pricing = get_pricing(['AAPL', 'MSFT'], 
                      start_date='2014-01-01', 
                      end_date='2014-01-07', 
                      frequency='minute',
                      fields='price')

pricing.head(10)

from pandas.tseries.tools import normalize_date

def my_grouper(ts):
    "Function to apply to the index of the DataFrame to break it into groups."
    # Returns midnight of the supplied date.
    return normalize_date(ts)


def first_thirty_minutes(frame):
    "Function to apply to the resulting groups."
    return frame.iloc[:30]

data = pricing.groupby(my_grouper).apply(first_thirty_minutes)
data.head(40)

from pandas import Timestamp
# This gives us the first thirty minutes of January 3rd.
data.loc[Timestamp('2014-01-03', tz='UTC')]

data.xs(Timestamp('2014-01-03 14:58:00', tz='UTC'), level=1)

data_copy = data.copy()
data_copy.index = data_copy.index.droplevel(0)
data_copy.head()