10 Lesser-Known Python Libraries Every Data Scientist Should Be Using in 2026
As a data scientist, you're likely familiar with popular libraries like NumPy, Pandas, and Scikit-learn. However, there are many other powerful tools in the Python ecosystem that can take your analysis to the next level.
In this article, we'll explore ten lesser-known Python libraries that every data scientist should be using in 2026. From data cleaning to visualization, these libraries offer a range of features and functionalities that can simplify your workflow and improve your results.
Data Cleaning and Preprocessing
- Vaex: A high-performance library for data manipulation and analysis. Vaex provides an efficient way to handle large datasets by storing them in memory as a series of arrays.
- Pandas-Int64Index: An extension to Pandas that allows for 64-bit integer indexing, making it ideal for handling large datasets with unique identifiers.
Data Visualization
- Plotnine: A Python implementation of the popular ggplot2 library. Plotnine provides an easy-to-use interface for creating beautiful and informative visualizations.
- MPL-Tools: A collection of tools for creating high-quality plots using Matplotlib. MPL-Tools offers a range of features, including automatic label placement and custom plot themes.
Machine Learning
- Optunity: An optimization library that provides an efficient way to optimize machine learning models. Optunity supports a range of algorithms, including gradient descent and Bayesian optimization.
- H2O-3: A Python implementation of the popular H2O machine learning platform. H2O-3 offers a range of features, including automatic model selection and hyperparameter tuning.
Data Storage and Retrieval
- Apache Arrow: An open-source library for in-memory data processing. Apache Arrow provides a columnar storage format that enables efficient data transfer between different systems.
- Feather: A lightweight library for storing and retrieving data. Feather supports a range of formats, including JSON and CSV.
Data Science Pipelines
- PySpark: A Python API for working with Apache Spark. PySpark provides an easy-to-use interface for creating complex data science pipelines.
- Dask-ML: A library for distributed machine learning that integrates seamlessly with Dask. Dask-ML offers a range of features, including automatic model selection and hyperparameter tuning.
These libraries offer a wealth of new possibilities for data scientists looking to improve their workflow and results. By incorporating these tools into your toolkit, you'll be able to tackle complex analysis tasks with ease and efficiency.
Whether you're working on a small-scale project or a large-scale enterprise application, these libraries are sure to make an impact. So why wait? Dive in and explore the world of Python data science – there's never been a better time!
By Malik Abualzait
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