Python Pandas - Plotting the Autocorrelation Plot

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 matplotlib.axis.Axes

Example 1:

# 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)

# plotting the Curve
x.plot()

# Display
plt.show() 

Output:

Example 2:

# 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)

# plotting the Curve
x.plot()

# Display
plt.show() 

Output: