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Enforce Numpy Docstring Validation for pandas.Series.mod (#58619)
* DOC: add PR07 for pandas.Series.mod * DOC: remove PR07 for pandas.Series.mod
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ci/code_checks.sh

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@@ -193,7 +193,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
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-i "pandas.Series.list.flatten SA01" \
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-i "pandas.Series.list.len SA01" \
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-i "pandas.Series.lt PR07,SA01" \
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-i "pandas.Series.mod PR07" \
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-i "pandas.Series.mode SA01" \
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-i "pandas.Series.ne PR07,SA01" \
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-i "pandas.Series.pad PR01,SA01" \

pandas/core/series.py

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@@ -6084,8 +6084,64 @@ def rfloordiv(self, other, level=None, fill_value=None, axis: Axis = 0) -> Serie
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other, roperator.rfloordiv, level=level, fill_value=fill_value, axis=axis
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)
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@Appender(ops.make_flex_doc("mod", "series"))
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def mod(self, other, level=None, fill_value=None, axis: Axis = 0) -> Series:
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"""
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Return Modulo of series and other, element-wise (binary operator `mod`).
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Equivalent to ``series % other``, but with support to substitute a
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fill_value for missing data in either one of the inputs.
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Parameters
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----------
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other : Series or scalar value
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Series with which to compute modulo.
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level : int or name
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Broadcast across a level, matching Index values on the
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passed MultiIndex level.
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fill_value : None or float value, default None (NaN)
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Fill existing missing (NaN) values, and any new element needed for
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successful Series alignment, with this value before computation.
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If data in both corresponding Series locations is missing
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the result of filling (at that location) will be missing.
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axis : {0 or 'index'}
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Unused. Parameter needed for compatibility with DataFrame.
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Returns
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-------
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Series
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The result of the operation.
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See Also
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--------
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Series.rmod : Reverse of the Modulo operator, see
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`Python documentation
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<https://docs.python.org/3/reference/datamodel.html#emulating-numeric-types>`_
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for more details.
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Examples
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--------
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>>> a = pd.Series([1, 1, 1, np.nan], index=["a", "b", "c", "d"])
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>>> a
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a 1.0
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b 1.0
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c 1.0
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d NaN
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dtype: float64
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>>> b = pd.Series([1, np.nan, 1, np.nan], index=["a", "b", "d", "e"])
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>>> b
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a 1.0
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b NaN
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d 1.0
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e NaN
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dtype: float64
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>>> a.mod(b, fill_value=0)
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a 0.0
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b NaN
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c NaN
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d 0.0
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e NaN
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dtype: float64
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"""
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return self._flex_method(
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other, operator.mod, level=level, fill_value=fill_value, axis=axis
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)

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