What is the primary purpose of the “melt” function in Pandas?

The correct answer is C. Reshaping data.

The melt function in Pandas is used to reshape data from a long format to a wide format. In a long format, each row represents a single observation, and each column represents a variable. In a wide format, each row represents a single variable, and each column represents a single observation.

The melt function can be used to reshape data for a variety of purposes, such as:

• To make data easier to visualize
• To make data easier to analyze
• To make data compatible with other software

The melt function takes two arguments:

• id_vars: The columns that should be used as the identifiers for each observation in the wide format.
• value_vars: The columns that should be used as the values for each observation in the wide format.

The melt function returns a new DataFrame with the reshaped data.

Here is an example of how to use the melt function:

“`
import pandas as pd

df = pd.DataFrame({‘A’: [1, 2, 3], ‘B’: [4, 5, 6], ‘C’: [7, 8, 9]})

melted_df = pd.melt(df, id_vars=[‘A’, ‘B’], value_vars=[‘C’])

print(melted_df)

A B C
0 1 7
1 2 8
2 3 9
“`

As you can see, the melt function has reshaped the data from a long format to a wide format. In