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It’s one thing to make a graph, it’s another to make it look good. Unfortunately, Matplotlib’s default colours don’t really look that great:

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

# An example of a graph that uses the base colours in Matplotlib
x = np.linspace(0, math.tau, 100)
plt.plot(x, -0.3 * np.sin(4 * x + 0.5), c='r', lw=3)
plt.plot(x, 0.5 * np.sin(x) + 0.5, c='g', lw=3)
plt.plot(x, 0.5 * np.sin(1.5 * x) - 0.5, c='b', lw=3)
plt.plot(x, np.sin(2 * x), c='c', lw=3)
plt.plot(x, np.sin(x), c='m', lw=3)
plt.plot(x, np.sin(0.5 * x) - 0.75, c='y', lw=3)
plt.plot(x, -np.sin(0.8 * x), c='k', lw=3)
plt.title('Yuck.')
plt.xlim(0, math.tau)
plt.show()

The ‘Tableau Palette’ is slightly better:

# An example of a graph that uses the Tableau Palette colours in Matplotlib
x = np.linspace(0, math.tau, 100)
plt.plot(x, -0.3 * np.sin(4 * x + 0.5), c='tab:red', lw=3)
plt.plot(x, 0.5 * np.sin(x) + 0.5, c='tab:green', lw=3)
plt.plot(x, 0.5 * np.sin(1.5 * x) - 0.5, c='tab:blue', lw=3)
plt.plot(x, np.sin(2 * x), c='tab:cyan', lw=3)
plt.plot(x, np.sin(x), c='tab:pink', lw=3)
plt.plot(x, np.sin(0.5 * x) - 0.75, c='tab:orange', lw=3)
plt.plot(x, -np.sin(0.8 * x), c='tab:gray', lw=3)
plt.title('The Tableau Palette colours look ok')
plt.xlim(0, math.tau)
plt.show()

One option is to use custom pastel colours. Once you’ve found the hex codes of a nice-looking group of colours you can specify them manually using the hash symbol:

# An example of a graph that uses custom colours
x = np.linspace(0, math.tau, 100)
plt.plot(x, -0.3 * np.sin(4 * x + 0.5), c='#ffc09f', lw=3)  # Red
plt.plot(x, 0.5 * np.sin(x) + 0.5, c='#f6bc66', lw=3)  # Orange
plt.plot(x, 0.5 * np.sin(1.5 * x) - 0.5, c='#e9ff70', lw=3)  # Yellow
plt.plot(x, np.sin(2 * x), c='#84dcc6', lw=3)  # Green
plt.plot(x, np.sin(x), c='#6caddf', lw=3)  # Blue
plt.plot(x, np.sin(0.5 * x) - 0.75, c='#cdb4db', lw=3)  # Purple
plt.plot(x, -np.sin(0.8 * x), c='#d1d1d1', lw=3)  # Grey
plt.title('Custom Pastel Colours')
plt.xlim(0, math.tau)
plt.show()

Colour Gradients

Instead of contrasting colours such as those used in the previous examples, sometimes you need to use multiple similar but different colours in the same plot. Here’s an example of a nice-looking green gradient:

# An example of a graph that uses custom shades of green
x_positions = [0, 1, 2]
heights = np.array([6, 12, 9])
plt.bar(x_positions, heights * 1.2, color='#038B62')
plt.bar(x_positions, heights * 1.0, color='#027F5A')
plt.bar(x_positions, heights * 0.8, color='#027351')
plt.bar(x_positions, heights * 0.6, color='#036648')
plt.bar(x_positions, heights * 0.4, color='#045A40')
plt.bar(x_positions, heights * 0.2, color='#054C35')
plt.title('Shades of Green')
plt.show()

…and here’s a blue example:

# An example of a graph that uses custom shades of blue
x_positions = [0, 1, 2]
heights = np.array([6, 12, 9])
plt.bar(x_positions, heights * 1.2, color='#bfd4db')
plt.bar(x_positions, heights * 1.0, color='#aecbd6')
plt.bar(x_positions, heights * 0.8, color='#a4c3d2')
plt.bar(x_positions, heights * 0.6, color='#96b9d0')
plt.bar(x_positions, heights * 0.4, color='#88aed0')
plt.bar(x_positions, heights * 0.2, color='#78a2cc')
plt.title('Shades of Blue')
plt.show()

Adding Latex

Finally, here’s how to complete the look of a plot:

import random

random.seed(20220708)

# Make figures A6 in size
A = 6
plt.rc('figure', figsize=[46.82 * .5**(.5 * A), 33.11 * .5**(.5 * A)])
# Image quality
plt.rc('figure', dpi=141)
# Be able to add Latex
plt.rc('text', usetex=True)
plt.rc('font', family='serif')
plt.rc('text.latex', preamble=r'\usepackage{textgreek}')

# Plot
plt.title('Custom Colour Gradient\nwith \LaTeX\ in the Labels')
plt.ylabel('Values')
plt.xlabel('Categories')
x = np.linspace(0, math.tau, 100)
colours = ['#002147', '#233a57', '#3c5568', '#557278', '#6d9189', '#85b09a']
for i, colour in enumerate(colours):
    plt.bar(i, random.randint(5, 20), color=colour)
plt.show()

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