The Best jSciPy Alternatives in 2026

Machine learning pipelines can use whylogs, an open-source library for logging data quality and model performance.

The Geni project provides a Clojure dataframe library that runs on Apache Spark.

Weights & Biases supports model training, fine tuning, and management from experimentation through production.

Class-imbalanced machine learning is supported by imbalanced-ensemble, a toolbox for ensemble methods and datasets.

Python developers can use gensim for topic modeling and related natural language processing tasks.

SynapseML provides distributed machine learning algorithms for Spark through its Python library.

mlxtend adds helper modules for Python data analysis and machine learning libraries.

torchaudio provides data manipulation and transformation tools for audio signal processing with PyTorch.

Python developers can use Dash to build data apps and dashboards without requiring JavaScript.

AWS Data Wrangler connects Pandas with AWS data services and databases, including Athena, Glue, Redshift, and S3.

DataComPy compares Pandas, Polars, Spark, and Snowpark dataframes, with output that includes comparison statistics.

Koalas implements the pandas DataFrame API on Apache Spark and is deprecated; for Apache Spark 3.2 and above, use PySpark directly.

For Python data preparation, DataPrep offers an open-source low-code library to collect, clean, and visualize data.

statsmodels provides statistical modeling and econometrics tools in Python.

DataDeps.jl provides reproducible data setup for scientific work, with a Julia package for fetching and using data dependencies.

DSP.jl provides Julia routines for digital signal processing, including filter design, periodograms, convolution, and window functions.

Python developers can use Zipline’s event-driven library to backtest algorithmic trading strategies.

Segment Chinese text in PHP applications with jieba-php, a library for Chinese word tokenization.

The Pattern Recognition Toolbox provides MATLAB tools for pattern recognition and machine learning.

This Node.js library implements Latent Dirichlet Allocation topic modeling in JavaScript.

Netron displays neural network, deep learning, and machine learning models across formats such as ONNX, TensorFlow, and PyTorch.

Browse large JSONL, Parquet, and CSV datasets in VS Code with the Bread Dataset Viewer extension.

The fastai deep learning library includes documentation and setup guidance, including instructions for use with Google Colab.

Prediction with gradient boosting regression tree models is implemented in leaves, a Go software library.