category: "london"

title: "pandas visualisation vol.2"

level: "All"

time: "18:30"

rsvp_link: https://www.meetup.com/Python-Sprints/events/262426168/

project:

sponsor: ecom

Let's continue working on visualisation in `pandas`.

Plotting in `pandas` is very easy, mainly by using `Series.plot()` and `DataFrame.plot()` methods.

There is a plotting subsystem in `pandas` based in `matplotlib` that implement different types of

plots (e.g. lines, bars, boxplots, kde...).

In the last months, several new projects have been create to address new use cases for visualising

in `pandas`. For example, to generate interactive plots. Some of these projects are:

- https://hvplot.pyviz.org

- https://github.com/PatrikHlobil/Pandas-Bokeh

- https://github.com/altair-viz/pdvega/

Those libraries have been monkey patching `pandas` to be able to plot easily, so plotting can be

done by using `DataFrame.hvplot()`, `DataFrame.plot_bokeh()`...

But a better design would be to decouple the existing plotting code in pandas into an extension

registered with the `pandas` extension capabilities, and be able to select with an option with

plotting backend the user wants to use. The resulting code would be:

pandas.set_option('plotting.backend', 'hvplot')

df.plot()

With this architecture, there are several advantages:

- Developing new plotting backends for pandas becomes much simpler

- Plotting backends share a common API

- Users of pandas don't need to learn a new syntax for each plugin

- Migrating existing code becomes trivial, by just adding a single line of code setting the option for the backend

- Internal pandas code becomes cleaner, with the plotting code fully decoupled from the rest

Work on this is already going on, with a first PR that decoupled the current plotting code:

In this sprint we will continue the work, by working on different tasks:

- Adding the option to select the backend

- Defining and documenting the pandas plotting API

- Porting existing libraries to the new plotting API

- Document current pandas functionality

We also can work on other pandas issues, for example:

- pytest-azurepipelines: <https://github.com/pandas-dev/pandas/issues/26601>

- Documentation warnings: <https://github.com/pandas-dev/pandas/issues/24173> / <https://github.com/pandas-dev/pandas/issues/24661>

- PandasArray in API docs: <https://github.com/pandas-dev/pandas/issues/24507>

- pyarrow failure: <https://github.com/pandas-dev/pandas/issues/26656>

- Organise dependencies: <https://github.com/pandas-dev/pandas/issues/26659>

- Remove isort dependency: <https://github.com/pandas-dev/pandas/issues/26661>

- Feel free to propose yours

As usual, we will give priority to join the sprint to the next people:

- Experienced open source contributors

- People from underrepresented minorities in our sprints

Agenda

- 6:30pm: Food and networking

- 6:45pm: Presentation of the project and the sponsor

- 7:00pm: Coding

The day of the sprint

- Bring your own laptop if you can

- Join the [Gitter channel](https://gitter.im/py-sprints/pandas-bokeh) of the sprint

obj_id: ecom

name: "Ecom Recruitment"

logo: static/images/sponsors/ecom.png

link: https://www.interquestgroup.com/brands/ecom

address: "Cannon Green, 27 Bush Lane, London, United Kingdom, EC4R 0AA"

lat: 51.510744

lng: -0.0917458

ECOM is the digital talent brand of InterQuest Group. We have won the award for being LinkedIn’ most socially engaged staffing agency in 2016, 2017 and 2018. We operate at the forefront of digital transformation. We recruit the in-demand, technical skills that are shaping the new digital economy across analytics, information security, digital, information technology, networks & change management. We look to partner with our clients to bring optimized solutions to help them create opportunities for talented individuals across the verticals we serve.