The correct answer is: C. Combine DataFrames.
The merge function in Pandas is used to combine two or more DataFrames. It can be used to join DataFrames on a common column, or to concatenate them.
The aggregate function in Pandas is used to perform aggregate operations on a DataFrame. It can be used to calculate the mean, sum, or count of a column, or to group the DataFrame by a column and calculate the aggregate values for each group.
The sort function in Pandas is used to sort a DataFrame by a column. It can be sorted in ascending or descending order.
The group function in Pandas is used to group a DataFrame by a column. It can be used to calculate the mean, sum, or count of a column for each group, or to create a new DataFrame with the grouped data.
Here is an example of how to use the merge function:
“`
import pandas as pd
df1 = pd.DataFrame({‘A’: [1, 2, 3], ‘B’: [4, 5, 6]})
df2 = pd.DataFrame({‘C’: [7, 8, 9], ‘D’: [10, 11, 12]})
df = df1.merge(df2, on=’A’)
print(df)
A B C D
0 1 4 7
1 2 5 8
2 3 6 9
“`
In this example, the merge function has been used to combine the df1 and df2 DataFrames on the A column. The resulting DataFrame, df, has three columns: A, B, and C. The B and C columns come from the df1 and df2 DataFrames, respectively.