Reading data from csv file into time series with pandas

My goal is to read EURUSD data (daily) into a time series object where I can easily slice-and-dice, aggregate, and resample the information based on irregular-ish time frames. This is most likely a simple answer. I'm working out of Python for Data Analysis but can't seem to bridge the gap. After downloading and unzipping the data, I run the following code: ``` >>> import pandas as pd >>> df = pd.read_csv('EURUSD_day.csv', parse_dates = {'Timestamp' : ['<DATE>', '<TIME>']}, index_col = 'Timestamp') ``` So far so good. I now have a nice data frame with Timestamps as the index. However, the book implies (p. 295) that I should be able to subset the data, as follows, to look at all the data from the year 2001. ``` >>> df['2001'] ``` But, that doesn't work. Reading this question and answer tells me that I could import Timestamp: ``` >>> from pandas.lib import Timestamp >>> s = df['<CLOSE>'] ``` Which seems to work for a particular day: ``` >>> s[Timestamp('2001-01-04)] 0.9506999999 ``` Yet, the following code yields a single value for my desired range of all data from year 2001. ``` >>> s[Timestamp('2001')] 0.8959 ``` I know I am missing something simple, something basic. Can anyone help? Thank you, Brian