I think this situation is ambiguous since the test
dataframe doesn't have an index that identifies each unique row. If melt
simply stacked the rows with value_vars
SepalLength and SepalWidth, then you can manually create an index to pivot on; and it looks like the result ends up the same as the original:
In [15]: test['index'] = range(len(test) / 2) * 2
In [16]: test[:10]
Out[16]:
Name variable value index
0 Iris-setosa SepalLength 5.1 0
1 Iris-setosa SepalLength 4.9 1
2 Iris-setosa SepalLength 4.7 2
3 Iris-setosa SepalLength 4.6 3
4 Iris-setosa SepalLength 5.0 4
5 Iris-setosa SepalLength 5.4 5
6 Iris-setosa SepalLength 4.6 6
7 Iris-setosa SepalLength 5.0 7
8 Iris-setosa SepalLength 4.4 8
9 Iris-setosa SepalLength 4.9 9
In [17]: test[-10:]
Out[17]:
Name variable value index
290 Iris-virginica SepalWidth 3.1 140
291 Iris-virginica SepalWidth 3.1 141
292 Iris-virginica SepalWidth 2.7 142
293 Iris-virginica SepalWidth 3.2 143
294 Iris-virginica SepalWidth 3.3 144
295 Iris-virginica SepalWidth 3.0 145
296 Iris-virginica SepalWidth 2.5 146
297 Iris-virginica SepalWidth 3.0 147
298 Iris-virginica SepalWidth 3.4 148
299 Iris-virginica SepalWidth 3.0 149
In [18]: df = test.pivot(index='index', columns='variable', values='value')
In [19]: df['Name'] = test['Name']
In [20]: df[:10]
Out[20]:
variable SepalLength SepalWidth Name
index
0 5.1 3.5 Iris-setosa
1 4.9 3.0 Iris-setosa
2 4.7 3.2 Iris-setosa
3 4.6 3.1 Iris-setosa
4 5.0 3.6 Iris-setosa
5 5.4 3.9 Iris-setosa
6 4.6 3.4 Iris-setosa
7 5.0 3.4 Iris-setosa
8 4.4 2.9 Iris-setosa
9 4.9 3.1 Iris-setosa
In [21]: (iris[["SepalLength", "SepalWidth", "Name"]] == df[["SepalLength", "SepalWidth", "Name"]]).all()
Out[21]:
SepalLength True
SepalWidth True
Name True