Use a colormap as a palette in Seaborn

Question

This is probably a misunderstanding how colormaps are different from palettes, but I'd like to use a colormap that is not available in seaborn for coloring my binned dataset. I tried using palettable and now cmocean in particular directly but will get a TypeError;

'LinearSegmentedColormap' object is not iterable

Using any of the palettes that are available in Seaborn will work just fine, but I need a palette that doesn't go to white as this adds a weird 'banding' to the plot.

I have a dataframe with 3 columns with numerical data, dimensions and added a bin column for the colors usage in the plot.

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import cmocean

cmap=cmocean.cm.balance
cpal=sns.color_palette(cmap,n_colors=64,desat=0.2)

plt.style.use("seaborn-dark")
ax = sns.stripplot(x='Data', y='Dimension', data=dfBalance, jitter=0.15, edgecolor='none', alpha=0.4, size=4, hue='bin', palette=cpal)
sns.despine()
ax.legend_.remove()
plt.show()

Answer 1

Seaborn does not take a Colormap instance as input for .color_palette. It takes

name of matplotlib cmap, [...], or a list of colors in any format matplotlib accepts

Since cmocean registers its colormaps with matplotlib with a "cmo." prefix, you would do

import seaborn as sns
import cmocean

cpal = sns.color_palette("cmo.balance", n_colors=64, desat=0.2)

In case you have a custom colormap created yourself or from any other package, you might register it yourself.

import seaborn as sns
import matplotlib.cm
import matplotlib.colors

cmap = matplotlib.colors.LinearSegmentedColormap.from_list("", ["brown", "pink", "limegreen"])
matplotlib.cm.register_cmap("mycolormap", cmap)
cpal = sns.color_palette("mycolormap", n_colors=64, desat=0.2)