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# pandas.plotting.autocorrelation_plot

pandas.plotting.autocorrelation_plot(series, ax=None, **kwargs)

  Autocorrelation plot for time series.

  This method generates an autocorrelation plot for a given time series, which helps to identify any periodic structure or correlation within the data across various lags. It shows the correlation of a time series with a delayed copy of itself as a function of delay. Autocorrelation plots are useful for checking randomness in a data set. If the data are random, the autocorrelations should be near zero for any and all time-lag separations. If the data are not random, then one or more of the autocorrelations will be significantly non-zero.

  Parameters:
    series Series

      The time series to visualize.

    ax Matplotlib axis object, optional

      The matplotlib axis object to use.

    **kwargs

      Options to pass to matplotlib plotting method.

  Returns:
    matplotlib.axes.Axes

      The matplotlib axes containing the autocorrelation plot.

  See also

  `Series.autocorr`

    Compute the lag-N autocorrelation for a Series.

  `plotting.lag_plot`

    Lag plot for time series.

  Examples

  The horizontal lines in the plot correspond to 95% and 99% confidence bands.

  The dashed line is 99% confidence band.

      >>> spacing = np.linspace(-9 * np.pi, 9 * np.pi, num=1000)
      >>> s = pd.Series(0.7 * np.random.rand(1000) + 0.3 * np.sin(spacing))
      >>> pd.plotting.autocorrelation_plot(s)