Python | Pandas Series.apply()

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.