pandas.plotting.lag_plot — pandas 3.0.0.dev0+2100.gf496acffcc documentation (original) (raw)
pandas.plotting.lag_plot(series, lag=1, ax=None, **kwds)[source]#
Lag plot for time series.
A lag plot is a scatter plot of a time series against a lag of itself. It helps in visualizing the temporal dependence between observations by plotting the values at time t on the x-axis and the values at time t + lag on the y-axis.
Parameters:
seriesSeries
The time series to visualize.
lagint, default 1
Lag length of the scatter plot.
axMatplotlib axis object, optional
The matplotlib axis object to use.
**kwds
Matplotlib scatter method keyword arguments.
Returns:
matplotlib.axes.Axes
The matplotlib Axes object containing the lag plot.
See also
plotting.autocorrelation_plot
Autocorrelation plot for time series.
A scatter plot of y vs. x with varying marker size and/or color in Matplotlib.
Examples
Lag plots are most commonly used to look for patterns in time series data.
Given the following time series
np.random.seed(5) x = np.cumsum(np.random.normal(loc=1, scale=5, size=50)) s = pd.Series(x) s.plot()
A lag plot with lag=1 returns
pd.plotting.lag_plot(s, lag=1) <Axes: xlabel='y(t)', ylabel='y(t + 1)'>
