### 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 axis 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 Labels in Matplotlib

Now, let's take a look at how we can rotate the X-Axis 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:

**Note:** This function, like all others here, should be called *after* `plt.plot()`

, lest the ticks end up being potentially cropped or misplaced.

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()
```

**Note:** For this approach to work, you'll need to call `plt.draw()`

*before* accessing or setting the X tick labels. This is because the labels are populated after the plot is drawn, otherwise, they'll return empty text values.

Alternatively, we could've iterated over the `tick`

s 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 Labels in Matplotlib

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

Firstly, you can change it on the Figure-level with `plt.yticks()`

, or on the Axes-lebel 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()
```

Sme 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 `tick`

s 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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