Repository: pandas-dev/pandas (python)

Issue:
Allow method keyword for DataFrame.corrwith()
Now it is not possible to choose method when using corrwith() function, which defaults to pearson (I assume). Would be great to see the same methods that are allowed in corr().


Pull request description:
ENH: Allow parameter  min_periods in DataFrame.corrwith()
- [x] closes #9490
- [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [x] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.


Patch:
diff --git a/ci/code_checks.sh b/ci/code_checks.sh
index 161047197f..f49bfb1581 100755
--- a/ci/code_checks.sh
+++ b/ci/code_checks.sh
@@ -70,10 +70,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         --format=actions \
         -i ES01 `# For now it is ok if docstrings are missing the extended summary` \
         -i "pandas.Series.dt PR01" `# Accessors are implemented as classes, but we do not document the Parameters section` \
-        -i "pandas.DataFrame.__dataframe__ SA01" \
-        -i "pandas.DataFrame.at_time PR01" \
-        -i "pandas.DataFrame.kurt RT03,SA01" \
-        -i "pandas.DataFrame.kurtosis RT03,SA01" \
         -i "pandas.DataFrame.max RT03" \
         -i "pandas.DataFrame.mean RT03,SA01" \
         -i "pandas.DataFrame.median RT03,SA01" \
@@ -91,8 +87,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.DataFrame.var PR01,RT03,SA01" \
         -i "pandas.Grouper PR02" \
         -i "pandas.Index PR07" \
-        -i "pandas.Index.append PR07,RT03,SA01" \
-        -i "pandas.Index.difference PR07,RT03,SA01" \
         -i "pandas.Index.get_indexer PR07,SA01" \
         -i "pandas.Index.get_indexer_for PR01,SA01" \
         -i "pandas.Index.get_indexer_non_unique PR07,SA01" \
@@ -101,10 +95,7 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.Index.names GL08" \
         -i "pandas.Index.putmask PR01,RT03" \
         -i "pandas.Index.ravel PR01,RT03" \
-        -i "pandas.Index.slice_indexer PR07,RT03,SA01" \
         -i "pandas.Index.str PR01,SA01" \
-        -i "pandas.Index.take PR01,PR07" \
-        -i "pandas.Index.view GL08" \
         -i "pandas.Int16Dtype SA01" \
         -i "pandas.Int32Dtype SA01" \
         -i "pandas.Int64Dtype SA01" \
@@ -193,7 +184,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.Series SA01" \
         -i "pandas.Series.__iter__ RT03,SA01" \
         -i "pandas.Series.add PR07" \
-        -i "pandas.Series.at_time PR01" \
         -i "pandas.Series.backfill PR01,SA01" \
         -i "pandas.Series.case_when RT03" \
         -i "pandas.Series.cat PR07,SA01" \
diff --git a/doc/source/whatsnew/v3.0.0.rst b/doc/source/whatsnew/v3.0.0.rst
index afe63b6785..1fc2f1041e 100644
--- a/doc/source/whatsnew/v3.0.0.rst
+++ b/doc/source/whatsnew/v3.0.0.rst
@@ -39,6 +39,7 @@ Other enhancements
 - Users can globally disable any ``PerformanceWarning`` by setting the option ``mode.performance_warnings`` to ``False`` (:issue:`56920`)
 - :meth:`Styler.format_index_names` can now be used to format the index and column names (:issue:`48936` and :issue:`47489`)
 - :class:`.errors.DtypeWarning` improved to include column names when mixed data types are detected (:issue:`58174`)
+- :meth:`DataFrame.corrwith` now accepts ``min_periods`` as optional arguments, as in :meth:`DataFrame.corr` and :meth:`Series.corr` (:issue:`9490`)
 - :meth:`DataFrame.cummin`, :meth:`DataFrame.cummax`, :meth:`DataFrame.cumprod` and :meth:`DataFrame.cumsum` methods now have a ``numeric_only`` parameter (:issue:`53072`)
 - :meth:`DataFrame.fillna` and :meth:`Series.fillna` can now accept ``value=None``; for non-object dtype the corresponding NA value will be used (:issue:`57723`)
 - :meth:`Series.cummin` and :meth:`Series.cummax` now supports :class:`CategoricalDtype` (:issue:`52335`)
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index 9fbbc2c08e..3d2a609346 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -931,6 +931,11 @@ class DataFrame(NDFrame, OpsMixin):
         DataFrame interchange object
             The object which consuming library can use to ingress the dataframe.
 
