What is the primary purpose of the “applymap” method in Pandas?

The correct answer is: A. Apply a function element-wise

The applymap method in Pandas applies a function to each element of a DataFrame. This can be useful for performing operations such as converting data types, filtering rows, or calculating statistics.

The sort method in Pandas sorts a DataFrame by a specified column or columns. This can be useful for reordering the rows of a DataFrame or for finding the maximum or minimum values in a column.

The reshape method in Pandas reshapes a DataFrame into a different shape. This can be useful for converting a DataFrame from a long format to a wide format or vice versa.

The groupby method in Pandas groups rows of a DataFrame by a specified column or columns. This can be useful for performing operations such as calculating statistics on groups of rows or for filtering rows based on their group.

Here is an example of how to use the applymap method:

“`
import pandas as pd

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

df[‘C’] = df[‘A’].applymap(lambda x: x * 2)

print(df)

A B C
0 1 2
1 2 4
2 3 6
“`

In this example, the applymap method is used to multiply each element in the A column by 2. The result is a new column, C, which contains the doubled values.