# Construct pandas DataFrame from list of tuples of (row,col,values)

I have a list of tuples like

```
data = [
('r1', 'c1', avg11, stdev11),
('r1', 'c2', avg12, stdev12),
('r2', 'c1', avg21, stdev21),
('r2', 'c2', avg22, stdev22)
]
```

and I would like to put them into a pandas DataFrame with rows named by the first column and columns named by the 2nd column. It seems the way to take care of the row names is something like `pandas.DataFrame([x[1:] for x in data], index = [x[0] for x in data])`

but how do I take care of the columns to get a 2x2 matrix (the output from the previous set is 3x4)? Is there a more intelligent way of taking care of row labels as well, instead of explicitly omitting them?

**EDIT** It seems I will need 2 DataFrames - one for averages and one for standard deviations, is that correct? Or can I store a list of values in each "cell"?

You can pivot your DataFrame after creating:

```
>>> df = pd.DataFrame(data)
>>> df.pivot(index=0, columns=1, values=2)
# avg DataFrame
1 c1 c2
0
r1 avg11 avg12
r2 avg21 avg22
>>> df.pivot(index=0, columns=1, values=3)
# stdev DataFrame
1 c1 c2
0
r1 stdev11 stdev12
r2 stdev21 stdev22
```

From: stackoverflow.com/q/19961490

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