pandas.Series.apply¶

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 y : Series or DataFrame if func returns a Series

Series.map
For element-wise operations

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 the args 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
```