Pandas series is a One-dimensional ndarray with axis labels. The labels need not be unique but must be a hashable type. The object supports both integer- and label-based indexing and provides a host of methods for performing operations involving the index.

Pandas Series.apply() function invoke the passed function on each element of the given series object.

Syntax: Series.apply(func, convert_dtype=True, args=(), **kwds)

Parameter :
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.

Returns : Series

Example #1: Use Series.apply() function to change the city name to ‘Montreal’ if the city is ‘Rio’.

Output :

City 1    New York
City 2     Chicago
City 3     Toronto
City 4      Lisbon
City 5         Rio
dtype: object

Now we will use Series.apply() function to change the city name to ‘Montreal’ if the city is ‘Rio’.

Output :

City 1    New York
City 2     Chicago
City 3     Toronto
City 4      Lisbon
City 5    Montreal
dtype: object

As we can see in the output, the Series.apply() function has successfully changed the name of the city to ‘Montreal’.
 
Example #2 : Use Series.apply() function to return True if the value in the given series object is greater than 30 else return False.

Output :

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() function to return True if a value in the given series object is greater than 30 else return False.

Output :

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, the Series.apply() function has successfully returned the numpy array representation of the given series object.

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