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def apply(self, func: 'AggFuncType', axis: 'Axis' = 0, raw: 'bool' = False, result_type: "Literal['expand', 'reduce', 'broadcast'] | None" = None, args=(), by_row: "Literal[False, 'compat']" = 'compat', engine: "Literal['python', 'numba']" = 'python', engine_kwargs: 'dict[str, bool] | None' = None, **kwargs)
Apply a function along an axis of the DataFrame.
Objects passed to the function are Series objects whose index is
either the DataFrame's index (``axis=0``) or the DataFrame's columns
(``axis=1``). By default (``result_type=None``), the final return type
is inferred from the return type of the applied function. Otherwise,
it depends on the `result_type` argument.
Parameters
----------
func : function
Function to apply to each column or row.
axis : {0 or 'index', 1 or 'columns'}, default 0
Axis along which the function is applied:
* 0 or 'index': apply function to each column.
* 1 or 'columns': apply function to each row.
raw : bool, default False
Determines if row or column is passed as a Series or ndarray object:
* ``False`` : passes each row or column as a Series to the
function.
* ``True`` : the passed function will receive ndarray objects
instead.
If you are just applying a NumPy reduction function this will
achieve much better performance.
result_type : {'expand', 'reduce', 'broadcast', None}, default None
These only act when ``axis=1`` (columns):
* 'expand' : list-like results will be turned into columns.
* 'reduce' : returns a Series if possible rather than expanding
list-like results. This is the opposite of 'expand'.
* 'broadcast' : results will be broadcast to the original shape
of the DataFrame, the original index and columns will be
retained.
The default behaviour (None) depends on the return value of the
applied function: list-like results will be returned as a Series
of those. However if the apply function returns a Series these
are expanded to columns.
args : tuple
Positional arguments to pass to `func` in addition to the
array/series.
by_row : False or "compat", default "compat"
Only has an effect when ``func`` is a listlike or dictlike of funcs
and the func isn't a string.
If "compat", will if possible first translate the func into pandas
methods (e.g. ``Series().apply(np.sum)`` will be translated to
``Series().sum()``). If that doesn't work, will try call to apply again with
``by_row=True`` and if that fails, will call apply again with
``by_row=False`` (backward compatible).
If False, the funcs will be passed the whole Series at once.
.. versionadded:: 2.1.0
engine : {'python', 'numba'}, default 'python'
Choose between the python (default) engine or the numba engine in apply.
The numba engine will attempt to JIT compile the passed function,
which may result in speedups for large DataFrames.
It also supports the following engine_kwargs :
- nopython (compile the function in nopython mode)
- nogil (release the GIL inside the JIT compiled function)
- parallel (try to apply the function in parallel over the DataFrame)
Note: Due to limitations within numba/how pandas interfaces with numba,
you should only use this if raw=True
Note: The numba compiler only supports a subset of
valid Python/numpy operations.
Please read more about the `supported python features
<https://numba.pydata.org/numba-doc/dev/reference/pysupported.html>`_
and `supported numpy features
<https://numba.pydata.org/numba-doc/dev/reference/numpysupported.html>`_
in numba to learn what you can or cannot use in the passed function.
.. versionadded:: 2.2.0
engine_kwargs : dict
Pass keyword arguments to the engine.
This is currently only used by the numba engine,
see the documentation for the engine argument for more information.
**kwargs
Additional keyword arguments to pass as keywords arguments to
`func`.
Returns
-------
Series or DataFrame
Result of applying ``func`` along the given axis of the
DataFrame.
See Also
--------
DataFrame.map: For elementwise operations.
DataFrame.aggregate: Only perform aggregating type operations.
DataFrame.transform: Only perform transforming type operations.
Notes
-----
Functions that mutate the passed object can produce unexpected
behavior or errors and are not supported. See :ref:`gotchas.udf-mutation`
for more details.
Examples
--------
>>> df = pd.DataFrame([[4, 9]] * 3, columns=['A', 'B'])
>>> df
A B
0 4 9
1 4 9
2 4 9
Using a numpy universal function (in this case the same as
``np.sqrt(df)``):
>>> df.apply(np.sqrt)
A B
0 2.0 3.0
1 2.0 3.0
2 2.0 3.0
Using a reducing function on either axis
>>> df.apply(np.sum, axis=0)
A 12
B 27
dtype: int64
>>> df.apply(np.sum, axis=1)
0 13
1 13
2 13
dtype: int64
Returning a list-like will result in a Series
>>> df.apply(lambda x: [1, 2], axis=1)
0 [1, 2]
1 [1, 2]
2 [1, 2]
dtype: object
Passing ``result_type='expand'`` will expand list-like results
to columns of a Dataframe
>>> df.apply(lambda x: [1, 2], axis=1, result_type='expand')
0 1
0 1 2
1 1 2
2 1 2
Returning a Series inside the function is similar to passing
``result_type='expand'``. The resulting column names
will be the Series index.
>>> df.apply(lambda x: pd.Series([1, 2], index=['foo', 'bar']), axis=1)
foo bar
0 1 2
1 1 2
2 1 2
Passing ``result_type='broadcast'`` will ensure the same shape
result, whether list-like or scalar is returned by the function,
and broadcast it along the axis. The resulting column names will
be the originals.
>>> df.apply(lambda x: [1, 2], axis=1, result_type='broadcast')
A B
0 1 2
1 1 2
2 1 2
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