API: rename DataFrame.applymap -> DataFrame.map

Author: topper-123

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/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` (:meth:`DataFrame.applymap`) 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
@@ -59,14 +59,16 @@ 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")
 
 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.
 
+Notice also that :meth:`DataFrame.applymap` has been renamed to :meth:`DataFrame.map` (:issue:`52353`).
+
 .. _whatsnew_210.enhancements.other:
 
 Other enhancements
@@ -172,6 +174,7 @@ Deprecations
 - Deprecated the methods :meth:`Series.bool` and :meth:`DataFrame.bool` (:issue:`51749`)
 - Deprecated :meth:`DataFrame.swapaxes` and :meth:`Series.swapaxes`, use :meth:`DataFrame.transpose` or :meth:`Series.transpose` instead (:issue:`51946`)
 - Deprecated parameter ``convert_type`` in :meth:`Series.apply` (:issue:`52140`)
+- Deprecated :meth:`DataFrame.applymap`. Use the new :meth:`DataFrame.map` method instead (:issue:`52353`)
 -
 
 

pandas/core/frame.py

@@ -9711,7 +9711,7 @@ 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.
 
@@ -9803,7 +9803,7 @@ def apply(
         )
         return op.apply().__finalize__(self, method="apply")
 
-    def applymap(
+    def map(
         self, func: PythonFuncType, na_action: str | None = None, **kwargs
     ) -> DataFrame:
         """
@@ -9845,7 +9845,7 @@ 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
@@ -9862,7 +9862,7 @@ def applymap(
         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
@@ -9889,6 +9889,47 @@ def infer(x):
 
         return self.apply(infer).__finalize__(self, "applymap")
 
+    def applymap(
+        self, func: PythonFuncType, na_action: str | None = None, **kwargs
+    ) -> DataFrame:
+        """
+        Apply a function to a {klass} 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`.
+        """
+        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

@@ -5953,7 +5953,7 @@ 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

@@ -4265,7 +4265,7 @@ 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

@@ -606,7 +606,7 @@
     --------
     {klass}.fillna : Fill NA values.
     {klass}.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()