# 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

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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:

 # Implementation of matplotlib.pyplot.annotate() function  import numpy as npimport 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:

 # Implementation of matplotlib.pyplot.annotate() # function  import numpy as npimport matplotlib.pyplot as plt  x = np.linspace(0, 13, 100)  plt.rcParams['lines.linewidth'] = 2plt.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 # linel.set_zorder(2.5)  plt.show()

Output:

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