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Matplotlib.axes.Axes.acorr() in Python

  • Last Updated : 21 Apr, 2020

Matplotlib is a library in Python and it is numerical – mathematical extension for NumPy library. The Axes Class contains most of the figure elements: Axis, Tick, Line2D, Text, Polygon, etc., and sets the coordinate system. And the instances of Axes supports callbacks through a callbacks attribute.

matplotlib.axes.Axes.acorr() Function

The Axes.acorr() function in axes module of matplotlib library is used to plot the autocorrelation of x.

Syntax: Axes.acorr(self, 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.axes.Axes.acorr() function in matplotlib.axes:

Example 1:




# Implementation of matplotlib 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
fig, ax = plt.subplots()
ax.acorr(geeks, maxlags = 9)
   
# Add labels to autocorrelation
# plotax.xlabel('X-axis')
ax.set_ylabel('Y-axis')
  
ax.set_title('matplotlib.axes.Axes.acorr() Example')
  
plt.show()

Output:

Example 2:




# Implementation of matplotlib function
import matplotlib.pyplot as plt
import numpy as np
   
   
# Fixing random state for reproducibility
np.random.seed(10**7)
geeks = np.random.randn(100)
  
fig, ax = plt.subplots()
ax.acorr(geeks, usevlines = True, normed = True,
         maxlags = 80, lw = 3)
ax.grid(True)
  
ax.set_title('matplotlib.axes.Axes.acorr() Example')
  
plt.show()

Output:

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