matplotlib.pyplot.xticks() function is used to get or set the x-axis ticks in a plot. This function allows you to modify the labels and positions of the ticks along the x-axis, providing greater flexibility and control over your plots. Let's see an example to better understand this.

Example: Set Custom Tick Positions and Labels

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [2, 3, 5, 7, 11]

plt.plot(x, y)

# Set custom ticks and labels
plt.xticks([1, 3, 5], ['One', 'Three', 'Five'])

plt.show()

import matplotlib.pyplot as plt

​

x = [1, 2, 3, 4, 5]

y = [2, 3, 5, 7, 11]

​

plt.plot(x, y)

​

# Set custom ticks and labels

plt.xticks([1, 3, 5], ['One', 'Three', 'Five'])

​

plt.show()

Output:

Explanation:

• We can define specific tick positions and corresponding labels for the x-axis.
• The x-axis will have ticks at positions 1, 3, and 5.
• The labels will be "One", "Three", and "Five" corresponding to these positions.

Syntax

matplotlib.pyplot.xticks(ticks=None, labels=None, **kwargs)

Parameters:

• ticks: A list or array of positions where you want the ticks to appear on the x-axis. If not provided, it will return the current positions of the ticks.
• labels: A list or array of labels corresponding to each tick position. These labels will be displayed at the tick positions. If not provided, the function will use the default numerical labels.
• **kwargs: Additional keyword arguments, such as font size, color, rotation, etc., to customize the appearance of the ticks.

Return Value:

The function returns the current x-axis tick positions and labels.

Examples of Matplotlib.pyplot.xticks()

1. Customizing X-axis Tick Labels with Rotation and Adjusting Margins

import numpy as np 
import matplotlib.pyplot as plt 
	
x = [1, 2, 3, 4] 
y = [95, 38, 54, 35] 
labels = ['Geeks1', 'Geeks2', 'Geeks3', 'Geeks4'] 

plt.plot(x, y) 

# You can specify a rotation for the tick labels in degrees or with keywords. 
plt.xticks(x, labels, rotation ='vertical') 

# Pad margins so that markers don't get clipped by the axes 
plt.margins(0.2) 

# Tweak spacing to prevent clipping of tick-labels 
plt.subplots_adjust(bottom = 0.15) 
plt.show() 

import numpy as np 

import matplotlib.pyplot as plt 

x = [1, 2, 3, 4] 

y = [95, 38, 54, 35] 

labels = ['Geeks1', 'Geeks2', 'Geeks3', 'Geeks4'] 

​

plt.plot(x, y) 

​

# You can specify a rotation for the tick labels in degrees or with keywords. 

plt.xticks(x, labels, rotation ='vertical') 

​

# Pad margins so that markers don't get clipped by the axes 

plt.margins(0.2) 

​

# Tweak spacing to prevent clipping of tick-labels 

plt.subplots_adjust(bottom = 0.15) 

plt.show()

Output:

Explanation: This code creates a plot with custom x-axis labels (Geeks1, Geeks2, etc.), rotates them vertically, and adjusts margins to avoid clipping of tick labels. plt.subplots_adjust() ensures the labels fit within the plot.

2. Using Matplotlib.pyplot.xticks() with Zoomed-In Inset Plot

import matplotlib.pyplot as plt 

from mpl_toolkits.axes_grid1.inset_locator import inset_axes, zoomed_inset_axes 

def get_demo_image(): 
	from matplotlib.cbook import get_sample_data 
	import numpy as np 
	f = get_sample_data("axes_grid / bivariate_normal.npy", 
						asfileobj = False) 
	z = np.load(f) 
	
	# z is a numpy array of 15x15 
	return z, (3, 19, 4, 13) 


fig, ax = plt.subplots(figsize =[5, 4]) 

Z, extent = get_demo_image() 

ax.set(aspect = 1, 
	xlim =(0, 65), 
	ylim =(0, 50)) 


axins = zoomed_inset_axes(ax, zoom = 2, 
						loc ='upper right') 

im = axins.imshow(Z, extent = extent, 
				interpolation ="nearest", 
				origin ="upper") 

plt.xlabel('X-axis') 
plt.ylabel('Y-axis') 

plt.xticks(visible = False) 
plt.show() 

import matplotlib.pyplot as plt 

​

from mpl_toolkits.axes_grid1.inset_locator import inset_axes, zoomed_inset_axes 

​

def get_demo_image(): 

    from matplotlib.cbook import get_sample_data 

    import numpy as np 

    f = get_sample_data("axes_grid / bivariate_normal.npy", 

                        asfileobj = False) 

    z = np.load(f) 

    # z is a numpy array of 15x15 

    return z, (3, 19, 4, 13) 

​

​

fig, ax = plt.subplots(figsize =[5, 4]) 

​

Z, extent = get_demo_image() 

​

ax.set(aspect = 1, 

    xlim =(0, 65), 

    ylim =(0, 50)) 

​

​

axins = zoomed_inset_axes(ax, zoom = 2, 

                        loc ='upper right') 

​

im = axins.imshow(Z, extent = extent, 

                interpolation ="nearest", 

                origin ="upper") 

​

plt.xlabel('X-axis') 

plt.ylabel('Y-axis') 

​

plt.xticks(visible = False) 

plt.show()

Output:

Explanation: This code creates a main plot with a zoomed-in inset using zoomed_inset_axes. The get_demo_image() function loads a sample 15x15 data array, which is displayed in both the main plot and the inset. The inset plot zooms in on a specific portion of the main plot.