A special kind of scatter plot is used to draw two variables X & Y with lag. One set of observations X is plotted in time series parallel to the second Y.
pandas.plotting.lag_plot() functionThe lag_plot() function is used in pandas to draw a lag plot. It takes a time series, a lag, a matplotlib axis object, and some additional keyword argument values. This method returns a matplotlib.axis.Axes instance.
pandas.plotting.lag_plot(series, lag=1, ax=None, **kwds)
It takes the following argument values.
series: This is an instance of a time series.lag: This is an integer value, Default=1, which shows a lag between every point of the scatter plot.ax: This is a matplotlib axis object, Default=None.**kwds: These are the additional arguments for the scatter plot.It returns an instance of the matplotlib.axis.Axes module.
In this code snippet, we create use the pandas.plotting.lag_plot() function to create a lag plot.
# import python libraries in programimport pandas as pdimport numpy as npimport matplotlib.pyplot as plt# create a ndarray of cummulative sum of numbersx = np.cumsum(np.random.normal(loc=1, scale=5, size=25))# create a series of above created random valuess = pd.Series(x)# invoke lag_plot() function to draw a scaller plotpd.plotting.lag_plot(s, lag=3)# save output image as pngplt.savefig("output/lagplot.png")
pd.Series(x) function to create a series of the above-created cumulative values.s as series and lag=3.