DEPR: remove inplace arg in Categorical methods

doc/source/user_guide/categorical.rst

@@ -353,11 +353,6 @@ Renaming categories is done by using the
 
     In contrast to R's ``factor``, categorical data can have categories of other types than string.
 
-.. note::
-
-    Be aware that assigning new categories is an inplace operation, while most other operations
-    under ``Series.cat`` per default return a new ``Series`` of dtype ``category``.
-
 Categories must be unique or a ``ValueError`` is raised:
 
 .. ipython:: python
@@ -952,7 +947,6 @@ categorical (categories and ordering). So if you read back the CSV file you have
 relevant columns back to ``category`` and assign the right categories and categories ordering.
 
 .. ipython:: python
-    :okwarning:
 
     import io
 
@@ -969,8 +963,8 @@ relevant columns back to ``category`` and assign the right categories and catego
     df2["cats"]
     # Redo the category
     df2["cats"] = df2["cats"].astype("category")
-    df2["cats"].cat.set_categories(
-        ["very bad", "bad", "medium", "good", "very good"], inplace=True
+    df2["cats"] = df2["cats"].cat.set_categories(
+        ["very bad", "bad", "medium", "good", "very good"]
     )
     df2.dtypes
     df2["cats"]
@@ -1153,38 +1147,3 @@ Setting the index will create a ``CategoricalIndex``:
     df.index
     # This now sorts by the categories order
     df.sort_index()
-
-Side effects
-~~~~~~~~~~~~
-
-Constructing a ``Series`` from a ``Categorical`` will not copy the input
-``Categorical``. This means that changes to the ``Series`` will in most cases
-change the original ``Categorical``:
-
-.. ipython:: python
-    :okwarning:
-
-    cat = pd.Categorical([1, 2, 3, 10], categories=[1, 2, 3, 4, 10])
-    s = pd.Series(cat, name="cat")
-    cat
-    s.iloc[0:2] = 10
-    cat
-    df = pd.DataFrame(s)
-    df["cat"].cat.categories = [1, 2, 3, 4, 5]
-    cat
-
-Use ``copy=True`` to prevent such a behaviour or simply don't reuse ``Categoricals``:
-
-.. ipython:: python
-
-    cat = pd.Categorical([1, 2, 3, 10], categories=[1, 2, 3, 4, 10])
-    s = pd.Series(cat, name="cat", copy=True)
-    cat
-    s.iloc[0:2] = 10
-    cat
-
-.. note::
-
-    This also happens in some cases when you supply a NumPy array instead of a ``Categorical``:
-    using an int array (e.g. ``np.array([1,2,3,4])``) will exhibit the same behavior, while using
-    a string array (e.g. ``np.array(["a","b","c","a"])``) will not.

doc/source/whatsnew/v0.15.0.rst

@@ -70,7 +70,6 @@ For full docs, see the :ref:`categorical introduction <categorical>` and the
 :ref:`API documentation <api.arrays.categorical>`.
 
 .. ipython:: python
-    :okwarning:
 
     df = pd.DataFrame({"id": [1, 2, 3, 4, 5, 6],
                        "raw_grade": ['a', 'b', 'b', 'a', 'a', 'e']})
@@ -79,7 +78,7 @@ For full docs, see the :ref:`categorical introduction <categorical>` and the
     df["grade"]
 
     # Rename the categories
-    df["grade"].cat.categories = ["very good", "good", "very bad"]
+    df["grade"] = df["grade"].cat.rename_categories(["very good", "good", "very bad"])
 
     # Reorder the categories and simultaneously add the missing categories
     df["grade"] = df["grade"].cat.set_categories(["very bad", "bad",

doc/source/whatsnew/v0.19.0.rst

@@ -271,12 +271,12 @@ Individual columns can be parsed as a ``Categorical`` using a dict specification
    such as :func:`to_datetime`.
 
    .. ipython:: python
-      :okwarning:
 
       df = pd.read_csv(StringIO(data), dtype="category")
       df.dtypes
       df["col3"]
-      df["col3"].cat.categories = pd.to_numeric(df["col3"].cat.categories)
+      new_categories = pd.to_numeric(df["col3"].cat.categories)
+      df["col3"] = df["col3"].cat.rename_categories(new_categories)
       df["col3"]
 
 .. _whatsnew_0190.enhancements.union_categoricals: