import numpy as np
import pandas as pd

# generate a 1-week time series
dates = pd.date_range(start="2014-01-01 00:00", end="2014-01-07 00:00", freq="30min")
df = pd.DataFrame(np.random.randn(len(dates),3), index=dates, columns=("A", "B", "C"))

# generate some artificial gaps
df.ix["2014-01-04 10:00":"2014-01-04 11:00", "A"] = np.nan
df.ix["2014-01-04 12:30":"2014-01-04 14:00", "B"] = np.nan
df.ix["2014-01-04 09:30":"2014-01-04 15:00", "C"] = np.nan

print df["2014-01-04 08:00":"2014-01-04 16:00"]

                            A         B         C
2014-01-04 08:00:00  0.675720  2.186484 -0.033969
2014-01-04 08:30:00 -0.897217  1.332437 -2.618197
2014-01-04 09:00:00  0.299395  0.837023  1.346117
2014-01-04 09:30:00  0.223051  0.913047       NaN
2014-01-04 10:00:00       NaN  1.395480       NaN
2014-01-04 10:30:00       NaN -0.800921       NaN
2014-01-04 11:00:00       NaN -0.932760       NaN
2014-01-04 11:30:00  0.057219 -0.071280       NaN
2014-01-04 12:00:00  0.215810 -1.099531       NaN
2014-01-04 12:30:00 -0.532563       NaN       NaN
2014-01-04 13:00:00 -0.697872       NaN       NaN
2014-01-04 13:30:00 -0.028541       NaN       NaN
2014-01-04 14:00:00 -0.073426       NaN       NaN
2014-01-04 14:30:00 -1.187419  0.221636       NaN
2014-01-04 15:00:00  1.802449  0.144715       NaN
2014-01-04 15:30:00  0.446615  1.013915 -1.813272
2014-01-04 16:00:00 -0.410670  1.265309 -0.198607

[17 rows x 3 columns]