DEPR: remove Series.valid, is_copy, get_ftype_counts, Index.get_duplicate, Series.clip_upper, clip_lower

doc/source/reference/frame.rst

@@ -30,7 +30,6 @@ Attributes and underlying data
    DataFrame.dtypes
    DataFrame.ftypes
    DataFrame.get_dtype_counts
-   DataFrame.get_ftype_counts
    DataFrame.select_dtypes
    DataFrame.values
    DataFrame.get_values
@@ -40,7 +39,6 @@ Attributes and underlying data
    DataFrame.shape
    DataFrame.memory_usage
    DataFrame.empty
-   DataFrame.is_copy
 
 Conversion
 ~~~~~~~~~~
@@ -142,8 +140,6 @@ Computations / descriptive stats
    DataFrame.all
    DataFrame.any
    DataFrame.clip
-   DataFrame.clip_lower
-   DataFrame.clip_upper
    DataFrame.compound
    DataFrame.corr
    DataFrame.corrwith

doc/source/reference/series.rst

@@ -45,7 +45,6 @@ Attributes
    Series.dtypes
    Series.ftypes
    Series.data
-   Series.is_copy
    Series.name
    Series.put
 
@@ -148,8 +147,6 @@ Computations / descriptive stats
    Series.autocorr
    Series.between
    Series.clip
-   Series.clip_lower
-   Series.clip_upper
    Series.corr
    Series.count
    Series.cov

doc/source/whatsnew/v1.0.0.rst

@@ -332,6 +332,11 @@ or ``matplotlib.Axes.plot``. See :ref:`plotting.formatters` for more.
 - Removed previously deprecated "nthreads" argument from :func:`read_feather`, use "use_threads" instead (:issue:`23053`)
 - Removed :meth:`Index.is_lexsorted_for_tuple` (:issue:`29305`)
 - Removed support for nexted renaming in :meth:`DataFrame.aggregate`, :meth:`Series.aggregate`, :meth:`DataFrameGroupBy.aggregate`, :meth:`SeriesGroupBy.aggregate`, :meth:`Rolling.aggregate` (:issue:`29608`)
+- Removed the previously deprecated :meth:`Series.valid`; use :meth:`Series.dropna` instead (:issue:`18800`)
+- Removed the previously properties :attr:`DataFrame.is_copy`, :attr:`Series.is_copy` (:issue:`18812`)
+- Removed the previously deprecated :meth:`DataFrame.get_ftype_counts`, :meth:`Series.get_ftype_counts` (:issue:`18243`)
+- Removed the previously deprecated :meth:`Index.get_duplicated`, use ``idx[idx.duplicated()].unique()`` instead (:issue:`20239`)
+- Removed the previously deprecated :meth:`Series.clip_upper`, :meth:`Series.clip_lower`, :meth:`DataFrame.clip_upper`, :meth:`DataFrame.clip_lower` (:issue:`24203`)
 - Removed previously deprecated "order" argument from :func:`factorize` (:issue:`19751`)
 - Removed previously deprecated "v" argument from :meth:`FrozenNDarray.searchsorted`, use "value" instead (:issue:`22672`)
 - :func:`read_stata` and :meth:`DataFrame.to_stata` no longer supports the "encoding" argument (:issue:`21400`)

pandas/core/generic.py

@@ -172,16 +172,7 @@ class NDFrame(PandasObject, SelectionMixin):
     _internal_names_set = set(_internal_names)  # type: Set[str]
     _accessors = set()  # type: Set[str]
     _deprecations = frozenset(
-        [
-            "clip_lower",
-            "clip_upper",
-            "get_dtype_counts",
-            "get_ftype_counts",
-            "get_values",
-            "is_copy",
-            "ftypes",
-            "ix",
-        ]
+        ["get_dtype_counts", "get_values", "ftypes", "ix"]
     )  # type: FrozenSet[str]
     _metadata = []  # type: List[str]
     _is_copy = None
@@ -252,29 +243,6 @@ def attrs(self) -> Dict[Optional[Hashable], Any]:
     def attrs(self, value: Mapping[Optional[Hashable], Any]) -> None:
         self._attrs = dict(value)
 
-    @property
-    def is_copy(self):
-        """
-        Return the copy.
