Is there a convenient way to implement a function make_dataframe
, used as follows
mydict = {
('tom', 'gray') : [1,2,3,4,5],
('bill', 'ginger') : [6,7,8,9,10],
}
make_dataframe(mydict, tupleLabels=['catname', 'catcolor'], valueLabel='weight')
Expected result
| catname | catcolor | weight |
| tom | gray | 1 |
| tom | gray | 2 |
| tom | gray | 3 |
| tom | gray | 4 |
| tom | gray | 5 |
| bill | ginger | 6 |
| bill | ginger | 7 |
| bill | ginger | 8 |
| bill | ginger | 9 |
| bill | ginger | 10 |
It does not sound too difficult, I just don't want to reinvent the wheel
You can create your own function using dataframe unstack
after renaming the labels using rename_axis
:
def make_dataframe(dictionary , tupleLabels , valueLabel):
return (pd.DataFrame(dictionary).rename_axis(tupleLabels,axis=1)
.unstack().reset_index(tupleLabels,name=valueLabel))
out = make_dataframe(mydict, tupleLabels=['catname', 'catcolor'], valueLabel='weight')
print(out)
catname catcolor weight
0 tom gray 1
1 tom gray 2
2 tom gray 3
3 tom gray 4
4 tom gray 5
0 bill ginger 6
1 bill ginger 7
2 bill ginger 8
3 bill ginger 9
4 bill ginger 10
Your dictionary is misformatted for easy conversion to a Pandas DataFrame.
I suggest doing the following:
mydict = {
('tom', 'gray') : [1,2,3,4,5],
('bill', 'ginger') : [6,7,8,9,10],
}
l = [ [ k[0], k[1], val ] for k, v in mydict.items() for val in v ]
df = pd.DataFrame(l, columns=['catname', 'catcolor', 'weight'])
Which yields:
catname catcolor weight
0 tom gray 1
1 tom gray 2
2 tom gray 3
3 tom gray 4
4 tom gray 5
5 bill ginger 6
6 bill ginger 7
7 bill ginger 8
8 bill ginger 9
9 bill ginger 10
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