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DOC: Removed Notes from DataFrame.applymap #31695

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190faac
Update generic.py
r0cketr1kky Feb 4, 2020
8b53292
Updated generic.py
r0cketr1kky Feb 4, 2020
4227ba4
Updated generic.py
r0cketr1kky Feb 4, 2020
cd7a881
Removed notes from df.applymap
r0cketr1kky Feb 5, 2020
4a44260
Updated frame.py
r0cketr1kky Feb 5, 2020
f95e6c5
Updated frame.py
r0cketr1kky Feb 5, 2020
0ec76ed
Updated frame.py
r0cketr1kky Feb 5, 2020
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r0cketr1kky Feb 5, 2020
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r0cketr1kky Feb 5, 2020
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10 changes: 1 addition & 9 deletions pandas/core/frame.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6969,13 +6969,6 @@ def applymap(self, func) -> "DataFrame":
--------
DataFrame.apply : Apply a function along input axis of DataFrame.

Notes
-----
In the current implementation applymap calls `func` twice on the
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is this no longer accurate? we regularly get questions about the user-defined functions being called in groupby.apply/agg to determine fast vs slow path

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In #28854 this was removed from DataFrame.apply, and there are comments on the original issue (#28827) saying that the implementation has changed recently, and DataFrame.applymap directly calls DataFrame.apply

first column/row to decide whether it can take a fast or slow
code path. This can lead to unexpected behavior if `func` has
side-effects, as they will take effect twice for the first
column/row.

Examples
--------
Expand All @@ -6990,8 +6983,7 @@ def applymap(self, func) -> "DataFrame":
0 3 4
1 5 5

Note that a vectorized version of `func` often exists, which will
be much faster. You could square each number elementwise.
You could square each number elementwise.
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This part doesn't need changing, the vectorised version is still faster:

In [28]: s = pd.Series(np.random.randn(10000))                                                                                                               

In [29]: %timeit s.apply(lambda x: x**2)                                                                                                                     
6.39 ms ± 5.73 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

In [30]: %timeit s**2                                                                                                                                        
243 µs ± 4.38 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

In [31]: pd.testing.assert_series_equal(s.apply(lambda x: x**2), s**2)

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Oh okay. Thanks a lot. Included it.

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can you revert this

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Sorry, for being late! I'm having a very busy semester. I'll do this asap and get back!


>>> df.applymap(lambda x: x**2)
0 1
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22 changes: 21 additions & 1 deletion pandas/core/generic.py
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Expand Up @@ -9960,7 +9960,7 @@ def _add_numeric_operations(cls):
see_also="",
examples="",
)
@Appender(_num_doc)
@Appender(_num_doc_mad)
def mad(self, axis=None, skipna=None, level=None):
if skipna is None:
skipna = True
Expand Down Expand Up @@ -10329,6 +10329,26 @@ def _doc_parms(cls):
%(examples)s
"""

_num_doc_mad = """
%(desc)s
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this is an unrelated change, can you revert


Parameters
----------
axis : %(axis_descr)s
Axis for the function to be applied on.
skipna : bool, default None
Exclude NA/null values when computing the result.
level : int or level name, default None
If the axis is a MultiIndex (hierarchical), count along a
particular level, collapsing into a %(name1)s.

Returns
-------
%(name1)s or %(name2)s (if level specified)\
%(see_also)s\
%(examples)s
"""

_num_ddof_doc = """
%(desc)s

Expand Down