Python | Pandas Series.apply()

27 Feb, 2019
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 pdimport pandas as pd  # Creating the Seriessr = pd.Series(['New York', 'Chicago', 'Toronto', 'Lisbon', 'Rio'])  # Create the Indexindex_ = ['City 1', 'City 2', 'City 3', 'City 4', 'City 5']   # set the indexsr.index = index_  # Print the seriesprint(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 functionresult = sr.apply(lambda x : 'Montreal' if x =='Rio' else x )  # Print the resultprint(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 pdimport pandas as pd  # Creating the Seriessr = pd.Series([11, 21, 8, 18, 65, 18, 32, 10, 5, 32, None])  # Create the Index# apply yearly frequencyindex_ = pd.date_range('2010-10-09 08:45', periods = 11, freq ='Y')  # set the indexsr.index = index_  # Print the seriesprint(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 Falseresult = sr.apply(lambda x : True if x>30 else False)  # Print the resultprint(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.
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