Contents

dtale 3.3.0

0

Web Client for Visualizing Pandas Objects

Web Client for Visualizing Pandas Objects

Stars: 4170, Watchers: 4170, Forks: 350, Open Issues: 47

The man-group/dtale repo was created 4 years ago and the last code push was 1 weeks ago.
The project is very popular with an impressive 4170 github stars!

How to Install dtale

You can install dtale using pip

pip install dtale

or add it to a project with poetry

poetry add dtale

Package Details

Author
MAN Alpha Technology
License
LGPL
Homepage
https://github.com/man-group/dtale
PyPi:
https://pypi.org/project/dtale/
GitHub Repo:
https://github.com/man-group/dtale

Classifiers

  • Scientific/Engineering
No  dtale  pypi packages just yet.

Errors

A list of common dtale errors.

Code Examples

Here are some dtale code examples and snippets.

GitHub Issues

The dtale package has 47 open issues on GitHub

  • Column widths when dtale initially hidden
  • 404 error using D-Tale + Jupyter Server Proxy
  • when the number of groups are many the x axis title will cover some legend texts
  • In the charts: Have an option to change the chart transparency (opacity)
  • Enhancement Request to export as standalone HTML file
  • Display data frame name in browser tab instead of device_name:port/dtale/main/X
  • [Bug] Cannot use replacement feature
  • Customization of column analysis for categorical column
  • Range highlight needs to have also AND
  • Interruptting large file upload causes error in view instances.

See more issues on GitHub

Related Packages & Articles

plotly 5.15.0

An open-source, interactive data visualization library for Python

pandas-ta 0.3.14b

An easy to use Python 3 Pandas Extension with 130+ Technical Analysis Indicators. Can be called from a Pandas DataFrame or standalone like TA-Lib. Correlation tested with TA-Lib.

pandas 2.0.3

Powerful data structures for data analysis, time series, and statistics

ta 0.10.2

ta is a Python module that provides a technical analysis library, designed to enable feature engineering from financial time series datasets. It is built on the pandas and numpy libraries and offers a wide range of indicators such as volume, volatility, trend, and momentum indicators. ta is designed for Python developers working in the financial sector, making it a valuable asset in the field of Financial Software and Fintech Solutions, particularly for those developing trading algorithms or investment strategies.