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:
Python3
# importing various packageimport pandas as pdimport numpy as npimport matplotlib.pyplot as plt# making Time seriesspacing = 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 plotx = pd.plotting.autocorrelation_plot(s)# plotting the Curvex.plot()# Displayplt.show() |
Output:
Example 2:
Python3
# importing various packageimport pandas as pdimport numpy as npimport matplotlib.pyplot as plt# making Time seriesdata = 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 plotx = pd.plotting.autocorrelation_plot(data)# plotting the Curvex.plot()# Displayplt.show() |
Output:
