The pd.merge() function with the argument how='right' will perform a right join on the two dataframes, which means all the rows from the right_test_df and any common rows from the left_test_df will be in the result. The indicator=True parameter adds a column called _merge to the output DataFrame that can have three possible values: left_only, right_only, or both, indicating the source of each row. By filtering for rows where _merge is right_only, you get all rows that are unique to the right_test_df. However, if you want to find rows that are unique to either DataFrame, you would set how='outer' and then filter for rows where _merge is either right_only or left_only. Here is an example: merged_df = pd.merge(left_test_df, right_test_df, how='outer', indicator=True) final_df = merged_df[merged_df['_merge'] != 'both'] In this version of the code, final_df will contain all rows that are unique to either the left_test_df or the right_test_df. As with many things in programming (and Python/Pandas in particular), there are often multiple valid ways to accomplish a task. This is one efficient way, but depending on the specific requirements of your task, other methods may be more suitable. More on reddit.com

You are on the right track! You can merge df1 and df2 on a shared key ("student_id"). This creates a new dataframe (lets call it df3). df3 will have the column values from both df1 and df2, so you can then compare if both "passed" columns in df3 are equal. df3 = pd.merge(d1, df2, on='student_id) More on reddit.com

You want to try a merge . So you can have 1 dataframe with columns from both years. In the how parameter if you only want players that have played both seasons you can use inner. And the on parameter for there unique key which will be name (if no player shares same name) More on reddit.com

Don't think there's a built-in functionality to quickly do that, but it can be done in two steps: In [19]: df Out[19]: A B 0 h h 1 h h 2 h i Count the number of uniques in each column: In [20]: uniques = df.apply(lambda x: x.nunique()) In [21]: uniques Out[21]: A 1 B 2 dtype: int64 Use boolean indexing on uniques to filter out rows where the number of uniques is not equal to one. Use the result's index to drop the columns in the original dataframe. In [22]: df = df.drop(uniques[uniques==1].index, axis=1) In [23]: df Out[23]: B 0 h 1 h 2 i More on reddit.com