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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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