read_csv(filepath, usecols)

e.g., subnames_no_MPR = pd.read_csv(filepath, delimiter=',', usecols = ['Subject ID', 'Age']);

usecols: Return a subset of the columns. If list-like, all elements must either be positional (i.e. integer indices into the document columns) or strings that correspond to column names provided either by the user in names or inferred from the document header row(s). For example, a valid list-like usecols parameter would be [0, 1, 2] or ['foo','bar', 'baz']. Element order is ignored, so usecols=[0, 1] is the same as [1, 0].

https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html

DataFrame.drop_duplicates(subset, keep)

e.g., subs_MPR = subnames_MPR.drop_duplicates(subset = ['Subject ID', 'Age'], keep = 'first');

subset : column label or sequence of labels, optional

Only consider certain columns for identifying duplicates, by default use all of the columns

keep : {‘first’, ‘last’, False}, default ‘first’

    first : Drop duplicates except for the first occurrence.

    last : Drop duplicates except for the last occurrence.

          False : Drop all duplicates.

http://pandas.pydata.org/pandas-docs/version/0.17/generated/pandas.DataFrame.drop_duplicates.html

https://stackoverflow.com/questions/23667369/drop-all-duplicate-rows-in-python-pandas