Update groupby.py

Author: ericcht

pandas/core/groupby/groupby.py

@@ -3231,80 +3231,81 @@ def max(
     def first(
         self, numeric_only: bool = False, min_count: int = -1, skipna: bool = True
     ) -> NDFrameT:
-       """
-Compute the first non-null entry of each column within each group.
+        """
+        Compute the first non-null entry of each column within each group.
 
-This method operates column-wise, returning the first non-null value
-in each column for every group. Unlike `nth(0)`, which returns the
-first row (even if it contains nulls), `first()` skips over NA/null
-values in each column independently.
+        This method operates column-wise, returning the first non-null value
+        in each column for every group. Unlike `nth(0)`, which returns the
+        first row (even if it contains nulls), `first()` skips over NA/null
+        values in each column independently.
 
-Parameters
-----------
-numeric_only : bool, default False
-    Include only float, int, boolean columns.
-min_count : int, default -1
-    The required number of valid values to perform the operation. If fewer
-    than ``min_count`` valid values are present the result will be NA.
-skipna : bool, default True
-    Exclude NA/null values. If an entire group is NA, the result will be NA.
+        Parameters
+        ----------
+        numeric_only : bool, default False
+            Include only float, int, boolean columns.
+        min_count : int, default -1
+            The required number of valid values to perform the operation. If fewer
+            than ``min_count`` valid values are present the result will be NA.
+        skipna : bool, default True
+            Exclude NA/null values. If an entire group is NA, the result will be NA.
 
-    .. versionadded:: 2.2.1
+            .. versionadded:: 2.2.1
 
-Returns
--------
-Series or DataFrame
-    First non-null values within each group, selected independently per column.
+        Returns
+        -------
+        Series or DataFrame
+            First non-null values within each group, selected independently per column.
 
-See Also
---------
-DataFrame.groupby : Group DataFrame using a mapper or by a Series of columns.
-Series.groupby : Group Series using a mapper or by a Series of values.
-GroupBy.nth : Take the nth row from each group.
-GroupBy.head : Return the first `n` rows from each group.
-GroupBy.last : Compute the last non-null entry of each column.
+        See Also
+        --------
+        DataFrame.groupby : Group DataFrame using a mapper or by a Series of columns.
+        Series.groupby : Group Series using a mapper or by a Series of values.
+        GroupBy.nth : Take the nth row from each group.
+        GroupBy.head : Return the first `n` rows from each group.
+        GroupBy.last : Compute the last non-null entry of each column.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame(
+        ...     dict(
+        ...         A=[1, 1, 3],
+        ...         B=[None, 5, 6],
+        ...         C=[1, 2, 3],
+        ...         D=["3/11/2000", "3/12/2000", "3/13/2000"],
+        ...     )
+        ... )
+        >>> df["D"] = pd.to_datetime(df["D"])
+
+        >>> df.groupby("A").first()
+            B  C          D
+        A
+        1  5.0  1 2000-03-11
+        3  6.0  3 2000-03-13
+
+        >>> df.groupby("A").nth(0)
+            B  C          D
+        A
+        1  NaN  1 2000-03-11
+        3  6.0  3 2000-03-13
+
+        >>> df.groupby("A").head(1)
+        A    B  C          D
+        0  1  NaN  1 2000-03-11
+        2  3  6.0  3 2000-03-13
+
+        >>> df.groupby("A").first(min_count=2)
+            B    C          D
+        A
+        1  NaN  1.0 2000-03-11
+        3  NaN  NaN        NaT
+
+        >>> df.groupby("A").first(numeric_only=True)
+            B  C
+        A
+        1  5.0  1
+        3  6.0  3
+        """
 
-Examples
---------
->>> df = pd.DataFrame(
-...     dict(
-...         A=[1, 1, 3],
-...         B=[None, 5, 6],
-...         C=[1, 2, 3],
-...         D=["3/11/2000", "3/12/2000", "3/13/2000"],
-...     )
-... )
->>> df["D"] = pd.to_datetime(df["D"])
-
->>> df.groupby("A").first()
-     B  C          D
-A
-1  5.0  1 2000-03-11
-3  6.0  3 2000-03-13
-
->>> df.groupby("A").nth(0)
-     B  C          D
-A
-1  NaN  1 2000-03-11
-3  6.0  3 2000-03-13
-
->>> df.groupby("A").head(1)
-   A    B  C          D
-0  1  NaN  1 2000-03-11
-2  3  6.0  3 2000-03-13
-
->>> df.groupby("A").first(min_count=2)
-     B    C          D
-A
-1  NaN  1.0 2000-03-11
-3  NaN  NaN        NaT
-
->>> df.groupby("A").first(numeric_only=True)
-     B  C
-A
-1  5.0  1
-3  6.0  3
-"""
 
         def first_compat(obj: NDFrameT):
             def first(x: Series):