pandas.Series.apply — pandas 2.2.3 documentation (original) (raw)

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. 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.

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.

Notes

Functions that mutate the passed object can produce unexpected behavior or errors and are not supported. See Mutating 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']) 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 theargs 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