How Can I Use The Apply() Function For A Single Column?

A DataFrame is the primary data structure of the Pandas library and is commonly used for storing and working with tabular data. A common operation that could be performed on such data is to use the apply() function for a single column in order to update information in the DataFrame.

To start working with Pandas, we first need to import this statement in the Python code :

Python 3 Code :

import pandas as pd

Running Example

Let us understand this operation with the help of an example. Consider the following DataFrame containing 3 students with names A, B, and C and their corresponding marks (out of 10) for two subjects, Mathematics and Physics.

Python code snippet for generating the above DataFrame : 

Python 3 Code : 

# Importing pandas
import pandas as pd

# Dictionary for our data
data = {'Name' : ['A', 'B', 'C'], 'Mathematics' : [8, 5, 10], 'Physics' : [7, 9, 8]}

# DataFrame for the dictionary
df = pd.DataFrame(data)

# Printing the DataFrame
print(df)

Here, data is a dictionary we created to initialize the DataFrame. For this, we use the DataFrame() function of the Pandas library which takes the dictionary as an argument and returns the required DataFrame.

Now, let’s say we need to reduce the physics marks of all students by 1 for some reason. The marks that are 7,9,8 currently would become 6,8,7 respectively. The resulting DataFrame would look like this :

Let us look at different ways of performing this operation on a given DataFrame : 

1. Using Series.apply() function

This method is pretty straightforward and is the most commonly used one. In this method we use the Series.apply() function, as planned, to perform an operation on an entire column.

Here, the operation is to reduce the physics marks of each student by 1. Here, df[‘Physics’] is used to access the column with the label Physics in the DataFrame.

Lambda functions can be used wherever function objects are required. Small anonymous functions can be created with the lambda keyword.

They are syntactically restricted to a single expression. The changes are not made in place so we need to reassign the column.

Let us look at the Python 3 code and corresponding output for this method

Python 3 Code : 

# Importing pandas
import pandas as pd

# Dictionary for our data
data = {'Name' : ['A', 'B', 'C'], 'Mathematics' : [8, 5, 10], 'Physics' : [7, 9, 8]}

# DataFrame for the dictionary
df = pd.DataFrame(data)

# Performing the operation
df['Physics'] = df['Physics'].apply(lambda x : x-1)

# Printing the updated DataFrame
print(df)

Output : 

Another way to perform the same operation would be to use df.Physics to access the column labeled as Physics instead of using df[‘Physics’]. The changes are not made in place so we need to reassign the column. Let us look at the Python code and corresponding output for this method - 

Python 3 Code : 

# Importing pandas
import pandas as pd

# Dictionary for our data
data = {'Name' : ['A', 'B', 'C'], 'Mathematics' : [8, 5, 10], 'Physics' : [7, 9, 8]}

# DataFrame for the dictionary
df = pd.DataFrame(data)

# Performing the operation
df.Physics = df.Physics.apply(lambda x : x-1)

# Printing the updated DataFrame
print(df)

Output : 

2. Using the Series.map() function

This method is an alternative method to the previous one. In this method, we use the Series.map() function to perform an operation on an entire column.

Here, the operation is to reduce the physics marks of each student by 1. Here, df[‘Physics’] is used to access the column with the label Physics in the DataFrame. Lambda functions can be used wherever function objects are required.

Small anonymous functions can be created with the lambda keyword. They are syntactically restricted to a single expression. The changes are not made in place so we need to reassign the column.

Let us look at the Python code and corresponding output for this method:

Python 3 Code : 

# Importing panda
import pandas as pd

# Dictionary for our data
data = {'Name' : ['A', 'B', 'C'], 'Mathematics' : [8, 5, 10], 'Physics' : [7, 9, 8]}

# DataFrame for the dictionary
df = pd.DataFrame(data)

# Performing the operation
df['Physics'] = df['Physics'].map(lambda x : x-1)

# Printing the updated DataFrame
print(df)

Output : 

Another way to perform the same operation would be to use df.Physics to access the column labeled as Physics instead of using df[‘Physics’]. Let us look at the Python code and corresponding output for this method - 

Python 3 Code : 

# Importing pandas
import pandas as pd

# Dictionary for our data
data = {'Name' : ['A', 'B', 'C'], 'Mathematics' : [8, 5, 10], 'Physics' : [7, 9, 8]}

# DataFrame for the dictionary
df = pd.DataFrame(data)

# Performing the operation
df.Physics = df.Physics.map(lambda x : x-1)

# Printing the updated DataFrame
print(df)

Output : 

Conclusion

In this topic, we have learned how to use the apply() function for a single column in an existing Pandas DataFrame, following a running example of test scores of students in different subjects, thus giving us an intuition of how this concept could be applied in real-world situations. Feel free to reach out to info.javaexercise@gmail.com in case of any suggestions