docs: add code samples for Series.corr

bigframes/series.py

@@ -735,26 +735,6 @@ def round(self, decimals=0) -> "Series":
         return self._apply_binary_op(decimals, ops.round_op)
 
     def corr(self, other: Series, method="pearson", min_periods=None) -> float:
-        """
-        Compute the correlation with the other Series.  Non-number values are ignored in the
-        computation.
-
-        Uses the "Pearson" method of correlation.  Numbers are converted to float before
-        calculation, so the result may be unstable.
-
-        Args:
-            other (Series):
-                The series with which this is to be correlated.
-            method (string, default "pearson"):
-                Correlation method to use - currently only "pearson" is supported.
-            min_periods (int, default None):
-                The minimum number of observations needed to return a result.  Non-default values
-                are not yet supported, so a result will be returned for at least two observations.
-
-        Returns:
-            float;  Will return NaN if there are fewer than two numeric pairs, either series has a
-                variance or covariance of zero, or any input value is infinite.
-        """
         # TODO(kemppeterson): Validate early that both are numeric
         # TODO(kemppeterson): Handle partially-numeric columns
         if method != "pearson":

third_party/bigframes_vendored/pandas/core/series.py

@@ -809,6 +809,21 @@ def corr(self, other, method="pearson", min_periods=None) -> float:
         Uses the "Pearson" method of correlation.  Numbers are converted to float before
         calculation, so the result may be unstable.
 
+        **Examples:**
+
+            >>> import bigframes.pandas as bpd
+            >>> bpd.options.display.progress_bar = None
+
+            >>> s1 = bpd.Series([.2, .0, .6, .2])
+            >>> s2 = bpd.Series([.3, .6, .0, .1])
+            >>> s1.corr(s2)
+            -0.8510644963469901
+
+            >>> s1 = bpd.Series([1, 2, 3], index=[0, 1, 2])
+            >>> s2 = bpd.Series([1, 2, 3], index=[2, 1, 0])
+            >>> s1.corr(s2)
+            -1.0
+
         Args:
             other (Series):
                 The series with which this is to be correlated.