### 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))
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:

### 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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