-        """
-        warnings.warn(
-            "Attribute 'is_copy' is deprecated and will be removed "
-            "in a future version.",
-            FutureWarning,
-            stacklevel=2,
-        )
-        return self._is_copy
-
-    @is_copy.setter
-    def is_copy(self, msg):
-        warnings.warn(
-            "Attribute 'is_copy' is deprecated and will be removed "
-            "in a future version.",
-            FutureWarning,
-            stacklevel=2,
-        )
-        self._is_copy = msg
-
     def _validate_dtype(self, dtype):
         """ validate the passed dtype """
 
@@ -5595,49 +5563,6 @@ def get_dtype_counts(self):
 
         return Series(self._data.get_dtype_counts())
 
-    def get_ftype_counts(self):
-        """
-        Return counts of unique ftypes in this object.
-
-        .. deprecated:: 0.23.0
-
-        Returns
-        -------
-        dtype : Series
-            Series with the count of columns with each type and
-            sparsity (dense/sparse).
-
-        See Also
-        --------
-        ftypes : Return ftypes (indication of sparse/dense and dtype) in
-            this object.
-
-        Examples
-        --------
-        >>> a = [['a', 1, 1.0], ['b', 2, 2.0], ['c', 3, 3.0]]
-        >>> df = pd.DataFrame(a, columns=['str', 'int', 'float'])
-        >>> df
-          str  int  float
-        0   a    1    1.0
-        1   b    2    2.0
-        2   c    3    3.0
-
-        >>> df.get_ftype_counts()  # doctest: +SKIP
-        float64:dense    1
-        int64:dense      1
-        object:dense     1
-        dtype: int64
-        """
-        warnings.warn(
-            "get_ftype_counts is deprecated and will be removed in a future version",
-            FutureWarning,
-            stacklevel=2,
-        )
-
-        from pandas import Series
-
-        return Series(self._data.get_ftype_counts())
-
     @property
     def dtypes(self):
         """
@@ -7526,208 +7451,6 @@ def clip(self, lower=None, upper=None, axis=None, inplace=False, *args, **kwargs
 
         return result
 
-    def clip_upper(self, threshold, axis=None, inplace=False):
-        """
-        Trim values above a given threshold.
-
-        .. deprecated:: 0.24.0
-            Use clip(upper=threshold) instead.
-
-        Elements above the `threshold` will be changed to match the
-        `threshold` value(s). Threshold can be a single value or an array,
-        in the latter case it performs the truncation element-wise.
-
-        Parameters
-        ----------
-        threshold : numeric or array-like
-            Maximum value allowed. All values above threshold will be set to
-            this value.
-
-            * float : every value is compared to `threshold`.
-            * array-like : The shape of `threshold` should match the object
-              it's compared to. When `self` is a Series, `threshold` should be
-              the length. When `self` is a DataFrame, `threshold` should 2-D
-              and the same shape as `self` for ``axis=None``, or 1-D and the
-              same length as the axis being compared.
-
-        axis : {0 or 'index', 1 or 'columns'}, default 0
-            Align object with `threshold` along the given axis.
-        inplace : bool, default False
-            Whether to perform the operation in place on the data.
-
-            .. versionadded:: 0.21.0
-
-        Returns
-        -------
-        Series or DataFrame
-            Original data with values trimmed.
-
-        See Also
-        --------
-        Series.clip : General purpose method to trim Series values to given
-            threshold(s).
-        DataFrame.clip : General purpose method to trim DataFrame values to
-            given threshold(s).
-
-        Examples
-        --------
-        >>> s = pd.Series([1, 2, 3, 4, 5])
-        >>> s
-        0    1
-        1    2
-        2    3
-        3    4
-        4    5
-        dtype: int64
-
-        >>> s.clip(upper=3)
-        0    1
-        1    2
-        2    3
-        3    3
-        4    3
-        dtype: int64
-
-        >>> elemwise_thresholds = [5, 4, 3, 2, 1]
-        >>> elemwise_thresholds
-        [5, 4, 3, 2, 1]
-
-        >>> s.clip(upper=elemwise_thresholds)
-        0    1
-        1    2
-        2    3
-        3    2
-        4    1
-        dtype: int64
-        """
-        warnings.warn(
-            "clip_upper(threshold) is deprecated, use clip(upper=threshold) instead",
-            FutureWarning,
-            stacklevel=2,
-        )
-        return self._clip_with_one_bound(
-            threshold, method=self.le, axis=axis, inplace=inplace
-        )
-
-    def clip_lower(self, threshold, axis=None, inplace=False):
-        """
-        Trim values below a given threshold.