+        See Also
+        --------
+        DataFrame.from_records : Constructor from tuples, also record arrays.
+        DataFrame.from_dict : From dicts of Series, arrays, or dicts.
+
         Notes
         -----
         Details on the interchange protocol:
@@ -11127,6 +11132,7 @@ class DataFrame(NDFrame, OpsMixin):
         drop: bool = False,
         method: CorrelationMethod = "pearson",
         numeric_only: bool = False,
+        min_periods: int | None = None,
     ) -> Series:
         """
         Compute pairwise correlation.
@@ -11157,6 +11163,9 @@ class DataFrame(NDFrame, OpsMixin):
         numeric_only : bool, default False
             Include only `float`, `int` or `boolean` data.
 
+        min_periods : int, optional
+            Minimum number of observations needed to have a valid result.
+
             .. versionadded:: 1.5.0
 
             .. versionchanged:: 2.0.0
@@ -11200,7 +11209,10 @@ class DataFrame(NDFrame, OpsMixin):
         this = self._get_numeric_data() if numeric_only else self
 
         if isinstance(other, Series):
-            return this.apply(lambda x: other.corr(x, method=method), axis=axis)
+            return this.apply(
+                lambda x: other.corr(x, method=method, min_periods=min_periods),
+                axis=axis,
+            )
 
         if numeric_only:
             other = other._get_numeric_data()
@@ -12064,7 +12076,6 @@ class DataFrame(NDFrame, OpsMixin):
     ) -> Series | Any: ...
 
     @deprecate_nonkeyword_arguments(version="3.0", allowed_args=["self"], name="kurt")
-    @doc(make_doc("kurt", ndim=2))
     def kurt(
         self,
         axis: Axis | None = 0,
@@ -12072,6 +12083,85 @@ class DataFrame(NDFrame, OpsMixin):
         numeric_only: bool = False,
         **kwargs,
     ) -> Series | Any:
+        """
+        Return unbiased kurtosis over requested axis.
+
+        Kurtosis obtained using Fisher's definition of
+        kurtosis (kurtosis of normal == 0.0). Normalized by N-1.
+
+        Parameters
+        ----------
+        axis : {index (0), columns (1)}
+            Axis for the function to be applied on.
+            For `Series` this parameter is unused and defaults to 0.
+
+            For DataFrames, specifying ``axis=None`` will apply the aggregation
+            across both axes.
+
+            .. versionadded:: 2.0.0
+
+        skipna : bool, default True
+            Exclude NA/null values when computing the result.
+        numeric_only : bool, default False
+            Include only float, int, boolean columns.
+
+        **kwargs
+            Additional keyword arguments to be passed to the function.
+
+        Returns
+        -------
+        Series or scalar
+            Unbiased kurtosis over requested axis.
+
+        See Also
+        --------
+        Dataframe.kurtosis : Returns unbiased kurtosis over requested axis.
+
+        Examples
+        --------
+        >>> s = pd.Series([1, 2, 2, 3], index=["cat", "dog", "dog", "mouse"])
+        >>> s
+        cat    1
+        dog    2
+        dog    2
+        mouse  3
+        dtype: int64
+        >>> s.kurt()
+        1.5
+
+        With a DataFrame
+
+        >>> df = pd.DataFrame(
+        ...     {"a": [1, 2, 2, 3], "b": [3, 4, 4, 4]},
+        ...     index=["cat", "dog", "dog", "mouse"],
+        ... )
+        >>> df
+               a   b
+          cat  1   3
+          dog  2   4
+          dog  2   4
+        mouse  3   4
+        >>> df.kurt()
+        a   1.5
+        b   4.0
+        dtype: float64
+
+        With axis=None
+
+        >>> df.kurt(axis=None).round(6)
+        -0.988693
+
+        Using axis=1
+
+        >>> df = pd.DataFrame(
+        ...     {"a": [1, 2], "b": [3, 4], "c": [3, 4], "d": [1, 2]},
+        ...     index=["cat", "dog"],
+        ... )
+        >>> df.kurt(axis=1)
+        cat   -6.0
+        dog   -6.0
+        dtype: float64
+        """
         result = super().kurt(
             axis=axis, skipna=skipna, numeric_only=numeric_only, **kwargs
         )
diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index 121f49cb7d..24727bb9d8 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -8532,6 +8532,8 @@ class NDFrame(PandasObject, indexing.IndexingMixin):
         ----------
         time : datetime.time or str
             The values to select.
+        asof : bool, default False
+            This parameter is currently not supported.
         axis : {0 or 'index', 1 or 'columns'}, default 0
             For `Series` this parameter is unused and defaults to 0.
 
