pandas.Series.apply¶
-
Series.
apply
(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.
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.
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