How to handle incoming real time data with python pandas

Which is the most recommended/pythonic way of handling live incoming data with pandas?

Every few seconds I'm receiving a data point in the format below:

{'time' :'2013-01-01 00:00:00', 'stock' : 'BLAH',
 'high' : 4.0, 'low' : 3.0, 'open' : 2.0, 'close' : 1.0}

I would like to append it to an existing DataFrame and then run some analysis on it.

The problem is, just appending rows with DataFrame.append can lead to performance issues with all that copying.

Things I've tried:

A few people suggested preallocating a big DataFrame and updating it as data comes in:

In [1]: index = pd.DatetimeIndex(start='2013-01-01 00:00:00', freq='S', periods=5)

In [2]: columns = ['high', 'low', 'open', 'close']

In [3]: df = pd.DataFrame(index=t, columns=columns)

In [4]: df
Out[4]: 
                    high  low open close
2013-01-01 00:00:00  NaN  NaN  NaN   NaN
2013-01-01 00:00:01  NaN  NaN  NaN   NaN
2013-01-01 00:00:02  NaN  NaN  NaN   NaN
2013-01-01 00:00:03  NaN  NaN  NaN   NaN
2013-01-01 00:00:04  NaN  NaN  NaN   NaN

In [5]: data = {'time' :'2013-01-01 00:00:02', 'stock' : 'BLAH', 'high' : 4.0, 'low' : 3.0, 'open' : 2.0, 'close' : 1.0}

In [6]: data_ = pd.Series(data)

In [7]: df.loc[data['time']] = data_

In [8]: df
Out[8]: 
                    high  low open close
2013-01-01 00:00:00  NaN  NaN  NaN   NaN
2013-01-01 00:00:01  NaN  NaN  NaN   NaN
2013-01-01 00:00:02    4    3    2     1
2013-01-01 00:00:03  NaN  NaN  NaN   NaN
2013-01-01 00:00:04  NaN  NaN  NaN   NaN

The other alternative is building a list of dicts. Simply appending the incoming data to a list and slicing it into smaller DataFrames to do the work.

In [9]: ls = []

In [10]: for n in range(5):
   .....:     # Naive stuff ahead =)
   .....:     time = '2013-01-01 00:00:0' + str(n)
   .....:     d = {'time' : time, 'stock' : 'BLAH', 'high' : np.random.rand()*10, 'low' : np.random.rand()*10, 'open' : np.random.rand()*10, 'close' : np.random.rand()*10}
   .....:     ls.append(d)

In [11]: df = pd.DataFrame(ls[1:3]).set_index('time')

In [12]: df
Out[12]: 
                        close      high       low      open stock
time                                                             
2013-01-01 00:00:01  3.270078  1.008289  7.486118  2.180683  BLAH
2013-01-01 00:00:02  3.883586  2.215645  0.051799  2.310823  BLAH

or something like that, maybe processing the input a little bit more.