Optimus is the missing framework to profile, clean, process and do ML in a distributed fashion using Apache Spark (PySpark).
Prepare, explore, visualize and create Machine Learning models for Big Data with this library.
Optimus is free and open source software.
Key Features
- Simple and robust – prepare, explore, visualize your data in few lines of code.
- Easy, fast, parallelized and scalable data cleansing, exploration and Machine Learning Models creation.
- Local or in the cloud.
- Easy to use API. Optimus expands the Spark DataFrame functionality adding .rows and .cols attributes.
- Connect to external API to enrich your data.
- String clustering – cluster similar strings and change it for a single value.
Website: hi-optimus.com
Support: GitHub Code Repository
Developer: Argenis Leon, Favio Vazquez and contributors
License: Apache License 2.0
Optimus is written in Python. Learn Python with our recommended free books and free tutorials.
Related Software
| Python Data Analysis | |
|---|---|
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| statsmodels | Statistical modeling and econometrics in Python |
| Dask | Advanced parallelism for analytics |
| Orange | Component-based framework for machine learning and data mining |
| Modin | Drop-in replacement for pandas |
| Vaex | Fast visualization of big data |
| AWS DW | Extends the power of pandas library |
| yt | Multi-code Toolkit for Analyzing and Visualizing Volumetric Data |
| HoloViews | Make Data Analysis and Visualization Seamless |
| datatable | Manipulate 2-dimensional tabular data structures |
| xarray | Work with labelled multi-dimensional arrays and datasets |
| pyjanitor | Extend pandas with readable data-cleaning functions |
| Optimus | Agile Data Preparation Workflows |
Read our verdict in the software roundup.
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