I haven't quite got it working, but I think that's because of how I copy/pasted the data. Try this, let me know if it doesn't work.
Using some inspiration from this question
pat = "TimeStamp\tGazePointXLeft\tGazePointYLeft\tValidityLeft\tGazePointXRight\tGazePointYRight\tValidityRight\tGazePointX\tGazePointY\tEvent\n"
with open('rec.txt') as infile:
header, names, tail = infile.read().partition(pat)
names = names.split() # get rid of the tabs here
all_data = tail.split(pat)
res = [pd.read_csv(StringIO(x), sep='\t', names=names) for x in all_data]
We read in the whole file so this won't work for huge files, and then partition it based on the known line giving the column names. tail
is just a string with the rest of the data so we can split that, again based on the names. There may be a better way than using StringIO, but this should work.
I'm note sure how you want to join the separate blocks together, but this leaves them as a list. You can concat from there however you desire.
For larger files you might want to write a generator to read until you hit the column names and write a new file until you hit them again. Then read those in separately using something like Andy's answer.
A separate question from how to work with the multiple blocks. Assuming you've got the list of Dataframe
s, which I've called res
, you can use pandas' concat to join them together into a single DataFrame with a MultiIndex (also see the link Andy posted).
In [122]: df = pd.concat(res, axis=1, keys=['a', 'b', 'c']) # Use whatever makes sense for the keys
In [123]: df.xs('TimeStamp', level=1, axis=1)
Out[123]:
a b c
0 NaN NaN NaN
1 0.0 0.0 0.0
2 3.3 3.3 3.3
3 6.6 6.6 6.6