pyedahelper 1.0.3

A beginner-friendly Python library that simplifies Exploratory Data Analysis (EDA) with AI-powered guide, and provides an interactive cheat-sheet for quick reference and tools for data visualization, cleaning and feature engineering.

🧠 pyedahelper - Simplify Your Exploratory Data Analysis (EDA)

pyedahelper is an educational and practical Python library designed to make Exploratory Data Analysis (EDA) simple, guided, and fast, especially for data analysts, students, and early-career data scientists who want to spend more time analyzing data and less time remembering syntax.

It's a lightweight, educational, and intelligent Python library that helps you perform Exploratory Data Analysis (EDA) faster — with guided suggestions, ready-to-use utilities, and clean visualizations.

🌟 Key Features:

• ⚡ A smart EDA cheat sheet (interactive and collapsible),
• 💬 AI-guided EDA assistant — suggests the next logical step (e.g., “View top rows with df.head()”).
• 🧩 A suite of data tools for real-world EDA tasks (loading, cleaning, feature engineering, visualization, and summaries),
• 💬 Handy code hints and examples you can copy directly into your notebook.

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🌍 Why pyedahelper?

Performing EDA often involves the use of numerous syntaxes to understand the dataset, it forces the narrative that good data professionals are those who know all the Python syntaxes by heart rather than those who can interprete accurately, the output of each of the EDA steps. And more importantly, Data Analysts spend more than 80% of their analytics time on iterative EDA, some of these hours spent checking documentary and Googling stuffs.

pyedahelper solves this by combining ready-to-use functions for your data workflow, AI-powered guide with inline learning — you can see, learn, and apply the same steps.

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⚙️ Installation

pip install pyedahelper==1.0.2

Upgrade

pip install --upgrade pyedahelper

🚀 Quick Start

import edahelper as eda
import pandas as pd

# Load your dataset
df = pd.read_csv("data.csv")

# 📚 Display the interactive EDA cheat-sheet
eda.show() -- for experienced analysts or
eda.core.show() -- for total newbies

# 🔍 Start guided suggestion
eda.next("read_csv")   # Suggests: "View first rows with df.head()"

# 💡 View an example command with short explanation
eda.core.example("describe")

From there, the assistant automatically continues:

df.head() → df.columns → df.shape → df.info() → df.describe() → ...

If you want to skip a suggestion, simply type "Next".

🔍 Modules Overview

1️⃣ EDA Guidance (AI Suggestion System)

The AI-powered step recommender helps complete beginners know what to do next.

Example flow:

eda.next("read_csv")   # Suggests df.head()
eda.next("head")       # Suggests df.columns
eda.next("columns")    # Suggests df.shape

It covers:

. Dataset overview (head, columns, shape, info, describe)

. Missing values (isnull, fillna, dropna)

. Data cleaning (duplicated, astype, replace)

. Visualization (plot_distribution, scatterplot, plot_correlation)

. Feature prep and modeling steps (label_encode, split, fit_model, predict)

5️⃣ Visualization Module

Functions for exploring and visualizing data quickly.

from edahelper import visualization as vis

vis.plot_correlation(df)
vis.plot_distribution(df, "Age")
vis.scatter(df, "Age", "Income", hue="Gender")

🎨 Uses matplotlib and seaborn under the hood for fast, clean plots.

📘 The Interactive Cheat-Sheet

When you forget a syntax, simply call:

eda.core.show()

✨ Displays a colorful grouped guide of:

Data Loading Overview Missing Values Indexing & Grouping Visualization Feature Engineering NumPy & sklearn tips

🧑🏽‍💻 Example Workflow

import pyedahelper as eda
import pandas as pd

# Load data
df = pd.read_csv("sales.csv")

# Start guided mode
eda.next("read_csv")    # Suggests df.head()
eda.next('head')        # Suggests df.info()

📦 Project Structure

pyedahelper/
│
├── __init__.py              # Main entrypoint
├── core.py                  # Cheat-sheet + examples
├── show.py                  # Display logic
├── stats_summary.py         # Dataset summary helpers
├── visualization.py         # Quick plots (hist, scatter, heatmap)
├── nextstep.py              # AI-guided EDA assistant (eda.next)
└── __init__.py              # Exports unified functions

🛠 Requirements

Python 3.8+ pandas numpy seaborn scikit-learn matplotlib rich (for colored terminal output)

🧾 License

MIT License © 2025 Chidiebere Christopher Feel free to fork, contribute, or use it in your analytics workflow!

🌟 Contributing

We welcome contributions — bug fixes, new EDA tools, or notebook examples.

1. Fork the repo
2. Create your feature branch (git checkout -b feature-name)
3. Commit your changes
4. Push and open a Pull Request 🎉

🔗 Links

📦 PyPI: https://pypi.org/project/pyedahelper/ 💻 GitHub: https://github.com/93Chidiebere/pyedahelper-Python-EDA-Helper ✉️ Author: Chidiebere V. Christopher

🚀 Learn. Explore. Analyze. Faster. pyedahelper — your friendly companion for every EDA project.