The default cmap is sns.cm.rocket. To reverse it set cmap to sns.cm.rocket_r

Using your code:

cmap = sns.cm.rocket_r

ax = sns.heatmap(cm_prob,
                 annot=False,
                 fmt=".3f",
                 xticklabels=print_categories,
                 yticklabels=print_categories,
                 vmin=-0.05,
                 cmap = cmap)

You can reverse all of the matplotlib colormaps by appending _r to the name, i.e., plt.cm.coolwarm vs plt.cm.coolwarm_r.

I believe seaborn uses a cubehelix colormap by default.

So you'd do:

from matplotlib import pyplot
import seaborn as sns

colormap = pyplot.cm.cubehelix_r
flights = sns.load_dataset('flights').pivot("month", "year", "passengers")
sns.heatmap(flights, cmap=colormap)

It seems there is a bug in seaborn that prevents the below idea from working correctly, so falling back to pure matplotlib:

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors
import seaborn as sns
# choose 5 colours to create 5 bins
colors = sns.color_palette('rocket', 5)
levels = [0, 200, 300, 400, 600]
cmap, norm = matplotlib.colors.from_levels_and_colors(levels, colors, extend="max")

flights = sns.load_dataset("flights")
flights = flights.pivot("month", "year", "passengers")

fig, ax = plt.subplots()
im = ax.imshow(flights.values, cmap = cmap, norm=norm)
ax.set(xticks=range(flights.shape[1]), yticks=range(flights.shape[0]),
       xticklabels=flights.columns, yticklabels=flights.index)
ax.tick_params(axis="x", rotation=90)
fig.colorbar(im, ax=ax, spacing="propotional")

plt.show()

Use Listed colors or Linear Segmented Colormap from Matplotlib, eg. like in this question (Discrete legend in seaborn heatmap plot):

myColors = ((0.8, 0.0, 0.0, 1.0), (0.0, 0.8, 0.0, 1.0), (0.0, 0.0, 0.8, 1.0))

cmap = LinearSegmentedColormap.from_list('Custom', myColors, len(myColors))

You can specify your colours using RGB or also hex format. However, seaborn provides many predefined palettes, see https://seaborn.pydata.org/tutorial/color_palettes.html.

"RdBu_r" is a matplotlib colormap. There hence seems little reason to convert it to a list of colors first. Instead just use it as it is.

import matplotlib.pyplot as plt
import seaborn as sns

cmap = plt.get_cmap("RdBu_r")
cmap.set_bad(color='black', alpha=0.5)

sns.heatmap(data, cmap=cmap) 

You have to convert a list of colors from seaborn palette to color map of matplolib (thx to @RafaelLopes for proposed changes):

import seaborn as sns
import matplotlib.pylab as plt
import numpy as np
from matplotlib.colors import ListedColormap

# construct cmap
flatui = ["#9b59b6", "#3498db", "#95a5a6", "#e74c3c", "#34495e", "#2ecc71"]
my_cmap = ListedColormap(sns.color_palette(flatui).as_hex())

N = 500
data1 = np.random.randn(N)
data2 = np.random.randn(N)
colors = np.linspace(0,1,N)
plt.scatter(data1, data2, c=colors, cmap=my_cmap)
plt.colorbar()
plt.show()

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)