pandas.Series.apply — pandas 0.18.1 documentation (original) (raw)
Series.apply(func, convert_dtype=True, args=(), **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 convert_dtype : boolean, default True Try to find better dtype for elementwise function results. If False, leave as dtype=object args : tuple Positional arguments to pass to function in addition to the value Additional keyword arguments will be passed as keywords to the function |
|---|---|
| Returns: | y : Series or DataFrame if func returns a Series |
Examples
Create a series with typical summer temperatures for each city.
import pandas as pd import numpy as np series = pd.Series([20, 21, 12], index=['London', ... 'New York','Helsinki']) 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 series.apply(square) London 400 New York 441 Helsinki 144 dtype: int64
Square the values by passing an anonymous function as an argument to apply().
series.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
series.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
series.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.
series.apply(np.log) London 2.995732 New York 3.044522 Helsinki 2.484907 dtype: float64