Rotate Axis Labels in Matplotlib

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 rotate tick text/labels in a Matplotlib plot.

Creating a Plot

Let's create a simple plot first:

import matplotlib.pyplot as plt
import numpy as np

x = np.arange(0, 10, 0.1)
y = np.sin(x)

plt.plot(x, y)
plt.show()

Rotate X-Axis Tick Labels in Matplotlib

Now, let's take a look at how we can rotate the X-Axis tick labels here. There are two ways to go about it - change it on the Figure-level using plt.xticks() or change it on an Axes-level by using tick.set_rotation() individually, or even by using ax.set_xticklabels() and ax.xtick_params().

Let's start off with the first option:

import matplotlib.pyplot as plt
import numpy as np

x = np.arange(0, 10, 0.1)
y = np.sin(x)

plt.plot(x, y)
plt.xticks(rotation = 45) # Rotates X-Axis Ticks by 45-degrees
plt.show()

Here, we've set the rotation of xticks to 45, signifying a 45-degree tilt, counterclockwise:

Another option would be to get the current Axes object and call ax.set_xticklabels() on it. Here we can set the labels, as well as their rotation:

import matplotlib.pyplot as plt
import numpy as np

x = np.arange(0, 10, 0.1)
y = np.sin(x)

plt.plot(x, y)

ax = plt.gca()
plt.draw()

ax.set_xticklabels(ax.get_xticks(), rotation = 45)

plt.show()

Alternatively, we could've iterated over the ticks in the ax.get_xticklabels() list. Then, we can call tick.set_rotation() on each of them:

import matplotlib.pyplot as plt
import numpy as np

x = np.arange(0, 10, 0.1)
y = np.sin(x)

plt.plot(x, y)

ax = plt.gca()
plt.draw()

for tick in ax.get_xticklabels():
    tick.set_rotation(45)
plt.show()

This also results in:

And finally, you can use the ax.tick_params() function and set the label rotation there:

import matplotlib.pyplot as plt
import numpy as np

x = np.arange(0, 10, 0.1)
y = np.sin(x)

plt.plot(x, y)

ax = plt.gca()
ax.tick_params(axis='x', labelrotation = 45)
plt.show()

This also results in:

Rotate Y-Axis Tick Labels in Matplotlib

The exact same steps can be applied for the Y-Axis tick labels.

Firstly, you can change it on the Figure-level with plt.yticks(), or on the Axes-level by using tick.set_rotation() or by manipulating the ax.set_yticklabels() and ax.tick_params().

Let's start off with the first option:

import matplotlib.pyplot as plt
import numpy as np

x = np.arange(0, 10, 0.1)
y = np.sin(x)

plt.plot(x, y)
plt.yticks(rotation = 45)
plt.show()

Same as last time, this sets the rotation of yticks by 45-degrees:

Now, let's work directly with the Axes object:

import matplotlib.pyplot as plt
import numpy as np

x = np.arange(0, 10, 0.1)
y = np.sin(x)

plt.plot(x, y)

ax = plt.gca()
plt.draw()

ax.set_yticklabels(ax.get_yticks(), rotation = 45)

plt.show()

The same note applies here, you have to call plt.draw() before this call to make it work correctly.

Now, let's iterate over the list of ticks and set_rotation() on each of them:

import matplotlib.pyplot as plt
import numpy as np

x = np.arange(0, 10, 0.1)
y = np.sin(x)

plt.plot(x, y)

ax = plt.gca()
plt.draw()

for tick in ax.get_yticklabels():
    tick.set_rotation(45)
plt.show()

This also results in:

And finally, you can use the ax.tick_params() function and set the label rotation there:

import matplotlib.pyplot as plt
import numpy as np

x = np.arange(0, 10, 0.1)
y = np.sin(x)

plt.plot(x, y)

ax = plt.gca()
ax.tick_params(axis='y', labelrotation = 45)
plt.show()

This also results in:

Rotate Dates to Fit in Matplotlib

Most often, the reason people rotate ticks in their plots is because they contain dates. Dates can get long, and even with a small dataset, they'll start overlapping and will quickly become unreadable.

Of course, you can rotate them like we did before, usually, a 45-degree tilt will solve most of the problems, while a 90-degree tilt will free up even more.

Though, there's another option for rotating and fixing dates in Matplotlib, which is even easier than the previous methods - fig.autofmt__date().

This function can be used either as fig.autofmt_xdate() or fig.autofmt_ydate() for the two different axes.

Let's take a look at how we can use it on the Seattle Weather Dataset:

import pandas as pd
import matplotlib.pyplot as plt

weather_data = pd.read_csv("seattleWeather.csv")

fig = plt.figure()
plt.plot(weather_data['DATE'], weather_data['PRCP'])
fig.autofmt_xdate()
plt.show()

This results in:

Conclusion

In this tutorial, we've gone over several ways to rotate Axis text/labels in a Matplotlib plot, including a specific way to format and fit dates .

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