Top 10 Rust Packages for Data Analysis

Are you looking for a powerful and efficient way to analyze your data? Look no further than Rust! This programming language has a growing ecosystem of packages that can help you with everything from data cleaning to machine learning. In this article, we'll take a look at the top 10 Rust packages for data analysis.

1. ndarray

The ndarray package is a fundamental package for scientific computing in Rust. It provides an N-dimensional array object, which is similar to NumPy's ndarray in Python. This package is fast, memory-efficient, and provides a wide range of operations for manipulating arrays. With ndarray, you can easily perform operations like slicing, broadcasting, and element-wise arithmetic on your data.

2. csv

The csv package is a simple and fast CSV reader and writer for Rust. It provides a flexible and easy-to-use interface for working with CSV files. With csv, you can easily read and write CSV files, parse CSV data into Rust structs, and serialize Rust structs into CSV data. This package is perfect for working with large datasets that are stored in CSV format.

3. serde

The serde package is a powerful serialization and deserialization framework for Rust. It provides a flexible and efficient way to serialize Rust data structures into various formats, including JSON, YAML, and TOML. With serde, you can easily convert your data into a format that can be easily consumed by other applications or stored in a database.

4. rayon

The rayon package is a fast and easy-to-use parallelism library for Rust. It provides a simple and intuitive API for parallelizing computations across multiple cores. With rayon, you can easily parallelize operations like map, filter, and reduce on your data. This package is perfect for speeding up data processing tasks that can be parallelized.

5. ndarray-stats

The ndarray-stats package is a collection of statistical functions for ndarray. It provides a wide range of statistical functions, including mean, variance, standard deviation, and correlation. With ndarray-stats, you can easily perform statistical analysis on your data and extract insights from it.

6. ndarray-nn

The ndarray-nn package is a collection of neural network functions for ndarray. It provides a wide range of neural network functions, including convolution, pooling, and activation functions. With ndarray-nn, you can easily build and train neural networks for tasks like image classification and natural language processing.

7. ndarray-image

The ndarray-image package is a collection of image processing functions for ndarray. It provides a wide range of image processing functions, including filtering, transformation, and color space conversion. With ndarray-image, you can easily process and analyze images in Rust.

8. ndarray-linalg

The ndarray-linalg package is a collection of linear algebra functions for ndarray. It provides a wide range of linear algebra functions, including matrix multiplication, eigenvalue decomposition, and singular value decomposition. With ndarray-linalg, you can easily perform linear algebra operations on your data.

9. ndarray-rand

The ndarray-rand package is a collection of random number generation functions for ndarray. It provides a wide range of random number generation functions, including uniform, normal, and exponential distributions. With ndarray-rand, you can easily generate random data for testing and simulation.

10. ndarray-parallel

The ndarray-parallel package is a collection of parallel algorithms for ndarray. It provides a wide range of parallel algorithms, including sorting, searching, and partitioning. With ndarray-parallel, you can easily parallelize operations on your data and speed up your computations.


Rust is a powerful and efficient programming language that is well-suited for data analysis. With its growing ecosystem of packages, Rust provides a wide range of tools for working with data. In this article, we've looked at the top 10 Rust packages for data analysis. Whether you're working with large datasets, building neural networks, or performing statistical analysis, these packages can help you get the job done. So why not give Rust a try for your next data analysis project?

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Written by AI researcher, Haskell Ruska, PhD ( Scientific Journal of AI 2023, Peer Reviewed