Example usage
pypalettes offers 3 functions:
load_cmap(): the core function that loads a colormap from one of the 2500+ available palettes. All available palettes can be found in the Color Palette Finder.create_cmap(): a function to create your own colormaps from a list of colors.show_cmap(): a function to preview the colors of a given palette.
Since those functions are built for matplotlib, they work with any matplotlib-based plotting library (seaborn, plotnine, (geo)pandas, cartopy, statsmodels, etc).
Also, you have to understand the difference between a palette and a colormap (or cmap).
- palette: a list of colors, such as ["#D57A6D", "#E8B762", "#9CCDDF", "#525052"]. It's not necessarly related to data visualization, but design in general.
- colormap: in the context of
matplotlib, this a special object that contains colors and can be used to map colors and values.
Let's see how to use them in concrete examples!
load_cmap()
- In matplotlib
# mkdocs: render
import matplotlib.pyplot as plt
from pypalettes import load_cmap
cmap = load_cmap("Acadia")
x = [1, 2, 3, 4, 5, 6, 7, 8, 9]
y = [2, 1, 4, 3, 5, 7, 6, 9, 8]
c = [1, 3, 1, 2, 3, 2, 1, 3, 2]
fig, ax = plt.subplots()
ax.scatter(x, y, c=c, s=500, cmap=cmap)
- In seaborn
# mkdocs: render
import seaborn as sns
from pypalettes import load_cmap
cmap = load_cmap("Fun")
palette = cmap.colors # list of colors
df = sns.load_dataset("penguins")
g = sns.lmplot(
data=df,
x="bill_length_mm",
y="bill_depth_mm",
hue="species",
palette=palette,
)
- In plotnine
# mkdocs: render
import pandas as pd
from plotnine import ggplot, aes, geom_bar, theme_minimal, scale_fill_gradientn
from pypalettes import load_cmap
df = pd.DataFrame({
"category": ["A", "B", "C", "D", "E"],
"value": [10, 15, 7, 12, 20]
})
colors = load_cmap("Arches2", reverse=True).colors
(
ggplot(df, aes(x="category", y="value", fill="value"))
+ geom_bar(stat="identity")
+ scale_fill_gradientn(colors=colors)
+ theme_minimal()
)
create_cmap()
This function just requires a list of colors, and then you can use its output the same way as load_cmap():
- Create a categorical colormap
# mkdocs: render
import matplotlib.pyplot as plt
from pypalettes import create_cmap
import numpy as np
cmap = create_cmap(["#D57A6D", "#E8B762", "#9CCDDF", "#525052"])
x = np.linspace(0, 20, 1000)
y = np.sin(x)
plt.scatter(x, y, c=y, cmap=cmap)
plt.colorbar()
- Create a continuous colormap
# mkdocs: render
import matplotlib.pyplot as plt
from pypalettes import create_cmap
import numpy as np
cmap = create_cmap(
["#D57A6D", "#E8B762", "#9CCDDF", "#525052"],
cmap_type="continuous",
)
x = np.linspace(0, 20, 1000)
y = np.sin(x)
plt.scatter(x, y, c=y, cmap=cmap)
plt.colorbar()
show_cmap()
Finally, the utility function to preview the colors from a colormap.
