matplotlib.pyplot.axhline() 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.axhline() Function

The axhline() function in pyplot module of matplotlib library is used to add a horizontal line across the axis.

Syntax: matplotlib.pyplot.axhline(y=0, xmin=0, xmax=1, **kwargs) Parameters: This method accept the following parameters that are described below:

y: This parameter is an optional and it is position in data coordinates of the horizontal line.

xmin: This parameter is a scalar and optional. Its default value is 0.

xmax: This parameter is a scalar and optional. Its default value is 1.

Returns: This returns the following:
line : This returns the line created by this function.

Below examples illustrate the matplotlib.pyplot.axhline() function in matplotlib.pyplot: Example #1:

Python3 1==

# Implementation of matplotlib.pyplot.annotate() function

import numpy as np
import matplotlib.pyplot as plt

t = np.linspace(-10, 10, 100)
sig = 1 / t

plt.axhline(y = 0, color ="green", linestyle ="--")
plt.axhline(y = 0.5, color ="green", linestyle =":")
plt.axhline(y = 1.0, color ="green", linestyle ="--")

plt.axvline(color ="black")

plt.plot(t, sig, linewidth = 2, 
         label = r"$\sigma(t) = \frac{1}{x}$")

plt.xlim(-10, 10)
plt.xlabel("t")
plt.title("Graph of 1 / x")
plt.legend(fontsize = 14)

plt.show()

Output:

Example #2:

Python3 1==

# Implementation of matplotlib.pyplot.annotate() 
# function

import numpy as np
import matplotlib.pyplot as plt

x = np.linspace(0, 13, 100)

plt.rcParams['lines.linewidth'] = 2
plt.figure()

plt.plot(x, np.sin(x), label ='Line1', 
         color ='green', linestyle ="--")

plt.plot(x, np.sin(x + 0.5), label ='Line2',
         color ='black', linestyle =":")

plt.axhline(0, label ='Line3', color ='black')


plt.title('Axhline() Example')
l = plt.legend(loc ='upper right')

# legend between blue and orange 
# line
l.set_zorder(2.5)

plt.show()