pandas.Series.apply#

Series.apply(func,convert_dtype=<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. IfFalse, leave as dtype=object. Note that the dtype is alwayspreserved for some extension array dtypes, such as Categorical.

Deprecated since version 2.1.0:convert_dtype has been deprecated. Doser.astype(object).apply()instead if you wantconvert_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 ofthe Series, likeSeries.map. If func is a list or dict ofcallables, will first try to translate each func into pandas methods. Ifthat doesn’t work, will try call to apply again withby_row="compat"and if that fails, will call apply again withby_row=False(backward compatible).If False, the func will be passed the whole Series at once.

by_row has no effect whenfunc is a string.

Added 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 unexpectedbehavior or errors and are not supported. SeeMutating with User Defined Function (UDF) methodsfor more details.

Examples

Create a series with typical summer temperatures for each city.

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

Square the values by defining a function and passing it as anargument toapply().

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

Square the values by passing an anonymous function as anargument toapply().

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

Define a custom function that needs additional positionalarguments and pass these additional arguments using theargs keyword.

>>>defsubtract_custom_value(x,custom_value):...returnx-custom_value

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

Define a custom function that takes keyword argumentsand pass these arguments toapply.

>>>defadd_custom_values(x,**kwargs):...formonthinkwargs:...x+=kwargs[month]...returnx

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

Use a function from the Numpy library.

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