Skip to main content

PyPi Binder Lite

Profile your Pandas Dataframes! Autoprofiler will automatically visualize your Pandas dataframes after every execution, no extra code necessary.

Autoprofiler allows you to spend less time specifying charts and more time interacting with your data by automatically showing you profiling information like:

  • Distribution of each column
  • Sample values
  • Summary statistics

Updates profiles as your data updates

screenshot of Autoprofiler

Autoprofiler reads your current Jupyter notebook and produces profiles for the Pandas Dataframes in your memory as they change.

demo of Autoprofiler

Install

To instally locally use pip and then open a jupyter notebook and the extension will be running.

pip install -U digautoprofiler

Try it out

To try out Autoprofiler in a hosted notebook, use one of the options below

Jupyter Lite Binder
Lite Binder

Development Install

For development install instructions, see CONTRIBUTING.md.

If you're having install issues, see TROUBLESHOOTING.md.

Acknowledgements

Big thanks to the Rill Data team! Much of our profiler UI code is adapted from Rill Developer.

Let us know what you think! 📢

We would love to hear your feedback on how you are using AutoProfiler! Please fill out this form or email Will at willepp@cmu.edu.

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

digautoprofiler-0.2.0.tar.gz (1.7 MB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

digautoprofiler-0.2.0-py3-none-any.whl (2.8 MB view details)

Uploaded Python 3

File details

Details for the file digautoprofiler-0.2.0.tar.gz.

File metadata

  • Download URL: digautoprofiler-0.2.0.tar.gz
  • Upload date:
  • Size: 1.7 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.0 CPython/3.10.4

File hashes

Hashes for digautoprofiler-0.2.0.tar.gz
Algorithm Hash digest
SHA256 fd2bf458b075a92521fc79b6659f0101938639e5af59a58cadb6922b37c96e4a
MD5 3849cc2d1ae875e8f7f44db58c0331a9
BLAKE2b-256 83812de15324b00b2803aa0ad9f898a98f3fb199621e9553da8922a6aa5925c2

See more details on using hashes here.

File details

Details for the file digautoprofiler-0.2.0-py3-none-any.whl.

File metadata

File hashes

Hashes for digautoprofiler-0.2.0-py3-none-any.whl
Algorithm Hash digest
SHA256 5cf7101159defe0bb3fe1314b8513797fc25f0efee3603595557d20ef33b8915
MD5 bd86c7e52eec383b81128d3cbe61d314
BLAKE2b-256 6ba695f1d53ae6cc20bdb68909abd70a89e4fcac7258ebdff6bb5c7d9118de85

See more details on using hashes here.

Release history Release notifications | RSS feed

3.0.1

2 files

1.0.2

2 files

1.0.1

2 files

1.0.0

2 files

0.2.10

2 files

0.2.9

2 files

0.2.8

2 files

0.2.7

2 files

0.2.6

2 files

0.2.5

2 files

0.2.4

2 files

0.2.3

2 files

0.2.2

2 files

0.2.1

2 files

This release

0.2.0 This release

2 files

0.1.9

2 files

0.1.8

2 files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page