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vaex-jupyter 0.8.2

Jupyter notebook and Jupyter lab support for vaex

08-23-2023 238 words 2 minutes 0 views

Contents

Jupyter notebook and Jupyter lab support for vaex

Stars: 8279, Watchers: 8279, Forks: 590, Open Issues: 533

The vaexio/vaex repo was created 10 years ago and the last code push was 4 days ago.
The project is extremely popular with a mindblowing 8279 github stars!

How to Install vaex-jupyter

You can install vaex-jupyter using pip

pip install vaex-jupyter

or add it to a project with poetry

poetry add vaex-jupyter

Package Details

Author: Maarten A. Breddels
License: MIT
Homepage: https://www.github.com/maartenbreddels/vaex
PyPi:: https://pypi.org/project/vaex-jupyter/
GitHub Repo:: https://github.com/maartenbreddels/vaex

No vaex-jupyter pypi packages just yet.

Errors

A list of common vaex-jupyter errors.

Code Examples

Here are some vaex-jupyter code examples and snippets.

GitHub Issues

The vaex-jupyter package has 533 open issues on GitHub

[BUG-REPORT] converting massive CSV (50GB) stalls
[BUG-REPORT] AttributeError: 'ProgressBar' object has no attribute 'stime0'
vx.from_pandas(df).export_hdf5(path) giving KeyError while writing pandas df to HDF5 file.
DataFrame.max returning array containing -inf values
Issue on page /tutorial_jupyter.html
[BUG-REPORT] PydanticImportError: BaseSettings has been moved
[BUG-REPORT] AssertionError while performing math operation on shifted columns
Fixes #2350 Implementing take function in Vaex for first n colums
fix bug : open csv file use delimiter other than comma。
[Bug Fix] Broken graphQL query comparisons
Interactive widget fix
dont use take with arrow
Build aarch64 wheels and support python 3.11
fix typos in the learn more about vex section from the README file
Fix: evaluate iterator when selection=True

See more issues on GitHub

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PyOptimus is a Python library that brings together the power of various data processing engines like Pandas, Dask, cuDF, Dask-cuDF, Vaex, and PySpark under a single, easy-to-use API. It offers over 100 functions for data cleaning and processing, including handling strings, processing dates, URLs, and emails. PyOptimus also provides out-of-the-box functions for data exploration and quality fixing. One of the key features of PyOptimus is its ability to handle large datasets efficiently, allowing you to use the same code to process data on your laptop or on a remote cluster of GPUs.