Question

I have a pandas dataframe that I filled with this:

import pandas.io.data as web
test = web.get_data_yahoo('QQQ')

The dataframe looks like this in iPython:

In [13]:  test
Out[13]:
    <class 'pandas.core.frame.DataFrame'>
    DatetimeIndex: 729 entries, 2010-01-04 00:00:00 to 2012-11-23 00:00:00
    Data columns:
    Open         729  non-null values
    High         729  non-null values
    Low          729  non-null values
    Close        729  non-null values
    Volume       729  non-null values
    Adj Close    729  non-null values
    dtypes: float64(5), int64(1)

When I divide one column by another, I get a float64 result that has a satisfactory number of decimal places. I can even divide one column by another column offset by one, for instance test.Open[1:]/test.Close[:], and get a satisfactory number of decimal places. When I divide a column by itself offset, however, I get just 1:

In [83]: test.Open[1:] / test.Close[:]
Out[83]:

    Date
    2010-01-04         NaN
    2010-01-05    0.999354
    2010-01-06    1.005635
    2010-01-07    1.000866
    2010-01-08    0.989689
    2010-01-11    1.005393
...
In [84]: test.Open[1:] / test.Open[:]
Out[84]:
    Date
    2010-01-04   NaN
    2010-01-05     1
    2010-01-06     1
    2010-01-07     1
    2010-01-08     1
    2010-01-11     1

I’m probably missing something simple. What do I need to do in order to get a useful value out of that sort of calculation? Thanks in advance for the assistance.

Answer 1

If you’re looking to do operations between the column and lagged values, you should be doing something like test.Open / test.Open.shift().
shift realigns the data and takes an optional number of periods.