A company stores login data and password hashes in two different containers:
- DataFrame with columns: Id, Login, Verified.
- Two-dimensional NumPy array where each element is an array that contains: Id and Password.
Elements on the same row/index have the same Id.
Implement the function login_table that accepts these two containers and modifies id_name_verified DataFrame in-place, so that:
- The Verified column should be removed.
- The password from NumPy array should be added as the last column with the name "Password" to DataFrame.
For example, the following code snippet:
id_name_verified = pd.DataFrame([[1, "JohnDoe", True], [2, "AnnFranklin", False]], columns=["Id", "Login", "Verified"]) id_password = np.array([[1, 987340123], [2, 187031122]], np.int32) login_table(id_name_verified, id_password) print(id_name_verified)
Id Login Password 0 1 JohnDoe 987340123 1 2 AnnFranklin 187031122
- Example case: Wrong answer
- Column Verified is removed: Wrong answer
- Column Password is appended: Wrong answer
- Various DataFrames: Wrong answer