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Python Pandas – Plotting the Autocorrelation Plot
  • Last Updated : 25 Aug, 2020

Pandas can be used to plot the Autocorrelation Plot on a graph. Plotting the Autocorrelation Plot on a graph can be done using the autocorrelation_plot() method of the plotting module. This function generates the Autocorrelation plot for time series.

Autocorrelation plot

Autocorrelation plots are a commonly used tool for checking randomness in a data set. This randomness is ascertained by computing autocorrelation for data values at varying time lags. It shows the properties of a type of data known as a time series. These plots are available in most general-purpose statistical software programs. It can be plotted using the pandas.plotting.autocorrelation_plot().

Syntax: pandas.plotting.autocorrelation_plot(series, ax=None, **kwargs)

Parameters:

  • series: This parameter is the Time series to be used to plot.
  • ax: This parameter is a matplotlib axes object. Its default value is None.

Returns: This function returns an object of class matplotlip.axis.Axes



Example 1:

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# importing various package
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
  
# making Time series
spacing = np.linspace(-5 * np.pi, 5 * np.pi, num=100)
s = pd.Series(0.7 * np.random.rand(100) + 0.3 * np.sin(spacing))
  
# Creating Autocorrelation plot
x = pd.plotting.autocorrelation_plot(s)
  
# ploting the Curve
x.plot()
  
# Display
plt.show()

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Output:

Example 2:

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# importing various package
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
  
# making Time series
data = np.array([12.0, 24.0, 7., 20.0
                 7.0, 22.0, 18.0,22.0
                 6.0, 7.0, 20.0, 13.0
                 8.0, 5.0, 8])
  
# Creating Autocorrelation plot
x = pd.plotting.autocorrelation_plot(data)
  
# ploting the Curve
x.plot()
  
# Display
plt.show()

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Output:


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