Python, a versatile language, is widely used in the field of data science due to its simplicity and vast library ecosystem. While packages like NumPy, Pandas, and Matplotlib are well-known, there are several lesser-known but equally powerful packages.
Here are ten such Python packages that every data scientist should explore:
1. CleanLab
CleanLab is a Python package that helps clean data and labels by automatically detecting issues in a machine learning dataset. If you haven’t started using CleanLab yet, you’re missing out on a lot!
2. LazyPredict
This Python library enables you to train, test, and evaluate multiple machine learning models at once using just a few lines of code. It supports both regression and classification tasks.
3. Lux
Lux is a Python library for quickly visualizing and analyzing data. It provides an easy and efficient way to explore data.
4. PyForest
PyForest is a time-saving tool that helps in importing all the necessary data science libraries and functions with a single line of code.
5. PivotTableJS
PivotTableJS lets you interactively analyze your data in Jupyter Notebooks without any code. It’s a powerful tool for data exploration.
6. Drawdata
Drawdata is a Python library that allows you to draw a 2-D dataset of any shape in a Jupyter Notebook. It’s very handy for learning and understanding the behavior of machine learning algorithms.
7. Black
Known as the uncompromising code formatter, Black is a Python package that automatically formats your code to make it more readable.
8. PyCaret
PyCaret is an open-source, low-code machine learning library in Python that automates the machine learning workflow.
If you like PyTorch, you’ll love PyTorch Lightning! It streamlines your model training, automates boilerplate code, and lets you focus on what matters: research and innovation.
10. Streamlit
Although already quite popular, many folks are yet to try Streamlit. It’s a framework for creating web applications for data science and machine learning projects, allowing for easy and interactive data visualization and model deployment.
These Python packages can significantly enhance your data science workflow, making it more efficient and productive. So, if you haven’t explored them yet, now is the time to start!
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