# Change Tick Frequency in Matplotlib - Python

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### Introduction

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()
``````

`x` and `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 `plt`: 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 `xticks()` and `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_xticks()` and `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))

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: ### Conclusion

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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Reference: stackabuse.com