pandas.Series.apply#

Series.apply(func, convert_dtype=_NoDefault.no_default, args=(), *, by_row='compat', **kwargs)[source]#

Invoke function on values of Series.

Can be ufunc (a NumPy function that applies to the entire Series) or a Python function that only works on single values.

Parameters:

funcfunction

Python function or NumPy ufunc to apply.

convert_dtypebool, default True

Try to find better dtype for elementwise function results. If False, leave as dtype=object. Note that the dtype is always preserved for some extension array dtypes, such as Categorical.

Deprecated since version 2.1.0: convert_dtype has been deprecated. Do ser.astype(object).apply() instead if you want convert_dtype=False.

argstuple

Positional arguments passed to func after the series value.

by_rowFalse or “compat”, default “compat”

If "compat" and func is a callable, func will be passed each element of the Series, like Series.map. If func is a list or dict of callables, will first try to translate each func into pandas methods. If that doesn’t work, will try call to apply again with by_row="compat" and if that fails, will call apply again with by_row=False (backward compatible). If False, the func will be passed the whole Series at once.

by_row has no effect when func is a string.

New in version 2.1.0.

**kwargs

Additional keyword arguments passed to func.

Returns:

Series or DataFrame: If func returns a Series object the result will be a DataFrame.

See also

Series.map: For element-wise operations.
Series.agg: Only perform aggregating type operations.
Series.transform: Only perform transforming type operations.

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

Create a series with typical summer temperatures for each city.

>>> s = pd.Series([20, 21, 12],
...               index=['London', 'New York', 'Helsinki'])
>>> s
London      20
New York    21
Helsinki    12
dtype: int64

Square the values by defining a function and passing it as an argument to apply().

>>> def square(x):
...     return x ** 2
>>> s.apply(square)
London      400
New York    441
Helsinki    144
dtype: int64

Square the values by passing an anonymous function as an argument to apply().

>>> s.apply(lambda x: x ** 2)
London      400
New York    441
Helsinki    144
dtype: int64

Define a custom function that needs additional positional arguments and pass these additional arguments using the args keyword.

>>> def subtract_custom_value(x, custom_value):
...     return x - custom_value

>>> s.apply(subtract_custom_value, args=(5,))
London      15
New York    16
Helsinki     7
dtype: int64

Define a custom function that takes keyword arguments and pass these arguments to apply.

>>> def add_custom_values(x, **kwargs):
...     for month in kwargs:
...         x += kwargs[month]
...     return x

>>> s.apply(add_custom_values, june=30, july=20, august=25)
London      95
New York    96
Helsinki    87
dtype: int64

Use a function from the Numpy library.

>>> s.apply(np.log)
London      2.995732
New York    3.044522
Helsinki    2.484907
dtype: float64