Series.apply() Pandas Series.apply() calls the passed function for each element of the given object series.

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 () to change the title cities 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  
 We will now use  Series.apply ()  to change the city name to Montreal if it is Rio. 
 
 
 
 
 
 
 
 
 
 
  # change & # 39; Rio & # 39; on & # 39; Montreal & # 39;  
  # we used a lambda function  
 
  result   =   sr.   apply   (  lambda   x:  ` Montreal`   if   x   =   =   `Rio`   else   x)  
 
  
  # Print result  
 
  print   (result) 
 
 
  Output:  
 
 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. 





  # import pandas as pd  
  import   pandas as pd 
   
  # Create series  
  sr   =   pd.Series ([  11  ,   21  ,   8  ,   18  ,   65  ,   18  ,   32  ,   10  ,   5  ,   32  ,   None  ]) 
  
  # Create index  
  # apply annual rate   
  index_   =   pd.date_range (  `2010-10-09 08:45`  , periods   =   11  , freq   =   `Y`  ) 

  
  # set index  
  sr.index  < code class = "keyword"> =   index_ 
  
  # Print series  
  print   (sr)   



  
  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 ()  to return True if the value in this series object is greater than 30, otherwise False is returned. 





  # return True if more than 30  
  # still return False  
  result   =   sr.   apply   (  lambda   x:   True   if   x & gt;   30   else   False  ) 
  
  # Print result  
  print   (result) 




  
  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,  Series.apply ()  successfully returned an array representation for the given series object.