Apply a function along an axis of the DataFrame.
Objects passed to the function are Series objects whose index is either the DataFrameâs index (axis=0) or the DataFrameâs 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.
Function to apply to each column or row.
Axis along which the function is applied:
0 or âindexâ: apply function to each column.
1 or âcolumnsâ: apply function to each row.
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.
These only act when axis=1 (columns):
âexpandâ : list-like results will be turned into columns.
âreduceâ : returns a Series if possible rather than expanding list-like results. This is the opposite of âexpandâ.
âbroadcastâ : 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.
Positional arguments to pass to func in addition to the array/series.
Only has an effect when func is a listlike or dictlike of funcs and the func isnât a string. If âcompatâ, will if possible first translate the func into pandas methods (e.g. Series().apply(np.sum) will be translated to Series().sum()). If that doesnât 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.
Added in version 2.1.0.
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 and supported numpy features in numba to learn what you can or cannot use in the passed function.
Added in version 2.2.0.
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.
Additional keyword arguments to pass as keywords arguments to func.
Result of applying func along the given axis of the DataFrame.
Notes
Functions that mutate the passed object can produce unexpected behavior or errors and are not supported. See Mutating with User Defined Function (UDF) methods 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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