API/DEPR: Rename DataFrame.applymap to DataFrame.map

doc/source/development/roadmap.rst

@@ -179,7 +179,7 @@ Numba-accelerated operations
 
 `Numba <https://numba.pydata.org>`__ is a JIT compiler for Python code. We'd like to provide
 ways for users to apply their own Numba-jitted functions where pandas accepts user-defined functions
-(for example, :meth:`Series.apply`, :meth:`DataFrame.apply`, :meth:`DataFrame.applymap`,
+(for example, :meth:`Series.apply`, :meth:`DataFrame.apply`, :meth:`DataFrame.map`,
 and in groupby and window contexts). This will improve the performance of
 user-defined-functions in these operations by staying within compiled code.
 

doc/source/reference/frame.rst

@@ -116,6 +116,7 @@ Function application, GroupBy & window
    :toctree: api/
 
    DataFrame.apply
+   DataFrame.map
    DataFrame.applymap
    DataFrame.pipe
    DataFrame.agg

doc/source/user_guide/basics.rst

@@ -758,7 +758,7 @@ on an entire ``DataFrame`` or ``Series``, row- or column-wise, or elementwise.
 1. `Tablewise Function Application`_: :meth:`~DataFrame.pipe`
 2. `Row or Column-wise Function Application`_: :meth:`~DataFrame.apply`
 3. `Aggregation API`_: :meth:`~DataFrame.agg` and :meth:`~DataFrame.transform`
-4. `Applying Elementwise Functions`_: :meth:`~DataFrame.applymap`
+4. `Applying Elementwise Functions`_: :meth:`~DataFrame.map`
 
 .. _basics.pipe:
 
@@ -1170,7 +1170,7 @@ Applying elementwise functions
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 Since not all functions can be vectorized (accept NumPy arrays and return
-another array or value), the methods :meth:`~DataFrame.applymap` on DataFrame
+another array or value), the methods :meth:`~DataFrame.map` on DataFrame
 and analogously :meth:`~Series.map` on Series accept any Python function taking
 a single value and returning a single value. For example:
 
@@ -1187,7 +1187,7 @@ a single value and returning a single value. For example:
        return len(str(x))
 
    df4["one"].map(f)
-   df4.applymap(f)
+   df4.map(f)
 
 :meth:`Series.map` has an additional feature; it can be used to easily
 "link" or "map" values defined by a secondary series. This is closely related

doc/source/user_guide/cookbook.rst

@@ -242,7 +242,7 @@ Ambiguity arises when an index consists of integers with a non-zero start or non
 New columns
 ***********
 
-`Efficiently and dynamically creating new columns using applymap
+`Efficiently and dynamically creating new columns using DataFrame.map (previously named applymap)
 <https://stackoverflow.com/questions/16575868/efficiently-creating-additional-columns-in-a-pandas-dataframe-using-map>`__
 
 .. ipython:: python
@@ -254,7 +254,7 @@ New columns
    new_cols = [str(x) + "_cat" for x in source_cols]
    categories = {1: "Alpha", 2: "Beta", 3: "Charlie"}
 
-   df[new_cols] = df[source_cols].applymap(categories.get)
+   df[new_cols] = df[source_cols].map(categories.get)
    df
 
