feat: Add Series.autocorr

bigframes/series.py

@@ -772,6 +772,9 @@ def corr(self, other: Series, method="pearson", min_periods=None) -> float:
             )
         return self._apply_binary_aggregation(other, agg_ops.CorrOp())
 
+    def autocorr(self, lag: int = 1) -> float:
+        return self.corr(self.shift(lag))
+
     def cov(self, other: Series) -> float:
         return self._apply_binary_aggregation(other, agg_ops.CovOp())
 

tests/system/small/test_series.py

@@ -718,6 +718,14 @@ def test_series_corr(scalars_dfs):
     assert math.isclose(pd_result, bf_result)
 
 
+@skip_legacy_pandas
+def test_series_autocorr(scalars_dfs):
+    scalars_df, scalars_pandas_df = scalars_dfs
+    bf_result = scalars_df["float64_col"].autocorr(2)
+    pd_result = scalars_pandas_df["float64_col"].autocorr(2)
+    assert math.isclose(pd_result, bf_result)
+
+
 def test_series_cov(scalars_dfs):
     scalars_df, scalars_pandas_df = scalars_dfs
     bf_result = scalars_df["int64_too"].cov(scalars_df["int64_too"])

third_party/bigframes_vendored/pandas/core/series.py

@@ -819,6 +819,38 @@ def corr(self, other, method="pearson", min_periods=None) -> float:
         """
         raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
 
+    def autocorr(self, lag: int = 1) -> float:
+        """
+        Compute the lag-N autocorrelation.
+
+        This method computes the Pearson correlation between
+        the Series and its shifted self.
+
+        **Examples:**
+            >>> import bigframes.pandas as bpd
+            >>> bpd.options.display.progress_bar = None
+
+            >>> s = bpd.Series([0.25, 0.5, 0.2, -0.05])
+            >>> s.autocorr()  # doctest: +ELLIPSIS
+            0.10355...
+            >>> s.autocorr(lag=2)
+            -1.0
+
+            If the Pearson correlation is not well defined, then 'NaN' is returned.
+
+            >>> s = bpd.Series([1, 0, 0, 0])
+            >>> s.autocorr()
+            nan
+
+        Args:
+            lag (int, default 1):
+                Number of lags to apply before performing autocorrelation.
+
+        Returns:
+            float: The Pearson correlation between self and self.shift(lag).
+        """
+        raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
+
     def cov(
         self,
         other,