xorbits.pandas.Series.apply#

Series.apply(func, convert_dtype=True, output_type=None, args=(), dtypes=None, dtype=None, name=None, index=None, skip_infer=False, **kwds)#

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. Note that the dtype is always preserved for some extension array dtypes, such as Categorical.

  • args (tuple) – Positional arguments passed to func after the series value.

  • **kwargs – Additional keyword arguments passed to func.

Returns

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

Return type

Series or 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 gotchas.udf-mutation 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
output_type{‘dataframe’, ‘series’}, default None

Specify type of returned object. See Notes for more details.

dtypesSeries, default None

Specify dtypes of returned DataFrames. See Notes for more details.

dtypenumpy.dtype, default None

Specify dtype of returned Series. See Notes for more details.

namestr, default None

Specify name of returned Series. See Notes for more details.

indexIndex, default None

Specify index of returned object. See Notes for more details.

skip_infer: bool, default False

Whether infer dtypes when dtypes or output_type is not specified.

This docstring was copied from pandas.core.series.Series.