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

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Question :

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 2×2 matrix (the output from the previous set is 3×4)? 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”?

Asked By: gt6989b

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Answer #1:

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
Answered By: Roman Pekar

Answer #2:

I submit that it is better to leave your data stacked as it is:

df = pandas.DataFrame(data, columns=['R_Number', 'C_Number', 'Avg', 'Std'])

# Possibly also this if these can always be the indexes:
# df = df.set_index(['R_Number', 'C_Number'])

Then it’s a bit more intuitive to say

df.set_index(['R_Number', 'C_Number']).Avg.unstack(level=1)

This way it is implicit that you’re seeking to reshape the averages, or the standard deviations. Whereas, just using pivot, it’s purely based on column convention as to what semantic entity it is that you are reshaping.

Answered By: ely

Answer #3:

This is what I expected to see when I came to this question:

#!/usr/bin/env python

import pandas as pd


df = pd.DataFrame([(1, 2, 3, 4),
                   (5, 6, 7, 8),
                   (9, 0, 1, 2),
                   (3, 4, 5, 6)],
                  columns=list('abcd'),
                  index=['India', 'France', 'England', 'Germany'])
print(df)

gives

         a  b  c  d
India    1  2  3  4
France   5  6  7  8
England  9  0  1  2
Germany  3  4  5  6
Answered By: Martin Thoma

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