Chapter 1: Introduction to Matplotlib Formatting

Matplotlib stands as one of the most formidable plotting libraries within the Python ecosystem. Nevertheless, it often has a specific viewpoint on how plots should appear. While customizing your visualizations is certainly achievable, it can sometimes be unexpectedly complex. This article will address a frequent formatting challenge: creating a plot with x-ticks as multiples of π/2, presented in a visually appealing manner.

By default, Matplotlib generates a plot displaying raw integers on the x-axis and often has an excessive number of ticks on the y-axis, which can lead to a cluttered appearance:

import matplotlib.pyplot as plt

import numpy as np

fig, ax = plt.subplots()

x = np.linspace(-2*np.pi, 2*np.pi, 200)

f = np.sin(x)

ax.plot(x, f)

ax.set_xlabel('x')

ax.set_ylabel('f(x)')

To adjust the spacing of the x-axis ticks, we employ the ax.xaxis.set_major_locator(MultipleLocator(0.5*np.pi)) method. This function spaces the major ticks at intervals of half π. The MultipleLocator class assists in determining the tick interval on an axis, ensuring that the ticks are evenly distributed and aligned with the data. For the y-axis, we can lower the tick frequency to every 0.5:

import matplotlib.pyplot as plt

from matplotlib.ticker import MultipleLocator

import numpy as np

fig, ax = plt.subplots()

x = np.linspace(-2*np.pi, 2*np.pi, 200)

f = np.sin(x)

ax.xaxis.set_major_locator(MultipleLocator(0.5*np.pi))

ax.yaxis.set_major_locator(MultipleLocator(0.5))

ax.set_xlabel('$x$')

ax.set_ylabel(r'$sin(x)$')

ax.grid()

ax.plot(x, f)

While this approach improves the x-axis tick placement, the default numerical labels remain. To achieve a more refined look, with π represented in the labels, we must set the major formatter:

import matplotlib.pyplot as plt

from matplotlib.ticker import MultipleLocator

import numpy as np

fig, ax = plt.subplots()

x = np.linspace(-2*np.pi, 2*np.pi, 200)

f = np.sin(x)

ax.xaxis.set_major_locator(MultipleLocator(0.5*np.pi))

ax.yaxis.set_major_locator(MultipleLocator(0.5))

def format_func(value, tick_number):

"""Formatter for displaying x-ticks as multiples of π/2."""

mult_pi_half = int(np.round(2 * value / np.pi))

sign = "-" if mult_pi_half < 0 else ""

if mult_pi_half == 0:

return "0"

elif abs(mult_pi_half) == 1:

return '$' + sign + r'frac{pi}{2}' + '$'

elif abs(mult_pi_half) == 2:

return '$' + sign + r'pi$'

elif mult_pi_half % 2 > 0:

return r'$frac{' + str(abs(mult_pi_half)) + r'pi}{2}$'

else:

return r"${0}pi$".format(mult_pi_half // 2)

ax.xaxis.set_major_formatter(plt.FuncFormatter(format_func))

ax.set_xlabel('$x$')

ax.set_ylabel(r'$sin(x)$')

ax.grid()

ax.plot(x, f)

Here, we define a custom formatting function for the x-axis ticks using plt.FuncFormatter(format_func). This function formats the x-tick labels in terms of integer multiples of π/2. The format_func takes two parameters: value (the tick value) and tick_number (the index of the tick), though the latter is not utilized in our case. The function calculates the corresponding multiple of π/2 for each tick value and formats the label based on its sign and magnitude through a series of conditional statements.

Conclusion

In this article, we have demonstrated how to proficiently format both the x-axis and y-axis in Matplotlib using the MultipleLocator and FuncFormatter functions. By setting the major locator for the x-axis, reducing the y-axis tick frequency, and employing a custom formatting function for the tick labels, you can create visually appealing and informative plots. With these techniques, you can customize the spacing, labels, and overall formatting of your ticks to produce professional-looking visualizations effortlessly.

Chapter 2: Practical Demonstrations

This video titled "How to Change Datetime Axis Formatting in Matplotlib: A Step-by-Step Guide" provides a comprehensive walkthrough on manipulating datetime axes in Matplotlib, enhancing your data visualization skills.

In "Python Matplotlib Crash Course | Mastering Data Visualization," you'll gain essential insights and techniques to master data visualization using Matplotlib.