Learn how to transform a data table into a heat map using python!

To run this notebook you need to install basic datascience libraries:

import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt

# Minimalist design
sns.set_style("whitegrid")

# Examples of palette, choose wisely !
# More information here: https://seaborn.pydata.org/tutorial/color_palettes.html
sns.palplot(sns.cubehelix_palette(8, start=.5, rot=-.75))
sns.palplot(sns.diverging_palette(220, 20, sep=10, n=10))

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# Toy dataset
data = np.array([[14,14,64,0,7,0],
                  [1,15,52,16,11,5],
                 [6,17,53,9,7,3],
                 [0,6,24,29,35,6],                 
                 [0,21,18,18,31,11],   
                 [0,9,21,9,9,53]])

# Labels
labelsX = ['X1','X2', 'X3', 'X4', 'X5', 'X6']
labelsY = ['Y1','Y2', 'Y3', 'Y4', 'Y5', 'Y6']

# Version 0

import pandas as pd

pd.DataFrame(data, columns=labelsX, index=labelsY)
X1 X2 X3 X4 X5 X6
Y1 14 14 64 0 7 0
Y2 1 15 52 16 11 5
Y3 6 17 53 9 7 3
Y4 0 6 24 29 35 6
Y5 0 21 18 18 31 11
Y6 0 9 21 9 9 53 
# Version 1

fig, ax = plt.subplots(figsize=(10,10))

sns.heatmap(data, linewidths=.5, square=True, annot=True, fmt="d", robust=True,
           xticklabels=labelsX, yticklabels=labelsY,
           cmap=sns.cubehelix_palette(8, start=.5, rot=-.75),
           cbar_kws={'label': 'Legend description', 'shrink': 0.8})

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# Version 2

fig, ax = plt.subplots(figsize=(10,10))

sns.heatmap(data, linewidths=.5, square=True, annot=True, fmt="d", robust=True,
           xticklabels=labelsX, yticklabels=labelsY,
           cmap=sns.cubehelix_palette(8, start=.5, rot=-.75),
           cbar_kws={'label': 'Legend description', 'shrink': 0.8})

ax.xaxis.tick_top()

plt.ylabel('X axis Label')
plt.ylabel('X axis\n Label',rotation=0)
plt.xlabel('Y axis Label')

plt.yticks(rotation=0)

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# Version 3

fig, ax = plt.subplots(figsize=(10,10))

sns.heatmap(data, linewidths=.5, square=True, annot=True, fmt="d", robust=True,
           xticklabels=labelsX, yticklabels=labelsY,
           cmap=sns.diverging_palette(220, 20, sep=10, n=10), center=10,
           cbar_kws={'label': 'Legend description', 'shrink': 0.8})

ax.xaxis.tick_top()

plt.ylabel('X axis Label')
plt.ylabel('X axis\n Label',rotation=0)
plt.xlabel('Y axis Label')

plt.yticks(rotation=0)

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# Version 4

fig, ax = plt.subplots(figsize=(10,10))

sns.heatmap(data, linewidths=.5, square=True, annot=True, fmt="d", robust=True,
           xticklabels=labelsX, yticklabels=labelsY,
           cmap=sns.diverging_palette(220, 20, sep=10, n=10), center=10,
           cbar_kws = {'use_gridspec':False,'location':'top','label': 'Legend description', 'shrink': 0.78})

ax.xaxis.tick_top()

plt.ylabel('X axis Label')
plt.ylabel('X axis\n Label',rotation=0)
plt.xlabel('Y axis Label')

plt.yticks(rotation=0)

png