Series.apply() calls the passed function for each element of the given object series.
Syntax: Series.apply (func, convert_dtype = True, args = (), ** kwds)
func: Python function or NumPy ufunc to apply.
convert_dtype: Try to find better dtype for elementwise function results.
args: Positional arguments passed to func after the series value.
** kwds: Additional keyword arguments passed to func.
Example # 1: Use
Series.apply () to change the title cities to "Montreal" if the city is "Rio".
City 1 New York City 2 Chicago City 3 Toronto City 4 Lisbon City 5 Rio dtype: object pre >
We will now use
Series.apply ()to change the city name to Montreal if it is Rio.
City 1 New York City 2 Chicago City 3 Toronto City 4 Lisbon City 5 Montreal dtype: object
As we can see from the output,
Series.apply () < / code> successfully changed the city name to Montreal.
Example # 2: Use
Series.apply () to return True if the value is in this series object is greater than 30, otherwise it returns False.
2010 -12-31 08:45:00 11.0 2011-12-31 08:45:00 21.0 2012-12-31 08:45:00 8.0 2013-12-31 08:45:00 18.0 2014-12-31 08: 45:00 65.0 2015-12-31 08:45:00 18.0 2016-12-31 08:45:00 32.0 2017-12-31 08:45:00 10.0 2018-12-31 08:45:00 5.0 2019- 12-31 08:45:00 32.0 2020-12-31 08:45:00 NaN Freq: A-DEC, dtype: float64
Now we will use
Series.apply () to return True if the value in this series object is greater than 30, otherwise False is returned.
2010-12-31 08:45:00 False 2011-12-31 08:45:00 False 2012-12-31 08:45:00 False 2013-12-31 08:45:00 False 2014-12-31 08:45:00 True 2015-12-31 08:45:00 False 2016-12-31 08 : 45: 00 True 2017-12-31 08:45:00 False 2018-12-31 08:45:00 False 2019-12-31 08:45:00 True 2020-12-31 08:45:00 False Freq: A-DEC, dtype: bool
As we can see in the output,
Series.apply () successfully returned an array representation for the given series object.
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