1. Read in the college dataset and select just those columns with undergraduate race percentage information:

>>> college = pd.read_csv('data/college.csv', index_col='INSTNM')
>>> college_ugds = college.filter(like='UGDS_')
>>> college_ugds.head()

2. Use the idxmax method to get the column name with the highest race percentage for each row:

>>> highest_percentage_race = college_ugds.idxmax(axis='columns')
>>> highest_percentage_race.head()
INSTNM
Alabama A & M University               UGDS_BLACK
University of Alabama at Birmingham    UGDS_WHITE
Amridge University                     UGDS_BLACK
University of Alabama in Huntsville    UGDS_WHITE
Alabama State University               UGDS_BLACK
dtype: object

3. Use the value_counts method to return the distribution of maximum occurrences:

>>> highest_percentage_race.value_counts(normalize=True)
UGDS_WHITE    0.670352
UGDS_BLACK    0.151586
UGDS_HISP     0.129473
UGDS_UNKN     0.023422
UGDS_ASIAN    0.012074
UGDS_AIAN     0.006110
UGDS_NRA      0.004073
UGDS_NHPI     0.001746
UGDS_2MOR     0.001164
dtype: float64