12. DataFrame apply()

import pandas as pd

grades_df = pd.DataFrame(
    data={'exam1': [43, 81, 78, 75, 89, 70, 91, 65, 98, 87],
          'exam2': [24, 63, 56, 56, 67, 51, 79, 46, 72, 60]},
    index=['Andre', 'Barry', 'Chris', 'Dan', 'Emilio', 
           'Fred', 'Greta', 'Humbert', 'Ivan', 'James']
)

# Change False to True for this block of code to see what it does

# DataFrame apply()
if False:
    def convert_grades_curve(exam_grades):
        # Pandas has a bult-in function that will perform this calculation
        # This will give the bottom 0% to 10% of students the grade 'F',
        # 10% to 20% the grade 'D', and so on. You can read more about
        # the qcut() function here:
        # http://pandas.pydata.org/pandas-docs/stable/generated/pandas.qcut.html
        return pd.qcut(exam_grades,
                       [0, 0.1, 0.2, 0.5, 0.8, 1],
                       labels=['F', 'D', 'C', 'B', 'A'])
        
    # qcut() operates on a list, array, or Series. This is the
    # result of running the function on a single column of the
    # DataFrame.
    print convert_grades_curve(grades_df['exam1'])
    
    # qcut() does not work on DataFrames, but we can use apply()
    # to call the function on each column separately
    print grades_df.apply(convert_grades_curve)
    
def standardize(df):
    '''
    Fill in this function to standardize each column of the given
    DataFrame. To standardize a variable, convert each value to the
    number of standard deviations it is above or below the mean.
    '''
    return None