fixed plot type description in extended summary

Author: tuhinsharma121

pandas/plotting/_misc.py

@@ -635,13 +635,12 @@ def autocorrelation_plot(series: Series, ax: Axes | None = None, **kwargs) -> Ax
 
     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. The autocorrelations are computed for increasing
-    lags and plotted as vertical bars. Horizontal lines at 95% and 99% confidence
-    intervals are shown to assist in identifying statistically significant lags. The
-    dashed line corresponds to the 99% confidence band, which can help detect
-    whether observed autocorrelations deviate significantly from what would
-    be expected under white noise. This plot is useful in time series analysis for
-    identifying potential model structures, such as autoregressive components.
+    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
     ----------