DataFrame.apply(func, axis=0, broadcast=None, raw=False, reduce=None, result_type=None, args=(), **kwds)[source]

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.

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.
broadcast : bool, optional

Only relevant for aggregation functions:

  • False or None : returns a Series whose length is the length of the index or the number of columns (based on the axis parameter)
  • True : results will be broadcast to the original shape of the frame, the original index and columns will be retained.

Deprecated since version 0.23.0: This argument will be removed in a future version, replaced by result_type=’broadcast’.

raw : bool, default False
  • 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.
reduce : bool or None, default None

Try to apply reduction procedures. If the DataFrame is empty, apply will use reduce to determine whether the result should be a Series or a DataFrame. If reduce=None (the default), apply’s return value will be guessed by calling func on an empty Series (note: while guessing, exceptions raised by func will be ignored). If reduce=True a Series will always be returned, and if reduce=False a DataFrame will always be returned.

Deprecated since version 0.23.0: This argument will be removed in a future version, replaced by result_type='reduce'.

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.

New in version 0.23.0.

args : tuple

Positional arguments to pass to func in addition to the array/series.


Additional keyword arguments to pass as keywords arguments to func.

applied : Series or DataFrame

See also

For elementwise operations.
Only perform aggregating type operations.
Only perform transforming type operations.


In the current implementation apply calls func twice on the 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.


>>> 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

Retuning 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
Scroll To Top