Merge branch 'main' into np-doc-df-std

Author: tuhinsharma121

ci/code_checks.sh

@@ -78,18 +78,12 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.DataFrame.sum RT03" \
         -i "pandas.DataFrame.swaplevel SA01" \
         -i "pandas.DataFrame.to_markdown SA01" \
-        -i "pandas.DataFrame.var PR01,RT03,SA01" \
         -i "pandas.Grouper PR02" \
         -i "pandas.Index PR07" \
         -i "pandas.Index.join PR07,RT03,SA01" \
-        -i "pandas.Index.names GL08" \
         -i "pandas.Index.ravel PR01,RT03" \
         -i "pandas.Index.str PR01,SA01" \
         -i "pandas.Interval PR02" \
-        -i "pandas.Interval.closed SA01" \
-        -i "pandas.Interval.left SA01" \
-        -i "pandas.Interval.mid SA01" \
-        -i "pandas.Interval.right SA01" \
         -i "pandas.IntervalIndex.closed SA01" \
         -i "pandas.IntervalIndex.contains RT03" \
         -i "pandas.IntervalIndex.get_loc PR07,RT03,SA01" \
@@ -206,9 +200,7 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.Series.list.flatten SA01" \
         -i "pandas.Series.list.len SA01" \
         -i "pandas.Series.lt PR07,SA01" \
-        -i "pandas.Series.max RT03" \
         -i "pandas.Series.mean RT03,SA01" \
-        -i "pandas.Series.median RT03,SA01" \
         -i "pandas.Series.min RT03" \
         -i "pandas.Series.mod PR07" \
         -i "pandas.Series.mode SA01" \
@@ -302,7 +294,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.TimedeltaIndex.nanoseconds SA01" \
         -i "pandas.TimedeltaIndex.seconds SA01" \
         -i "pandas.TimedeltaIndex.to_pytimedelta RT03,SA01" \
-        -i "pandas.Timestamp PR07,SA01" \
         -i "pandas.Timestamp.as_unit SA01" \
         -i "pandas.Timestamp.asm8 SA01" \
         -i "pandas.Timestamp.astimezone SA01" \
@@ -321,9 +312,9 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.Timestamp.isocalendar SA01" \
         -i "pandas.Timestamp.isoformat SA01" \
         -i "pandas.Timestamp.isoweekday SA01" \
-        -i "pandas.Timestamp.max PR02,PR07,SA01" \
+        -i "pandas.Timestamp.max PR02" \
         -i "pandas.Timestamp.microsecond GL08" \
-        -i "pandas.Timestamp.min PR02,PR07,SA01" \
+        -i "pandas.Timestamp.min PR02" \
         -i "pandas.Timestamp.minute GL08" \
         -i "pandas.Timestamp.month GL08" \
         -i "pandas.Timestamp.month_name SA01" \

pandas/_libs/interval.pyx

@@ -167,6 +167,12 @@ cdef class IntervalMixin:
         """
         Return the midpoint of the Interval.
 
+        See Also
+        --------
+        Interval.left : Return the left bound for the interval.
+        Interval.right : Return the right bound for the interval.
+        Interval.length : Return the length of the interval.
+
         Examples
         --------
         >>> iv = pd.Interval(0, 5)
@@ -377,6 +383,12 @@ cdef class Interval(IntervalMixin):
     """
     Left bound for the interval.
 
+    See Also
+    --------
+    Interval.right : Return the right bound for the interval.
+    numpy.ndarray.left : A similar method in numpy for obtaining
+        the left endpoint(s) of intervals.
+
     Examples
     --------
     >>> interval = pd.Interval(left=1, right=2, closed='left')
@@ -390,6 +402,12 @@ cdef class Interval(IntervalMixin):
     """
     Right bound for the interval.
 
+    See Also
+    --------
+    Interval.left : Return the left bound for the interval.
+    numpy.ndarray.right : A similar method in numpy for obtaining
+        the right endpoint(s) of intervals.
+
     Examples
     --------
     >>> interval = pd.Interval(left=1, right=2, closed='left')
@@ -405,6 +423,13 @@ cdef class Interval(IntervalMixin):
 
     Either ``left``, ``right``, ``both`` or ``neither``.
 
