pandas.Series.apply

Series.apply(self, func, convert_dtype=True, args=(), **kwds) [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:	func : function Python function or NumPy ufunc to apply. convert_dtype : bool, default True Try to find better dtype for elementwise function results. If False, leave as dtype=object. args : tuple Positional arguments passed to func after the series value. **kwds 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.

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