This content originally appeared on DEV Community and was authored by Pratiksha Rawat

Introducing birddata: A Simple and Fun Bird Species Dataset for Python

Hey devs and data enthusiasts!

I’m excited to share my new Python dataset package called birddata, inspired by the classic load_iris dataset but focused on birds! Whether you’re learning data science, practicing machine learning, or just love birds, this dataset can be a fun way to explore and experiment.

What is birddata?

birddata is a lightweight Python package that provides a curated dataset of bird species features, ideal for classification and clustering tasks. It includes:

Several bird species with numerical features (like wing span, beak length, weight)

Ready-to-use pandas DataFrame format

Clean, simple API similar to sklearn’s load_iris

Why create birddata?

While the Iris dataset is a classic introduction to ML datasets, I wanted something a bit different — something relatable and beginners alike. birddata helps you:

Practice data analysis and ML modeling on a new dataset

Understand dataset structure and packaging by looking under the hood

Explore species classification with real-world inspired data

How to use birddata

First, install the package via pip (coming soon / or link if published):

pip install birddata

Then, loading the dataset is as simple as:

from birddata import load_birddata

data = load_birddata()
X = data.data # features
y = data.target # labels
df = data.frame # pandas DataFrame with data and labels

print(df.head())

From here, you can train classifiers, visualize data, or use it as a teaching tool!

Why Should You Use birddata?

1. Beginner-Friendly Dataset
   birddata is simple and clean, making it perfect for beginners who want to learn data analysis, preprocessing, and machine learning without getting overwhelmed.

2. Realistic Biological Features
   Unlike some synthetic datasets, birddata uses real-inspired features (like wing span, beak length), giving you practical insights into how biological data can be modeled.

3. Great for Practice and Learning
   Whether you’re practicing classification, clustering, or visualization, birddata offers a fresh alternative to the overused Iris dataset.

4. Easy to Use and Integrate
   Designed with a familiar API (similar to sklearn datasets), it’s quick to load and start experimenting with, reducing setup time.

5. Compact and Lightweight
   The dataset is small but meaningful — ideal for quick prototyping, demos, and educational projects without heavy computational cost.

6. Ideal for Teaching and Demonstrations
   If you’re an instructor or content creator, birddata can serve as a new example dataset to engage learners in biology and ML.

7. Open Source and Extendable

   You can freely explore the code, suggest improvements, or add more species/features to customize it for your projects.

   What’s next?

I plan to add more bird species, richer features, and maybe even image data. Suggestions and contributions are very welcome!—

If you want to try out birddata, give it a star and share your projects with it on Twitter or dev.to — tag me @pratiksha_rwt !

python, #machinelearning, #dataset, #opensource)

This content originally appeared on DEV Community and was authored by Pratiksha Rawat