pandas.plotting.lag_plot

pandas.plotting.lag_plot(series, lag=1, ax=None, **kwds)

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.

matplotlib.pyplot.scatter

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)'>