-
-        .. deprecated:: 0.24.0
-            Use clip(lower=threshold) instead.
-
-        Elements below the `threshold` will be changed to match the
-        `threshold` value(s). Threshold can be a single value or an array,
-        in the latter case it performs the truncation element-wise.
-
-        Parameters
-        ----------
-        threshold : numeric or array-like
-            Minimum value allowed. All values below threshold will be set to
-            this value.
-
-            * float : every value is compared to `threshold`.
-            * array-like : The shape of `threshold` should match the object
-              it's compared to. When `self` is a Series, `threshold` should be
-              the length. When `self` is a DataFrame, `threshold` should 2-D
-              and the same shape as `self` for ``axis=None``, or 1-D and the
-              same length as the axis being compared.
-
-        axis : {0 or 'index', 1 or 'columns'}, default 0
-            Align `self` with `threshold` along the given axis.
-
-        inplace : bool, default False
-            Whether to perform the operation in place on the data.
-
-            .. versionadded:: 0.21.0
-
-        Returns
-        -------
-        Series or DataFrame
-            Original data with values trimmed.
-
-        See Also
-        --------
-        Series.clip : General purpose method to trim Series values to given
-            threshold(s).
-        DataFrame.clip : General purpose method to trim DataFrame values to
-            given threshold(s).
-
-        Examples
-        --------
-
-        Series single threshold clipping:
-
-        >>> s = pd.Series([5, 6, 7, 8, 9])
-        >>> s.clip(lower=8)
-        0    8
-        1    8
-        2    8
-        3    8
-        4    9
-        dtype: int64
-
-        Series clipping element-wise using an array of thresholds. `threshold`
-        should be the same length as the Series.
-
-        >>> elemwise_thresholds = [4, 8, 7, 2, 5]
-        >>> s.clip(lower=elemwise_thresholds)
-        0    5
-        1    8
-        2    7
-        3    8
-        4    9
-        dtype: int64
-
-        DataFrames can be compared to a scalar.
-
-        >>> df = pd.DataFrame({"A": [1, 3, 5], "B": [2, 4, 6]})
-        >>> df
-           A  B
-        0  1  2
-        1  3  4
-        2  5  6
-
-        >>> df.clip(lower=3)
-           A  B
-        0  3  3
-        1  3  4
-        2  5  6
-
-        Or to an array of values. By default, `threshold` should be the same
-        shape as the DataFrame.
-
-        >>> df.clip(lower=np.array([[3, 4], [2, 2], [6, 2]]))
-           A  B
-        0  3  4
-        1  3  4
-        2  6  6
-
-        Control how `threshold` is broadcast with `axis`. In this case
-        `threshold` should be the same length as the axis specified by
-        `axis`.
-
-        >>> df.clip(lower=[3, 3, 5], axis='index')
-           A  B
-        0  3  3
-        1  3  4
-        2  5  6
-
-        >>> df.clip(lower=[4, 5], axis='columns')
-           A  B
-        0  4  5
-        1  4  5
-        2  5  6
-        """
-        warnings.warn(
-            "clip_lower(threshold) is deprecated, use clip(lower=threshold) instead",
-            FutureWarning,
-            stacklevel=2,
-        )
-        return self._clip_with_one_bound(
-            threshold, method=self.ge, axis=axis, inplace=inplace
-        )
-
     def groupby(
         self,
         by=None,

pandas/core/internals/managers.py

@@ -79,7 +79,6 @@ class BlockManager(PandasObject):
     copy(deep=True)
 
     get_dtype_counts
-    get_ftype_counts
     get_dtypes
     get_ftypes
 
@@ -246,9 +245,6 @@ def _get_counts(self, f):
     def get_dtype_counts(self):
         return self._get_counts(lambda b: b.dtype.name)
 
-    def get_ftype_counts(self):
-        return self._get_counts(lambda b: b.ftype)
-
     def get_dtypes(self):
         dtypes = np.array([blk.dtype for blk in self.blocks])
         return algos.take_1d(dtypes, self._blknos, allow_fill=False)
@@ -1555,9 +1551,6 @@ def ftype(self):
     def get_dtype_counts(self):
         return {self.dtype.name: 1}
 
-    def get_ftype_counts(self):
-        return {self.ftype: 1}
-
     def get_dtypes(self):
         return np.array([self._block.dtype])