{palmerpenguins}

Python
package development
data science
datasets
A Python package for easily loading the Palmer Penguins dataset into your Python environment
Published

March 1, 2023

Overview

{palmerpenguins} is a Python package that provides easy access to the Palmer Penguins dataset, making it simple to load this popular dataset for data science education, exploration, and visualization in Python. This is the Python equivalent of the popular R package of the same name.

About Palmer Penguins

The Palmer Penguins dataset is a modern alternative to the classic Iris dataset. It contains size measurements for three penguin species observed on three islands in the Palmer Archipelago, Antarctica.

The dataset includes: - 344 penguins across 3 species - 7 variables including species, island, bill dimensions, flipper length, body mass, and sex - Real data collected by Dr. Kristen Gorman at Palmer Station, Antarctica

Why This Package?

While the Palmer Penguins dataset is available in R, Python users needed an easy way to access it. This package:

  • Simplifies Data Loading: One-line import of the dataset
  • Multiple Formats: Returns pandas DataFrames or raw data
  • Consistent API: Follows Python conventions and best practices
  • Well-Documented: Clear examples and use cases
  • Lightweight: Minimal dependencies

Key Features

For Data Scientists - Quick dataset access - Pandas integration - Perfect for teaching - Ideal for testing code

Package Features - Simple API - Type hints - Comprehensive tests - PyPI distribution

Technologies Used

  • Python: Core language
  • pandas: Data manipulation
  • pytest: Testing framework
  • setuptools: Package distribution
  • GitHub Actions: CI/CD

Installation

pip install palmerpenguins

Basic Usage

from palmerpenguins import load_penguins

# Load the penguins dataset
penguins = load_penguins()

# Start exploring
print(penguins.head())
print(penguins.describe())

Use Cases

Perfect for: - Teaching: Introduce data science concepts with real data - Learning: Practice visualization and analysis techniques - Testing: Quick dataset for prototyping code - Examples: Demonstrate statistical methods and visualizations

Example Analysis

import matplotlib.pyplot as plt
import seaborn as sns
from palmerpenguins import load_penguins

# Load data
penguins = load_penguins()

# Create visualization
sns.scatterplot(
    data=penguins,
    x="bill_length_mm",
    y="bill_depth_mm",
    hue="species"
)
plt.title("Palmer Penguins: Bill Dimensions")
plt.show()

Impact

This package: - Makes quality educational datasets more accessible to Python users - Supports the data science teaching community - Promotes the use of modern, ethically-sourced datasets - Contributes to the Python data science ecosystem

What I Learned

Developing this package taught me: - Python package development and distribution - Working with PyPI and package management - Writing effective documentation - The importance of good educational resources - How to contribute to the open-source data science community - Testing and continuous integration best practices

Credits

Dataset originally published by: - Dr. Kristen Gorman: Palmer Station, Antarctica LTER - Dr. Allison Horst: Artwork and R package

Open source project available on GitHub and PyPI