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Matplotlib.pyplot.xticks() in Python
  • Last Updated : 12 Apr, 2020

Matplotlib is a library in Python and it is numerical – mathematical extension for NumPy library. Pyplot is a state-based interface to a Matplotlib module which provides a MATLAB-like interface.

matplotlib.pyplot.xticks() Function

The annotate() function in pyplot module of matplotlib library is used to get and set the current tick locations and labels of the x-axis.


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

Parameters: This method accept the following parameters that are described below:

  • ticks: This parameter is the list of xtick locations. and an optional parameter. If an empty list is passed as an argument then it will removes all xticks
  • labels: This parameter contains labels to place at the given ticks locations. And it is an optional parameter.
  • **kwargs: This parameter is Text properties that is used to control the appearance of the labels.

Returns: This returns the following:

  • locs :This returns the list of ytick locations.
  • labels :This returns the list of ylabel Text objects.

The resultant is (locs, labels)

Below examples illustrate the matplotlib.pyplot.xticks() function in matplotlib.pyplot:

Example #1:

# Implementation of matplotlib.pyplot.xticks()
# function
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
# Tweak spacing to prevent clipping of tick-labels
plt.subplots_adjust(bottom = 0.15)


Example #2:

# Implementation of matplotlib.pyplot.xticks()
# function
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.xticks(visible = False) 


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