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DOC: Enforce Numpy Docstring Validation for pandas.Series.mod #58619

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May 7, 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 @@ -193,7 +193,6 @@ 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.mod PR07" \
-i "pandas.Series.mode SA01" \
-i "pandas.Series.ne PR07,SA01" \
-i "pandas.Series.pad PR01,SA01" \
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58 changes: 57 additions & 1 deletion pandas/core/series.py
Original file line number Diff line number Diff line change
Expand Up @@ -6084,8 +6084,64 @@ def rfloordiv(self, other, level=None, fill_value=None, axis: Axis = 0) -> Serie
other, roperator.rfloordiv, level=level, fill_value=fill_value, axis=axis
)

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

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
Series with which to compute modulo.
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.rmod : Reverse of the Modulo 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.mod(b, fill_value=0)
a 0.0
b NaN
c NaN
d 0.0
e NaN
dtype: float64
"""
return self._flex_method(
other, operator.mod, level=level, fill_value=fill_value, axis=axis
)
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