Python visualization with Altair

I have been interested in finding a visualization library for Python that is similiar to ggplot2 in R. I like the quick plots in pandas but Matplotlib has never worked for me.

For customized visualizations I like the ggplot2 approach with layers. Its grammar of graphics provides a good balance between adjusting a visualization and productivity. The code with layers is nicely readable.

Vega-Altair, a Python library, uses a grammar to create visualization as well. So I gave it a try and it went well – see code below.

pandas and Altaire are likely to be the visualization tools that I use primarily in Python.

Here is some information on the penguins dataset 🐧 in the visualization. I didn’t want to use mtcars 🚗 again.

import altair as alt
import numpy as np
import pandas as pd
import seaborn as sns

dt = sns.load_dataset("penguins")

(alt.Chart(dt)
    .mark_circle()
    .encode(
      alt.X('flipper_length_mm', scale=alt.Scale(zero=False)),
      alt.Y('body_mass_g', scale=alt.Scale(zero=False)),
      color='species',
      tooltip=['species', 'island', 'sex']
    )
)

dt

	species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex
0	Adelie	Torgersen	39.1	18.7	181.0	3750.0	Male
1	Adelie	Torgersen	39.5	17.4	186.0	3800.0	Female
2	Adelie	Torgersen	40.3	18.0	195.0	3250.0	Female
3	Adelie	Torgersen	NaN	NaN	NaN	NaN	NaN
4	Adelie	Torgersen	36.7	19.3	193.0	3450.0	Female
...	...	...	...	...	...	...	...
339	Gentoo	Biscoe	NaN	NaN	NaN	NaN	NaN
340	Gentoo	Biscoe	46.8	14.3	215.0	4850.0	Female
341	Gentoo	Biscoe	50.4	15.7	222.0	5750.0	Male
342	Gentoo	Biscoe	45.2	14.8	212.0	5200.0	Female
343	Gentoo	Biscoe	49.9	16.1	213.0	5400.0	Male

344 rows × 7 columns