 `Keep other columns when using min() with groupby

doc/source/user_guide/style.ipynb

@@ -352,7 +352,7 @@
     "\n",
     "- Using [.set_table_styles()][table] to control broader areas of the table with specified internal CSS. Although table styles allow the flexibility to add CSS selectors and properties controlling all individual parts of the table, they are unwieldy for individual cell specifications. Also, note that table styles cannot be exported to Excel. \n",
     "- Using [.set_td_classes()][td_class] to directly link either external CSS classes to your data cells or link the internal CSS classes created by [.set_table_styles()][table]. See [here](#Setting-Classes-and-Linking-to-External-CSS). These cannot be used on column header rows or indexes, and also won't export to Excel. \n",
-    "- Using the [.apply()][apply] and [.applymap()][applymap] functions to add direct internal CSS to specific data cells. See [here](#Styler-Functions). As of v1.4.0 there are also methods that work directly on column header rows or indexes; [.apply_index()][applyindex] and [.applymap_index()][applymapindex]. Note that only these methods add styles that will export to Excel. These methods work in a similar way to [DataFrame.apply()][dfapply] and [DataFrame.applymap()][dfapplymap].\n",
+    "- Using the [.apply()][apply] and [.applymap()][applymap] functions to add direct internal CSS to specific data cells. See [here](#Styler-Functions). As of v1.4.0 there are also methods that work directly on column header rows or indexes; [.apply_index()][applyindex] and [.applymap_index()][applymapindex]. Note that only these methods add styles that will export to Excel. These methods work in a similar way to [DataFrame.apply()][dfapply] and [DataFrame.map()][dfmap].\n",
     "\n",
     "[table]: ../reference/api/pandas.io.formats.style.Styler.set_table_styles.rst\n",
     "[styler]: ../reference/api/pandas.io.formats.style.Styler.rst\n",
@@ -362,7 +362,7 @@
     "[applyindex]: ../reference/api/pandas.io.formats.style.Styler.apply_index.rst\n",
     "[applymapindex]: ../reference/api/pandas.io.formats.style.Styler.applymap_index.rst\n",
     "[dfapply]: ../reference/api/pandas.DataFrame.apply.rst\n",
-    "[dfapplymap]: ../reference/api/pandas.DataFrame.applymap.rst"
+    "[dfmap]: ../reference/api/pandas.DataFrame.map.rst"
    ]
   },
   {

doc/source/user_guide/visualization.rst

@@ -1709,7 +1709,7 @@ Colormaps can also be used other plot types, like bar charts:
 
 .. ipython:: python
 
-   dd = pd.DataFrame(np.random.randn(10, 10)).applymap(abs)
+   dd = pd.DataFrame(np.random.randn(10, 10)).map(abs)
    dd = dd.cumsum()
 
    plt.figure();

doc/source/whatsnew/v2.1.0.rst

@@ -25,7 +25,7 @@ enhancement1
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 When given a callable, :meth:`Series.map` applies the callable to all elements of the :class:`Series`.
-Similarly, :meth:`DataFrame.applymap` applies the callable to all elements of the :class:`DataFrame`,
+Similarly, :meth:`DataFrame.map` applies the callable to all elements of the :class:`DataFrame`,
 while :meth:`Index.map` applies the callable to all elements of the :class:`Index`.
 
 Frequently, it is not desirable to apply the callable to nan-like values of the array and to avoid doing
@@ -59,10 +59,12 @@ and ``na_action="ignore"`` did not work correctly for any ``ExtensionArray`` sub
     ser = pd.Series(["a", "b", np.nan], dtype="category")
     ser.map(str.upper, na_action="ignore")
     df = pd.DataFrame(ser)
-    df.applymap(str.upper, na_action="ignore")
+    df.map(str.upper, na_action="ignore")
     idx = pd.Index(ser)
     idx.map(str.upper, na_action="ignore")
 
