Matplotlib is one of the most widely used data visualization libraries in Python. Much of Matplotlib's popularity comes from its customization options - you can tweak just about any element from its hierarchy of objects.
In this tutorial, we'll take a look at how to change the tick frequency in Matplotlib. We'll do this on the figure-level as well as the axis-level.
How to Change Tick Frequency in Matplotlib?
Let's start off with a simple plot. We'll plot two lines, with random values:
import matplotlib.pyplot as plt import numpy as np fig = plt.subplots(figsize=(12, 6)) x = np.random.randint(low=0, high=50, size=100) y = np.random.randint(low=0, high=50, size=100) plt.plot(x, color='blue') plt.plot(y, color='black') plt.show()
y range from 0-50, and the length of these arrays is 100. This means, we'll have 100 datapoints for each of them. Then, we just plot this data onto the
Axes object and show it via the PyPlot instance
Now, the frequency of the ticks on the X-axis is 20. They're automatically set to a frequency that seems fitting for the dataset we provide.
Sometimes, we'd like to change this. Maybe we want to reduce or increase the frequency. What if we wanted to have a tick on every 5 steps, not 20?
The same goes for the Y-axis. What if the distinction on this axis is even more crucial, and we'd want to have each tick on every step?
Setting Figure-Level Tick Frequency in Matplotlib
Let's change the figure-level tick frequency. This means that if we have multiple
Axes, the ticks on all of these will be uniform and will have the same frequency:
import matplotlib.pyplot as plt import numpy as np fig = plt.subplots(figsize=(12, 6)) x = np.random.randint(low=0, high=50, size=100) y = np.random.randint(low=0, high=50, size=100) plt.plot(x, color='blue') plt.plot(y, color='black') plt.xticks(np.arange(0, len(x)+1, 5)) plt.yticks(np.arange(0, max(y), 2)) plt.show()
You can use the
yticks() functions and pass in an array denoting the actual ticks. On the X-axis, this array starts on
0 and ends at the length of the
x array. On the Y-axis, it starts at
0 and ends at the max value of
y. You can hard code the variables in as well.
The final argument is the
step. This is where we define how large each step should be. We'll have a tick at every
5 steps on the X-axis and a tick on every
2 steps on the Y-axis:
Setting Axis-Level Tick Frequency in Matplotlib
If you have multiple plots going on, you might want to change the tick frequency on the axis-level. For example, you'll want rare ticks on one graph, while you want frequent ticks on the other.
You can use the
set_yticks() functions on the returned
Axes instance when adding subplots to a
Figure. Let's create a
Figure with two axes and change the tick frequency on them separately:
import matplotlib.pyplot as plt import numpy as np fig = plt.figure(figsize=(12, 6)) ax = fig.add_subplot(121) ax2 = fig.add_subplot(122) x = np.random.randint(low=0, high=50, size=100) y = np.random.randint(low=0, high=50, size=100) z = np.random.randint(low=0, high=50, size=100) ax.plot(x, color='blue') ax.plot(y, color='black') ax2.plot(y, color='black') ax2.plot(z, color='green') ax.set_xticks(np.arange(0, len(x)+1, 5)) ax.set_yticks(np.arange(0, max(y), 2)) ax2.set_xticks(np.arange(0, len(x)+1, 25)) ax2.set_yticks(np.arange(0, max(y), 25)) plt.show()
Now, this results in:
In this tutorial, we've gone over several ways to change the tick frequency in Matplotlib both on the figure-level as well as the axis-level.
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