reorder panda data frame columns vertical

Question

I'm a bit new to panda and have some diabetic data that i would like to reorder.

I'd like to copy the data from column 'wakeup' through '23:00:00', and put this data vertical under each other so I would get a new dataframe column:

5.6  
8.1  
9.9   
6.3  
4.1  
13.3  
NAN  
3.9  
3.3  
6.8  
.....etc

I think `.melt`, to preserve `NaN`. There are examples in https://stackoverflow.com/questions/34376053/pandas-dataframe-stack-multiple-column-values-into-single-column — ALollz, Feb 14 '19 at 22:31

score 0 · Answer 1 · answered Feb 14 '19 at 22:51

I'm assuming the data is in a dataframe already. You can index the columns you want and then use melt as suggested. Without any parameters, melt will 'stack' all your data into one column of a new dataframe. There's another column created to identify the original column names, but you can drop that if needed.

df.loc[:, 'wakeup':'23:00:00'].melt()

    variable  value
0     wakeup    5.6
1     wakeup    8.1
2     wakeup    9.9
3     wakeup    6.3
4     wakeup    4.1
5     wakeup   13.3
6     wakeup    NAN
7   09:30:00    3.9
8   09:30:00    3.3
9   09:30:00    6.8
...

You mention you want this as another column, but there's no way to sensibly add it into your existing dataframe. The shape likely won't match also.

your right on that it should indeed be in a new dataset, note though that in your list the wakeups are all below each, its not what i want. — user613326, Feb 15 '19 at 17:14

score 0 · Accepted Answer · answered Feb 15 '19 at 18:42

Solved it myself finally took me quite some time. Notice here the orginal data was in df1 result in dfAllMeasurements

dfAllMeasurements = df1.loc[:, 'weekday':'23:00:00']
temp = dfAllMeasurements.set_index('weekday','ID').stack(dropna=False) #dropna = keeping NAN 
dfAllMeasurements = temp.reset_index(drop=False, level=0).reset_index()

reorder panda data frame columns vertical

2 Answers2