+    See Also
+    --------
+    Interval.closed_left : Check if the interval is closed on the left side.
+    Interval.closed_right : Check if the interval is closed on the right side.
+    Interval.open_left : Check if the interval is open on the left side.
+    Interval.open_right : Check if the interval is open on the right side.
+
     Examples
     --------
     >>> interval = pd.Interval(left=1, right=2, closed='left')

pandas/_libs/tslibs/timestamps.pyx

@@ -1297,10 +1297,24 @@ class Timestamp(_Timestamp):
     ----------
     ts_input : datetime-like, str, int, float
         Value to be converted to Timestamp.
-    year, month, day : int
-    hour, minute, second, microsecond : int, optional, default 0
+    year : int
+        Value of year.
+    month : int
+        Value of month.
+    day : int
+        Value of day.
+    hour : int, optional, default 0
+        Value of hour.
+    minute : int, optional, default 0
+        Value of minute.
+    second : int, optional, default 0
+        Value of second.
+    microsecond : int, optional, default 0
+        Value of microsecond.
     tzinfo : datetime.tzinfo, optional, default None
+        Timezone info.
     nanosecond : int, optional, default 0
+        Value of nanosecond.
     tz : str, pytz.timezone, dateutil.tz.tzfile or None
         Time zone for time which Timestamp will have.
     unit : str
@@ -1316,6 +1330,11 @@ class Timestamp(_Timestamp):
         datetime-like corresponds  to the first (0) or the second time (1)
         the wall clock hits the ambiguous time.
 
+    See Also
+    --------
+    Timedelta : Represents a duration, the difference between two dates or times.
+    datetime.datetime : Python datetime.datetime object.
+
     Notes
     -----
     There are essentially three calling conventions for the constructor. The

pandas/core/frame.py

@@ -12065,7 +12065,6 @@ def var(
     ) -> Series | Any: ...
 
     @deprecate_nonkeyword_arguments(version="3.0", allowed_args=["self"], name="var")
-    @doc(make_doc("var", ndim=2))
     def var(
         self,
         axis: Axis | None = 0,
@@ -12074,6 +12073,75 @@ def var(
         numeric_only: bool = False,
         **kwargs,
     ) -> Series | Any:
+        """
+        Return unbiased variance over requested axis.
+
+        Normalized by N-1 by default. This can be changed using the ddof argument.
+
+        Parameters
+        ----------
+        axis : {index (0), columns (1)}
+            For `Series` this parameter is unused and defaults to 0.
+
+            .. warning::
+
+                The behavior of DataFrame.var with ``axis=None`` is deprecated,
+                in a future version this will reduce over both axes and return a scalar
+                To retain the old behavior, pass axis=0 (or do not pass axis).
+
+        skipna : bool, default True
+            Exclude NA/null values. If an entire row/column is NA, the result
+            will be NA.
+        ddof : int, default 1
+            Delta Degrees of Freedom. The divisor used in calculations is N - ddof,
+            where N represents the number of elements.
+        numeric_only : bool, default False
+            Include only float, int, boolean columns. Not implemented for Series.
+        **kwargs :
+            Additional keywords passed.
+
+        Returns
+        -------
+        Series or scalaer
+            Unbiased variance over requested axis.
+
+        See Also
+        --------
+        numpy.var : Equivalent function in NumPy.
+        Series.var : Return unbiased variance over Series values.
+        Series.std : Return standard deviation over Series values.
+        DataFrame.std : Return standard deviation of the values over
+            the requested axis.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame(
+        ...     {
+        ...         "person_id": [0, 1, 2, 3],
+        ...         "age": [21, 25, 62, 43],
+        ...         "height": [1.61, 1.87, 1.49, 2.01],
+        ...     }
+        ... ).set_index("person_id")
+        >>> df
+                   age  height
+        person_id
+        0           21    1.61
+        1           25    1.87
+        2           62    1.49
+        3           43    2.01
+
+        >>> df.var()
+        age       352.916667
+        height      0.056367
+        dtype: float64
+
+        Alternatively, ``ddof=0`` can be set to normalize by N instead of N-1:
+
+        >>> df.var(ddof=0)
+        age       264.687500
+        height      0.042275
+        dtype: float64
+        """
         result = super().var(
             axis=axis, skipna=skipna, ddof=ddof, numeric_only=numeric_only, **kwargs
         )

pandas/core/indexes/base.py

@@ -1799,6 +1799,23 @@ def _get_default_index_names(
         return names
 
     def _get_names(self) -> FrozenList:
+        """
+        Get names on index.
+
+        See Also
+        --------
+        Index.name : Return Index or MultiIndex name.
+
+        Examples
+        --------
+        >>> idx = pd.Index([1, 2, 3], name="x")
+        >>> idx.names
+        FrozenList(['x'])
+
+        >>> idx = s = pd.Index([1, 2, 3], name=("x", "y"))
+        >>> idx.names
+        FrozenList([('x', 'y')])
+        """
         return FrozenList((self.name,))
 
     def _set_names(self, values, *, level=None) -> None: