dx

This package provides convenient formatting and IPython display formatter registration for tabular data and DEX media types.

A Pythonic Data Explorer, open sourced with ❤️ by Noteable, a collaborative notebook platform that enables teams to use and visualize data, together.

Requirements

Python 3.8+

Installation

Poetry

shell poetry add dx

Then import the package:

python import dx

Pip

shell pip install dx

Then import the package:

python import dx

Usage

The dx library currently enables DEX media type visualization of pandas DataFrame and Series objects, as well as numpy ndarray objects. This can be handled in two ways: - explicit dx.display() calls - setting the display_mode to update the IPython display formatter for a session

With dx.display()

dx.display() will display a single dataset using the DEX media type. It currently supports: - pandas DataFrame objects ```python import pandas as pd import random

df = pd.DataFrame({ 'randomints': [random.randint(0, 100) for _ in range(500)], 'randomfloats': [random.random() for _ in range(500)], }) dx.display(df) ```

• tabular data as dict or list types python dx.display([ [1, 5, 10, 20, 500], [1, 2, 3, 4, 5], [0, 0, 0, 0, 1] ])

• .csv or .json filepaths ```python df = dx.randomdataframe() df.tocsv("dxdocssample.csv", index=False)

dx.display("dxdocssample.csv") ```

With dx.set_display_mode()

Using either "simple" or "enhanced" display modes will allow dx will update the current IPython display formatters to allow DEX media type visualization of pandas DataFrame objects for an entire notebook / kernel session instead of the default DataFrame display output.

NOTE: Unlike dx.display(), this only affects pandas DataFrames (or any types set in settings.RENDERABLE_TYPES); it does not affect the display of .csv/.json file data, or dict/list outputs

• dx.set_display_mode("simple")

```python import dx import numpy as np import pandas as pd

# enable DEX display outputs from now on dx.setdisplaymode("simple")

df = pd.readcsv("dxdocs_sample.csv") df python df2 = pd.DataFrame( [ [1, 5, 10, 20, 500], [1, 2, 3, np.nan, 5], [0, 0, 0, np.nan, 1] ], columns=['a', 'b', 'c', 'd', 'e'] ) df2 ```

If, at any point, you want to go back to the default display formatting (vanilla pandas output), use the "plain" display mode. This will revert the IPython display format update to its original state and put the pandas options back to their default values.

• dx.set_display_mode("plain") ```python # revert to original pandas display outputs from now on dx.setdisplaymode("plain")

df = pd.readcsv("dxdocs_sample.csv") df python df2 = pd.DataFrame( [ [1, 5, 10, 20, 500], [1, 2, 3, np.nan, 5], [0, 0, 0, np.nan, 1] ], columns=['a', 'b', 'c', 'd', 'e'] ) df2 ```

Custom Settings

Default settings for dx can be found by calling dx.settings:

Each can be set using dx.set_option(): Setting `DISPLAYMAXROWSto3` for the current session

...or with the dx.settings_context() context manager: Setting `DISPLAYMAXROWSto3` within the current context, leaving options for the rest of the session alone

Generating Sample Data

Documentation coming soon!

Usage Outside of Noteable

If using this package in a notebook environment outside of Noteable, the frontend should support the following media types: - application/vnd.dataresource+json for "simple" display mode - application/vnd.dex.v1+json for "enhanced" display mode

Contributing

See CONTRIBUTING.md.

Code of Conduct

We follow the noteable.io code of conduct.

LICENSE

See LICENSE.md.

Open sourced with ❤️ by Noteable for the community.