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

Question

I have a list of tuples like

data = [
(\'r1\', \'c1\', avg11, stdev11),
(\'r1\', \'c2\', avg12, stdev12),
(\'r2\', \'c1\', avg21, stdev21),
(\'r2\', \'c2\',          


        

Answer 1

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.

Answer 2

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

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