Skip to content

DOC: Update two more links in pandas Ecosystem #60931

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 1 commit into from
Feb 14, 2025
Merged

DOC: Update two more links in pandas Ecosystem #60931

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
4 changes: 2 additions & 2 deletions web/pandas/community/ecosystem.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -158,7 +158,7 @@ df = pd.read_csv("data.csv")
df # discover interesting insights!
```

By printing out a dataframe, Lux automatically [recommends a set of visualizations](https://raw.githubusercontent.com/lux-org/lux-resources/master/readme_img/demohighlight.gif) that highlights interesting trends and patterns in the dataframe. Users can leverage any existing pandas commands without modifying their code, while being able to visualize their pandas data structures (e.g., DataFrame, Series, Index) at the same time. Lux also offers a [powerful, intuitive language](https://lux-api.readthedocs.io/en/latest/source/guide/vis.html>) that allow users to create Altair, matplotlib, or Vega-Lite visualizations without having to think at the level of code.
By printing out a dataframe, Lux automatically [recommends a set of visualizations](https://raw.githubusercontent.com/lux-org/lux-resources/master/readme_img/demohighlight.gif) that highlights interesting trends and patterns in the dataframe. Users can leverage any existing pandas commands without modifying their code, while being able to visualize their pandas data structures (e.g., DataFrame, Series, Index) at the same time. Lux also offers a [powerful, intuitive language](https://lux-api.readthedocs.io/en/latest/source/guide/vis.html) that allow users to create Altair, matplotlib, or Vega-Lite visualizations without having to think at the level of code.

### [D-Tale](https://github.com/man-group/dtale)

Expand Down Expand Up @@ -342,7 +342,7 @@ It supports the following data types:

- pandas data types
- data types defined in the [NTV format](https://loco-philippe.github.io/ES/JSON%20semantic%20format%20(JSON-NTV).htm)
- data types defined in [Table Schema specification](http://dataprotocols.org/json-table-schema/#field-types-and-formats)
- data types defined in [Table Schema specification](https://datapackage.org/standard/table-schema/)

The interface is always reversible (conversion round trip) with two formats (JSON-NTV and JSON-TableSchema).

Expand Down