Python library for plotting bivariate choropleth maps in Matplotlib, Folium and Lonboard.
With pip:
pip install bivario
With uv:
uv add bivario
With conda:
conda install conda-forge::bivario
Example of a Folium map in light and dark modes.
Simple Folium map:
from bivario import explore_bivariate_data
from bivario.example_data import nyc_bike_trips
explore_bivariate_data(
nyc_bike_trips(), "morning_starts", "morning_ends"
)Simple Lonboard map:
from bivario import viz_bivariate_data
from bivario.example_data import nyc_bike_trips
viz_bivariate_data(
nyc_bike_trips(), "morning_starts", "morning_ends"
)In dark mode:
from bivario import explore_bivariate_data
from bivario.example_data import nyc_bike_trips
explore_bivariate_data(
nyc_bike_trips(),
column_a="morning_starts",
column_b="morning_ends",
dark_mode=True, # default is False
)Use other palette:
from bivario import explore_bivariate_data
from bivario.example_data import nyc_bike_trips
explore_bivariate_data(
nyc_bike_trips(),
column_a="morning_starts",
column_b="morning_ends",
cmap="bubblegum"
)Set numerical mode (disable bucketing):
Note
By default, map is created using Mapclassify library with NaturalBreaks method to split data into 5 categories.
Method and number of buckets can be changed or disabled completely.
from bivario import explore_bivariate_data
from bivario.example_data import nyc_bike_trips
explore_bivariate_data(
nyc_bike_trips(),
column_a="morning_starts",
column_b="morning_ends",
dark_mode=True,
cmap="late_sunset",
scheme=False, # or set to None
legend_size_px=300,
)
Example of a Folium map in a numerical mode.
Palettes in bivario are created by blending 2 or 4 colours in a 2D space using OKLab colour space. The operations on input and output are done in RGB, an internally are transformed into OKLab values using colour-science library.
bivario has 4 modes of Bivariate colourmaps:
AccentsBivariateColourmap - defined by two accent colours and a light and a dark colour.
CornersBivariateColourmap - defined by 4 corner colours (accent a/b, low value and high value)
MplCmapBivariateColourmap - defined by 2 Matplotlib colourmaps along axis X and Y
NamedBivariateColourmap - can load predefined palette from string name
You can load these palettes by passing a string name to the cmap attribute, or load a NamedBivariateColourmap object:
cmap = NamedBivariateColourmap("coral_ocean")
# You can call it similar to Matplotlib Colormap object
rgb_values = cmap(values_a=[0, 1], values_b=[0, 1])