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DOC: Enforce Numpy Docstring Validation for pandas.Series.mul #58580

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May 5, 2024
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1 change: 0 additions & 1 deletion ci/code_checks.sh
Original file line number Diff line number Diff line change
Expand Up @@ -205,7 +205,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
-i "pandas.Series.min RT03" \
-i "pandas.Series.mod PR07" \
-i "pandas.Series.mode SA01" \
-i "pandas.Series.mul PR07" \
-i "pandas.Series.ne PR07,SA01" \
-i "pandas.Series.pad PR01,SA01" \
-i "pandas.Series.plot PR02,SA01" \
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64 changes: 63 additions & 1 deletion pandas/core/series.py
Original file line number Diff line number Diff line change
Expand Up @@ -5973,14 +5973,76 @@ def rsub(self, other, level=None, fill_value=None, axis: Axis = 0) -> Series:
other, roperator.rsub, level=level, fill_value=fill_value, axis=axis
)

@Appender(ops.make_flex_doc("mul", "series"))
def mul(
self,
other,
level: Level | None = None,
fill_value: float | None = None,
axis: Axis = 0,
) -> Series:
"""
Return Multiplication of series and other, element-wise (binary operator `mul`).

Equivalent to ``series * other``, but with support to substitute
a fill_value for missing data in either one of the inputs.

Parameters
----------
other : Series or scalar value
With which to compute the multiplication.
level : int or name
Broadcast across a level, matching Index values on the
passed MultiIndex level.
fill_value : None or float value, default None (NaN)
Fill existing missing (NaN) values, and any new element needed for
successful Series alignment, with this value before computation.
If data in both corresponding Series locations is missing
the result of filling (at that location) will be missing.
axis : {0 or 'index'}
Unused. Parameter needed for compatibility with DataFrame.

Returns
-------
Series
The result of the operation.

See Also
--------
Series.rmul : Reverse of the Multiplication operator, see
`Python documentation
<https://docs.python.org/3/reference/datamodel.html#emulating-numeric-types>`_
for more details.

Examples
--------
>>> a = pd.Series([1, 1, 1, np.nan], index=["a", "b", "c", "d"])
>>> a
a 1.0
b 1.0
c 1.0
d NaN
dtype: float64
>>> b = pd.Series([1, np.nan, 1, np.nan], index=["a", "b", "d", "e"])
>>> b
a 1.0
b NaN
d 1.0
e NaN
dtype: float64
>>> a.multiply(b, fill_value=0)
a 1.0
b 0.0
c 0.0
d 0.0
e NaN
dtype: float64
>>> a.mul(5, fill_value=0)
a 5.0
b 5.0
c 5.0
d 0.0
dtype: float64
"""
return self._flex_method(
other, operator.mul, level=level, fill_value=fill_value, axis=axis
)
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