diff --git a/pandas/core/indexes/base.py b/pandas/core/indexes/base.py
index 2bf0aca314..054f522e7a 100644
--- a/pandas/core/indexes/base.py
+++ b/pandas/core/indexes/base.py
@@ -1013,6 +1013,42 @@ class Index(IndexOpsMixin, PandasObject):
         return self[:]
 
     def view(self, cls=None):
+        """
+        Return a view on self.
+
+        Parameters
+        ----------
+        cls : data-type or ndarray sub-class, optional
+            Data-type descriptor of the returned view, e.g., float32 or int16.
+            Omitting it results in the view having the same data-type as `self`.
+            This argument can also be specified as an ndarray sub-class,
+            e.g., np.int64 or np.float32 which then specifies the type of
+            the returned object.
+
+        Returns
+        -------
+        numpy.ndarray
+            A new view of the same data in memory.
+
+        See Also
+        --------
+        numpy.ndarray.view : Returns a new view of array with the same data.
+
+        Examples
+        --------
+        >>> s = pd.Series([1, 2, 3], index=["1", "2", "3"])
+        >>> s.index.view("object")
+        array(['1', '2', '3'], dtype=object)
+
+        >>> s = pd.Series([1, 2, 3], index=[-1, 0, 1])
+        >>> s.index.view(np.int64)
+        array([-1,  0,  1])
+        >>> s.index.view(np.float32)
+        array([   nan,    nan, 0.e+00, 0.e+00, 1.e-45, 0.e+00], dtype=float32)
+        >>> s.index.view(np.uint64)
+        array([18446744073709551615,                    0,                    1],
+          dtype=uint64)
+        """
         # we need to see if we are subclassing an
         # index type here
         if cls is not None:
@@ -1120,9 +1156,21 @@ class Index(IndexOpsMixin, PandasObject):
         axis : int, optional
             The axis over which to select values, always 0.
         allow_fill : bool, default True
+            How to handle negative values in `indices`.
+
+            * False: negative values in `indices` indicate positional indices
+              from the right (the default). This is similar to
+              :func:`numpy.take`.
+
+            * True: negative values in `indices` indicate
+              missing values. These values are set to `fill_value`. Any other
+              other negative values raise a ``ValueError``.
+
         fill_value : scalar, default None
             If allow_fill=True and fill_value is not None, indices specified by
             -1 are regarded as NA. If Index doesn't hold NA, raise ValueError.
+        **kwargs
+            Required for compatibility with numpy.
 
         Returns
         -------
@@ -3249,6 +3297,8 @@ class Index(IndexOpsMixin, PandasObject):
         Parameters
         ----------
         other : Index or array-like
+            Index object or an array-like object containing elements to be compared
+            with the elements of the original Index.
         sort : bool or None, default None
             Whether to sort the resulting index. By default, the
             values are attempted to be sorted, but any TypeError from
@@ -3262,6 +3312,14 @@ class Index(IndexOpsMixin, PandasObject):
         Returns
         -------
         Index
+            Returns a new Index object containing elements that are in the original
+            Index but not in the `other` Index.
+
+        See Also
+        --------
+        Index.symmetric_difference : Compute the symmetric difference of two Index
+            objects.
+        Index.intersection : Form the intersection of two Index objects.
 
         Examples
         --------
@@ -5144,10 +5202,18 @@ class Index(IndexOpsMixin, PandasObject):
         Parameters
         ----------
         other : Index or list/tuple of indices
+            Single Index or a collection of indices, which can be either a list or a
+            tuple.
 
         Returns
         -------
         Index
+            Returns a new Index object resulting from appending the provided other
+            indices to the original Index.
+
+        See Also
+        --------
+        Index.insert : Make new Index inserting new item at location.
 
