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’.
# importing pandas as pd import pandas as pd # Creating the Series sr = pd.Series(['New York', 'Chicago', 'Toronto', 'Lisbon', 'Rio']) # Create the Index index_ = ['City 1', 'City 2', 'City 3', 'City 4', 'City 5'] # set the index sr.index = index_ # Print the series print(sr) |
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’.
# change 'Rio' to 'Montreal' # we have used a lambda function result = sr.apply(lambda x : 'Montreal' if x =='Rio' else x ) # Print the 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 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.
# importing pandas as pd import pandas as pd # Creating the Series sr = pd.Series([11, 21, 8, 18, 65, 18, 32, 10, 5, 32, None]) # Create the Index # apply yearly frequency index_ = pd.date_range('2010-10-09 08:45', periods = 11, freq ='Y') # set the index sr.index = index_ # Print the 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() function to return True if a value in the given series object is greater than 30 else return False.
# return True if greater than 30 # else return False result = sr.apply(lambda x : True if x>30 else False) # Print the 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, the Series.apply() function has successfully returned the numpy array representation of the given series object.