+Notice also that in this version, :meth:`DataFrame.map` been added and :meth:`DataFrame.applymap` has been deprecated. :meth:`DataFrame.map` has the same functionality as :meth:`DataFrame.applymap`, but the new name better communicate that this is the :class:`DataFrame` version of :meth:`Series.map` (:issue:`52353`).
+
 Also, note that :meth:`Categorical.map` implicitly has had its ``na_action`` set to ``"ignore"`` by default.
 This has been deprecated and will :meth:`Categorical.map` in the future change the default
 to ``na_action=None``, like for all the other array types.
@@ -223,6 +225,7 @@ Deprecations
 - Deprecated :func:`is_datetime64tz_dtype`, check ``isinstance(dtype, pd.DatetimeTZDtype)`` instead (:issue:`52607`)
 - Deprecated :func:`is_int64_dtype`, check ``dtype == np.dtype(np.int64)`` instead (:issue:`52564`)
 - Deprecated :func:`is_interval_dtype`, check ``isinstance(dtype, pd.IntervalDtype)`` instead (:issue:`52607`)
+- Deprecated :meth:`DataFrame.applymap`. Use the new :meth:`DataFrame.map` method instead (:issue:`52353`)
 - Deprecated :meth:`DataFrame.swapaxes` and :meth:`Series.swapaxes`, use :meth:`DataFrame.transpose` or :meth:`Series.transpose` instead (:issue:`51946`)
 - Deprecated ``freq`` parameter in :class:`PeriodArray` constructor, pass ``dtype`` instead (:issue:`52462`)
 - Deprecated behavior of :meth:`Series.dt.to_pydatetime`, in a future version this will return a :class:`Series` containing python ``datetime`` objects instead of an ``ndarray`` of datetimes; this matches the behavior of other :meth:`Series.dt` properties (:issue:`20306`)

pandas/core/frame.py

@@ -9673,7 +9673,7 @@ def apply(
 
         See Also
         --------
-        DataFrame.applymap: For elementwise operations.
+        DataFrame.map: For elementwise operations.
         DataFrame.aggregate: Only perform aggregating type operations.
         DataFrame.transform: Only perform transforming type operations.
 
@@ -9765,12 +9765,16 @@ def apply(
         )
         return op.apply().__finalize__(self, method="apply")
 
-    def applymap(
+    def map(
         self, func: PythonFuncType, na_action: str | None = None, **kwargs
     ) -> DataFrame:
         """
         Apply a function to a Dataframe elementwise.
 
+        .. versionadded:: 2.1.0
+
+           DataFrame.applymap was deprecated and renamed to DataFrame.map.
+
         This method applies a function that accepts and returns a scalar
         to every element of a DataFrame.
 
@@ -9798,6 +9802,7 @@ def applymap(
         --------
         DataFrame.apply : Apply a function along input axis of DataFrame.
         DataFrame.replace: Replace values given in `to_replace` with `value`.
+        Series.map : Apply a function elementwise on a Series.
 
         Examples
         --------
@@ -9807,7 +9812,7 @@ def applymap(
         0  1.000  2.120
         1  3.356  4.567
 
-        >>> df.applymap(lambda x: len(str(x)))
+        >>> df.map(lambda x: len(str(x)))
            0  1
         0  3  4
         1  5  5
@@ -9816,20 +9821,20 @@ def applymap(
 
         >>> df_copy = df.copy()
         >>> df_copy.iloc[0, 0] = pd.NA
-        >>> df_copy.applymap(lambda x: len(str(x)), na_action='ignore')
+        >>> df_copy.map(lambda x: len(str(x)), na_action='ignore')
              0  1
         0  NaN  4
         1  5.0  5
 
         Note that a vectorized version of `func` often exists, which will
         be much faster. You could square each number elementwise.
 
-        >>> df.applymap(lambda x: x**2)
+        >>> df.map(lambda x: x**2)
                    0          1
         0   1.000000   4.494400
         1  11.262736  20.857489
 
-        But it's better to avoid applymap in that case.
+        But it's better to avoid map in that case.
 
