You may use the HSV system to obtain differently saturated and luminated colors for the same hue. Suppose you have at most 10 categories, then the tab10 map can be used to get a certain number of base colors. From those you can choose a couple of lighter shades for the subcategories.

The following would be a function categorical_cmap, which takes as input the number of categories (nc) and the number of subcategories (nsc) and returns a colormap with nc*nsc different colors, where for each category there are nsc colors of same hue.

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors

def categorical_cmap(nc, nsc, cmap="tab10", continuous=False):
    if nc > plt.get_cmap(cmap).N:
        raise ValueError("Too many categories for colormap.")
    if continuous:
        ccolors = plt.get_cmap(cmap)(np.linspace(0,1,nc))
    else:
        ccolors = plt.get_cmap(cmap)(np.arange(nc, dtype=int))
    cols = np.zeros((nc*nsc, 3))
    for i, c in enumerate(ccolors):
        chsv = matplotlib.colors.rgb_to_hsv(c[:3])
        arhsv = np.tile(chsv,nsc).reshape(nsc,3)
        arhsv[:,1] = np.linspace(chsv[1],0.25,nsc)
        arhsv[:,2] = np.linspace(chsv[2],1,nsc)
        rgb = matplotlib.colors.hsv_to_rgb(arhsv)
        cols[i*nsc:(i+1)*nsc,:] = rgb       
    cmap = matplotlib.colors.ListedColormap(cols)
    return cmap

c1 = categorical_cmap(4, 3, cmap="tab10")
plt.scatter(np.arange(4*3),np.ones(4*3)+1, c=np.arange(4*3), s=180, cmap=c1)

c2 = categorical_cmap(2, 5, cmap="tab10")
plt.scatter(np.arange(10),np.ones(10), c=np.arange(10), s=180, cmap=c2)

c3 = categorical_cmap(5, 4, cmap="tab10")
plt.scatter(np.arange(20),np.ones(20)-1, c=np.arange(20), s=180, cmap=c3)    

plt.margins(y=0.3)
plt.xticks([])
plt.yticks([0,1,2],["(5, 4)", "(2, 5)", "(4, 3)"])
plt.show()

Seaborn's palettes are represented as lists of rgb values. You can use these lists to create a new palette. For example:

import seaborn as sns

palette_tab10 = sns.color_palette("tab10", 10)
palette = sns.color_palette([palette_tab10[0], palette_tab10[1], palette_tab10[3]])

sns.palplot(palette_tab10)

sns.palplot(palette)

To obtain a matplotlib colormap, add as_cmap=True:

cmap = sns.color_palette([palette_tab10[0], palette_tab10[1], palette_tab10[3]], as_cmap=True)

Seaborn also allows to just provide a list of colors as the palette parameter, e.g.:

sns.palplot(['DeepSkyBlue', palette_tab10[3], 'Chartreuse'])

Turns out this piece of code from the matplotlib examples gave me the answer I was after.

The hex codes of the "tableau" palette are as follows:

tab:blue : #1f77b4
tab:orange : #ff7f0e
tab:green : #2ca02c
tab:red : #d62728
tab:purple : #9467bd
tab:brown : #8c564b
tab:pink : #e377c2
tab:gray : #7f7f7f
tab:olive : #bcbd22
tab:cyan : #17becf

Using the following code I made a dictionnary containing all the named colors and their respective hex code :

import matplotlib.colors as mcolors


mcolors.TABLEAU_COLORS
mcolors.XKCD_COLORS
mcolors.CSS4_COLORS
#Base colors are in RGB so they need to be converted to HEX
BASE_COLORS_hex = {name:mcolors.rgb2hex(color) for name,color in mcolors.BASE_COLORS.items()}


all_named_colors = {}
all_named_colors.update(mcolors.TABLEAU_COLORS)
all_named_colors.update(BASE_COLORS_hex)
all_named_colors.update(mcolors.CSS4_COLORS)
all_named_colors.update(mcolors.XKCD_COLORS)


#print(all_named_colors)
print(all_named_colors["tab:blue"])
>>> #1f77b4