Skip to main content
`Ctrl`+`K`

# pandas.plotting.lag_plot

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

  Lag plot for time series.

  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

  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)'>