pandas.Series.apply — pandas 0.25.3 documentation (original) (raw)
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. |
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| Returns: | Series or DataFrame If func returns a Series object the result will be a DataFrame. |
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