Finding the right color has never been easier#

PyPalettes is a new Python library designed to simplify the use of color palettes in Python charts.

It provides mainly two things:

  • a super-easy-to-use library that requires only 1 line of code (in 99.99% of cases, 2 otherwise šŸ™ƒ) to access thousands of pre-defined and attractive palettes.
  • a web app to browse, filter, search, and preview all available palettes (with bonus: copy-pastable code to reproduce the charts).

Preview and try all the palettes

A small sample of the available palettes

From R to Python#

In R, there are dozens of packages dedicated to colors for data visualization. Then Paletteer came out to aggregate every color palette from those packages into a single one, meaning you only need one package to access almost all the color palettes people have created!

While re-crafting the colors section of the Python Graph Gallery, I started thinking of a way to have a similar tool to Paletteer but for Python.

That's where PyPalettes comes in.

Paletteer has a community-maintained galleryā€”a single page showcasing all its color palettes, along with their original sources and names. With the authorā€™s approval, I scraped this gallery to compile the data.

While there may have been other ways to obtain this information, using a short Python script to reproduce the dataset ensures both simplicity and reproducibility (the script scrapes a page stored locally instead of the web page). To make pypalettes more comprehensive, I also incorporated all built-in colors from Matplotlib.

As a result, I created a dataset containing approximately 2,500 unique palettes, each with a name, a list of hexadecimal colors, and a source.

At this point, the hardest part was already done. I just had to create a simple API to make them usable in a Python environment and add some additional simple features.

And since Yan supported the idea, he created this amazing web app, making it much easier to browse available palettes.

As a thank-you to Paletteer, Yan also created a color finder that features only Paletteer palettes! If you use R, check it out here.


How to use pypalettes#

The goal was to make the simplest API possible, and Iā€™m quite satisfied with the result. For example, you really like the ā€œEsox luciusā€ palette, and you want to make a chart with it.

First, you import the load_cmap() function (the main function of the library):

from pypalettes import load_cmap

And then you just have to call this function with name="Esox_lucius"

cmap = load_cmap("Esox_lucius")

The output of load_cmap() is either a matplotlib.colors.ListedColormap or a matplotlib.colors.LinearSegmentedColormap, depending on the value of the cmap_type argument (default is "discrete", so itā€™s ListedColormap in this case).

Finally, you can create your chart as you normally would:

# load libraries
import cartopy.crs as ccrs
import geopandas as gpd
import matplotlib.pyplot as plt
from matplotlib.font_manager import FontProperties
from highlight_text import fig_text

# load font
personal_path = "/Users/josephbarbier/Library/Fonts/"  # change this to your own path
font = FontProperties(fname=personal_path + "FiraSans-Light.ttf")
bold_font = FontProperties(fname=personal_path + "FiraSans-Medium.ttf")

# projection
proj = ccrs.Mercator()

# load the world dataset
df = gpd.read_file(
    "https://raw.githubusercontent.com/holtzy/The-Python-Graph-Gallery/master/static/data/all_world.geojson"
)
df = df[~df["name"].isin(["Antarctica"])]
df = df.to_crs(proj.proj4_init)

fig, ax = plt.subplots(figsize=(12, 8), dpi=300, subplot_kw={"projection": proj})
ax.set_axis_off()
df.plot(
    column="name",
    ax=ax,
    cmap=cmap,  # here we pass the colormap loaded before
    edgecolor="black",
    linewidth=0.2,
)
fig_text(
    x=0.5,
    y=0.93,
    s="World map with <PyPalettes> colors",
    fontsize=25,
    ha="center",
    font=font,
    highlight_textprops=[{"font": bold_font}],
)
fig_text(
    x=0.85, y=0.14, s="Joseph Barbier & Yan Holtz", fontsize=8, ha="right", font=font
)

plt.show()

And once the code is working, you can change the color map name and see straight away what it would look like!


Other usages#

PyPalettes is primarily designed for matplotlib due to its high compatibility with the cmap argument, but one can imagine much more.

For example, the output of load_cmap() includes attributes like colors and rgb, which return lists of hexadecimal colors or RGB values. These can be used in any contextā€”from Python visualization libraries like Plotly, Plotnine, and Altair to colorimetry, image processing, or any application that requires color!


Learn more#

The main links to find out more about this project are as follows: