Solution

The correct method for merging two DataFrames in Pandas is merge(). This function is used when you want to combine DataFrames based on a common column or index. In this case, df1.merge(df2, on='id') merges df1 and df2 based on the column id that is common to both DataFrames. This is a typical operation in data analysis where you want to combine two datasets that share a key variable. Why Other Options Are Incorrect: • B: pd.concat() is used to concatenate DataFrames along a particular axis, but it does not merge based on a common column. It is typically used for stacking DataFrames on top of each other or side by side. • C: df1.join(df2, on='id') is used for joining DataFrames by index, not by a specific column like id. It can be used for indexing-based joins but not for merging on non-index columns. • D: pd.merge(df1, df2, axis=0) is incorrect because the axis parameter is used for concatenation and determines whether to concatenate along rows or columns. It does not merge based on a common column. • E: df1.merge(df2, axis=1) will merge the DataFrames along columns, but it will not merge based on a common column like id. The axis=1 parameter is meant for column-wise operations, not for merging.