Matplotlib.pyplot.acorr() in Python

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.acorr() Function

The acorr() function in pyplot module of matplotlib library is used to plot the autocorrelation of x (array-like).

Syntax: matplotlib.pyplot.acorr(x, *, data=None, **kwargs)

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

  • x: This parameter is a sequence of scalar.
  • detrend: This parameter is an optional parameter. Its default value is mlab.detrend_none
  • normed: This parameter is also an optional parameter and contains the bool value. Its default value is True
  • usevlines: This parameter is also an optional parameter and contains the bool value. Its default value is True
  • maxlags: This parameter is also an optional parameter and contains the integer value. Its default value is 10
  • linestyle: This parameter is also an optional parameter and used for plotting the data points, only when usevlines is False.
  • marker: This parameter is also an optional parameter and contains the string. Its default value is ‘o’

Returns: This method returns the following:



  • lags:This method returns the lag vector
  • c:This method returns the auto correlation vector.
  • line : Added LineCollection if usevlines is True, otherwise add Line2D.
  • b: This method returns the horizontal line at 0 if usevlines is True, otherwise None.

The resultant is (lags, c, line, b).

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

Example #1:

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# Implementation of matplotlib.pyplot.acorr()
# function
  
import matplotlib.pyplot as plt
import numpy as np
  
# Time series data
geeks = np.array([24.40, 110.25, 20.05, 22.00,
                  61.90, 7.80, 15.00, 22.80
                  34.90, 57.30])
  
# Plot autocorrelation
plt.acorr(geeks, maxlags = 9)
  
# Add labels to autocorrelation plot
plt.title("Autocorrelation of Geeksforgeeks' Users data")
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
  
# Display the autocorrelation plot
plt.show()

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Output:

Example #2:

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# Implementation of matplotlib.pyplot.acorr() 
# function
  
import matplotlib.pyplot as plt
import numpy as np
  
  
# Fixing random state for reproducibility
np.random.seed(10**7)
  
geeks = np.random.randn(51 )
  
plt.title("Autocorrelation Example")
plt.acorr(geeks, usevlines = True
          normed = True, maxlags = 50
          lw = 2)
  
plt.grid(True)
plt.show()

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Output:

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