics-eda

Quickstart

Here's how to generate a univariate numeric EDA report from a pandas series:

import json
import numpy as np
import pandas as pd

from analytics_eda.univariate.numeric.univariate_numeric_analysis import univariate_numeric_analysis

# Series to analyze
rng = np.random.default_rng(0)
data = rng.normal(loc=0.0, scale=1.0, size=15)
normal_15_series = pd.Series(data, name="normal_series")

# Analyze numeric series
report_file_path = univariate_numeric_analysis(
    normal_15_series
)

# Access json report
result = json.loads(report_file_path.read_text())
print(json.dumps(result, indent=2))

Features

Univariate Exploratory Data Analysis

Univariate EDA analyzes a single variable at a time to understand its distribution, detect anomalies, and assess data quality.
This step is essential for uncovering data issues before modeling and making informed preprocessing decisions.

Supports:

• Numeric data:

  • Identifies missing data
  • Computes descriptive statistics (mean, median, std, skew, etc.)
  • Evaluates normality with tests and visualizations
  • Detects outliers using IQR, Z-score, and robust Z-score
  • Runs inferential tests (e.g. goodness-of-fit, variance checks)

• Categorical data:

  • Summarizes frequency distributions
  • Flags rare or dominant categories
  • Assesses cardinality and missing values

---

Bivariate Exploratory Data Analysis

Bivariate EDA compares two variables to assess relationships and patterns across groups.
It helps detect important group-level differences and guides feature selection or encoding strategies.

Supports:

• Numeric vs. Categorical:
  • Performs statistical tests (e.g. homogeneity of variance, distribution overlap)
  • Runs global hypothesis tests to compare group distributions
  • Estimates effect size between segments
  • Analyzes numeric patterns within categorical groups

API Reference

See our API Reference.md for details.

Contributing Guidelines

We welcome contributions! Please see CONTRIBUTING.md for details.

License

This project is open source under the Apache License 2.0. See LICENSE for details.