The mean Final Grade for all courses

The mean Final Grade for each course

The total Final Grade for each course

The mean Final Grade for the course "History"

A filtered DataFrame with rows where Final Grade > 90

A scatter plot of "Student Name" vs. "Final Grade"

A Series containing only the "Student Name" and "Final Grade" columns

A DataFrame containing only the "Student Name" and "Final Grade" columns

'df['Difference'] = df['Final Grade'].map(lambda x: x - df['Attendance'])'

'df['Difference'] = df.apply(lambda row: row['Attendance'] - row['Final Grade'], axis=1)'

'df['Difference'] = df.apply(lambda row: row['Final Grade'] - row['Attendance'], axis=0)'

'df['Difference'] = df['Attendance'].apply(lambda x: x - df['Final Grade'])'

All columns are of type object

All columns are of type int64

"Student Name" and "Course" are object, "Attendance" and "Final Grade" are int64, and "Date of Birth" is object

All columns are of type float64

To filter rows where Final Grade is less than 60 and display only the "Student Name" column

To plot a scatter graph where Final Grade is less than 60

To calculate the mean Final Grade where Final Grade is less than 60

To group students by their Final Grade where Final Grade is less than 60

df['Final Grade'].mean()

df.mean(axis=1)

df['Final Grade'].sum() / len(df)

df.groupby('Course')['Final Grade'].mean()

[90, 85, 88]

['Alice', 'Bob', 'Charlie']

['History', 'Math', 'Science']

['01-05-2000', '12-20-1999', '07-07-1998']