         Examples
         --------
@@ -6330,19 +6396,26 @@ class Index(IndexOpsMixin, PandasObject):
         end : label, default None
             If None, defaults to the end.
         step : int, default None
+            If None, defaults to 1.
 
         Returns
         -------
         slice
+            A slice object.
 
         Raises
         ------
         KeyError : If key does not exist, or key is not unique and index is
             not ordered.
 
+        See Also
+        --------
+        Index.slice_locs : Computes slice locations for input labels.
+        Index.get_slice_bound : Retrieves slice bound that corresponds to given label.
+
         Notes
         -----
-        This function assumes that the data is sorted, so use at your own peril
+        This function assumes that the data is sorted, so use at your own peril.
 
         Examples
         --------


Test patch:
diff --git a/pandas/tests/frame/methods/test_cov_corr.py b/pandas/tests/frame/methods/test_cov_corr.py
index 4d2d83d25e..53aa44f264 100644
--- a/pandas/tests/frame/methods/test_cov_corr.py
+++ b/pandas/tests/frame/methods/test_cov_corr.py
@@ -461,3 +461,28 @@ class TestDataFrameCorrWith:
         result = df_bool.corrwith(ser_bool)
         expected = Series([0.57735, 0.57735], index=["A", "B"])
         tm.assert_series_equal(result, expected)
+
+    def test_corrwith_min_periods_method(self):
+        # GH#9490
+        pytest.importorskip("scipy")
+        df1 = DataFrame(
+            {
+                "A": [1, np.nan, 7, 8],
+                "B": [False, True, True, False],
+                "C": [10, 4, 9, 3],
+            }
+        )
+        df2 = df1[["B", "C"]]
+        result = (df1 + 1).corrwith(df2.B, method="spearman", min_periods=2)
+        expected = Series([0.0, 1.0, 0.0], index=["A", "B", "C"])
+        tm.assert_series_equal(result, expected)
+
+    def test_corrwith_min_periods_boolean(self):
+        # GH#9490
+        df_bool = DataFrame(
+            {"A": [True, True, False, False], "B": [True, False, False, True]}
+        )
+        ser_bool = Series([True, True, False, True])
+        result = df_bool.corrwith(ser_bool, min_periods=3)
+        expected = Series([0.57735, 0.57735], index=["A", "B"])
+        tm.assert_series_equal(result, expected)
diff --git a/pandas/tests/groupby/test_api.py b/pandas/tests/groupby/test_api.py
index d2cfa530e7..33b39bad4a 100644
--- a/pandas/tests/groupby/test_api.py
+++ b/pandas/tests/groupby/test_api.py
@@ -192,6 +192,8 @@ def test_frame_consistency(groupby_func):
         exclude_expected = {"numeric_only"}
     elif groupby_func in ("quantile",):
         exclude_expected = {"method", "axis"}
+    elif groupby_func in ["corrwith"]:
+        exclude_expected = {"min_periods"}
     if groupby_func not in ["pct_change", "size"]:
         exclude_expected |= {"axis"}
 
diff --git a/pandas/tests/plotting/test_datetimelike.py b/pandas/tests/plotting/test_datetimelike.py
index 6b709522ba..4b4eeada58 100644
--- a/pandas/tests/plotting/test_datetimelike.py
+++ b/pandas/tests/plotting/test_datetimelike.py
@@ -1432,13 +1432,19 @@ class TestTSPlot:
         values1 = np.arange(10.0, 11.0, 0.5)
         values2 = np.arange(11.0, 12.0, 0.5)
 
-        kw = {"fmt": "-", "lw": 4}
-
         _, ax = mpl.pyplot.subplots()
-        ax.plot_date([x.toordinal() for x in dates], values1, **kw)
-        ax.plot_date([x.toordinal() for x in dates], values2, **kw)
-
-        line1, line2 = ax.get_lines()
+        (
+            line1,
+            line2,
+        ) = ax.plot(
+            [x.toordinal() for x in dates],
+            values1,
+            "-",
+            [x.toordinal() for x in dates],
+            values2,
+            "-",
+            linewidth=4,
+        )
 
         exp = np.array([x.toordinal() for x in dates], dtype=np.float64)
         tm.assert_numpy_array_equal(line1.get_xydata()[:, 0], exp)