pythonfix
dtale 3.14.1
Web Client for Visualizing Pandas Objects
Web Client for Visualizing Pandas Objects
Stars: 4738, Watchers: 4738, Forks: 403, Open Issues: 60The man-group/dtale repo was created 5 years ago and the last code push was 1 months ago.
The project is very popular with an impressive 4738 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
Related Packages
Errors
A list of common dtale errors.
Code Examples
Here are some dtale code examples and snippets.
GitHub Issues
The dtale package has 60 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.
Related Packages & Articles
plotly 5.24.1
An open-source, interactive data visualization library for Python
seaborn 0.13.2
Statistical data visualization
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.
missingno 0.5.2
Missing data visualization module for Python.
streamlit 1.36.0
A faster way to build and share data apps
pandas 2.2.3
Powerful data structures for data analysis, time series, and statistics
ta 0.11.0
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.
jupyterlab-lsp 5.1.0
Coding assistance for JupyterLab with Language Server Protocol
xarray 2024.9.0
N-D labeled arrays and datasets in Python