         >>> df ** 2
                    0          1
@@ -9849,7 +9854,61 @@ def applymap(
         def infer(x):
             return x._map_values(func, na_action=na_action)
 
-        return self.apply(infer).__finalize__(self, "applymap")
+        return self.apply(infer).__finalize__(self, "map")
+
+    def applymap(
+        self, func: PythonFuncType, na_action: str | None = None, **kwargs
+    ) -> DataFrame:
+        """
+        Apply a function to a Dataframe elementwise.
+
+        .. deprecated:: 2.1.0
+
+           DataFrame.applymap has been deprecated. Use DataFrame.map instead.
+
+        This method applies a function that accepts and returns a scalar
+        to every element of a DataFrame.
+
+        Parameters
+        ----------
+        func : callable
+            Python function, returns a single value from a single value.
+        na_action : {None, 'ignore'}, default None
+            If ‘ignore’, propagate NaN values, without passing them to func.
+        **kwargs
+            Additional keyword arguments to pass as keywords arguments to
+            `func`.
+
+        Returns
+        -------
+        DataFrame
+            Transformed DataFrame.
+
+        See Also
+        --------
+        DataFrame.apply : Apply a function along input axis of DataFrame.
+        DataFrame.map : Apply a function along input axis of DataFrame.
+        DataFrame.replace: Replace values given in `to_replace` with `value`.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([[1, 2.12], [3.356, 4.567]])
+        >>> df
+               0      1
+        0  1.000  2.120
+        1  3.356  4.567
+
+        >>> df.map(lambda x: len(str(x)))
+           0  1
+        0  3  4
+        1  5  5
+        """
+        warnings.warn(
+            "DataFrame.applymap has been deprecated. Use DataFrame.map instead.",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        return self.map(func, na_action=na_action, **kwargs)
 
     # ----------------------------------------------------------------------
     # Merging / joining methods

pandas/core/generic.py

@@ -5942,7 +5942,7 @@ def pipe(
         See Also
         --------
         DataFrame.apply : Apply a function along input axis of DataFrame.
-        DataFrame.applymap : Apply a function elementwise on a whole DataFrame.
+        DataFrame.map : Apply a function elementwise on a whole DataFrame.
         Series.map : Apply a mapping correspondence on a
             :class:`~pandas.Series`.
 

pandas/core/series.py

@@ -4250,7 +4250,7 @@ def map(
         Series.apply : For applying more complex functions on a Series.
         Series.replace: Replace values given in `to_replace` with `value`.
         DataFrame.apply : Apply a function row-/column-wise.
-        DataFrame.applymap : Apply a function elementwise on a whole DataFrame.
+        DataFrame.map : Apply a function elementwise on a whole DataFrame.
 
         Notes
         -----

pandas/core/shared_docs.py

@@ -609,7 +609,7 @@
     DataFrame.fillna : Fill NA values.
     Series.where : Replace values based on boolean condition.
     DataFrame.where : Replace values based on boolean condition.
-    DataFrame.applymap: Apply a function to a Dataframe elementwise.
+    DataFrame.map: Apply a function to a Dataframe elementwise.
     Series.map: Map values of Series according to an input mapping or function.
     Series.str.replace : Simple string replacement.
 

pandas/io/formats/style.py

@@ -1813,7 +1813,7 @@ def _apply_index(
         if method == "apply":
             result = data.apply(func, axis=0, **kwargs)
         elif method == "applymap":
-            result = data.applymap(func, **kwargs)
+            result = data.map(func, **kwargs)
 
         self._update_ctx_header(result, axis)
         return self
@@ -1938,7 +1938,7 @@ def _applymap(
         if subset is None:
             subset = IndexSlice[:]
         subset = non_reducing_slice(subset)
-        result = self.data.loc[subset].applymap(func)
+        result = self.data.loc[subset].map(func)
         self._update_ctx(result)
         return self
 

pandas/io/json/_json.py

@@ -365,7 +365,7 @@ def __init__(
         obj = obj.copy()
         timedeltas = obj.select_dtypes(include=["timedelta"]).columns
         if len(timedeltas):
-            obj[timedeltas] = obj[timedeltas].applymap(lambda x: x.isoformat())
+            obj[timedeltas] = obj[timedeltas].map(lambda x: x.isoformat())
         # Convert PeriodIndex to datetimes before serializing
         if isinstance(obj.index.dtype, PeriodDtype):
             obj.index = obj.index.to_timestamp()