Python Pandas pivot one column and unpivot X columns

Question

I'm using pandas dataframes and have two major things to do. I don't know which is the best order but I need to turn Food column into multiple columns titled pizza , burger , and hot dog as column names (pretty much pivot) and then unpivot columns pd.iloc[:,2:] so that the first date column through X-number of date columns is turned into a single column.

Steps taken so far are to split the df into 3 separate dfs (one is just Name , two is Food and pivot, three is unstack and then reset index on each). I don't think this is the correct approach. I just need the basic workflow to collapse into a single large df (relational dfs won't work for this specific task).

SAMPLE DATA:

Name    Food    1/1/2018    2/1/2018    3/1/2018    4/1/2018
Mike    pizza   1           0           1           1
Mike    burger  0           3           0           0
Mike    hot dog 0           0           0           0
Bob     pizza   0           1           0           0
Bob     burger  2           0           2           2
Bob     hot dog 1.5         0           1.5         1.5

WHAT I NEED:

Name                pizza   burger  hot dog
Mike    1/1/2018    1       0       0
Mike    2/1/2018    0       3       0
Mike    3/1/2018    1       0       0
Mike    4/1/2018    1       0       0
Bob     1/1/2018    0       2       1.5
Bob     2/1/2018    1       0       0
Bob     3/1/2018    0       2       1.5
Bob     4/1/2018    0       2       1.5

Answer 1

Try like this:

df.set_index(['Name', 'Food']).stack().unstack('Food')

Food           burger  hot dog  pizza
Name                                 
Bob  1/1/2018     2.0      1.5    0.0
     2/1/2018     0.0      0.0    1.0
     3/1/2018     2.0      1.5    0.0
     4/1/2018     2.0      1.5    0.0
Mike 1/1/2018     0.0      0.0    1.0
     2/1/2018     3.0      0.0    0.0
     3/1/2018     0.0      0.0    1.0
     4/1/2018     0.0      0.0    1.0

If formatting is an issue, just reset the index and then rename your columns to appropriate names:

df.set_index(['Name', 'Food']).stack().unstack('Food').reset_index().rename(columns={'level_1':'date'})

Food  Name      date  burger  hot dog  pizza
0      Bob  1/1/2018     2.0      1.5    0.0
1      Bob  2/1/2018     0.0      0.0    1.0
2      Bob  3/1/2018     2.0      1.5    0.0
3      Bob  4/1/2018     2.0      1.5    0.0
4     Mike  1/1/2018     0.0      0.0    1.0
5     Mike  2/1/2018     3.0      0.0    0.0
6     Mike  3/1/2018     0.0      0.0    1.0
7     Mike  4/1/2018     0.0      0.0    1.0

Answer 2

Since you mention 'unpivot' ( melt )

df.melt(['Name','Food']).pivot_table(index=['Name','variable'],columns='Food',values='value')
Out[752]: 
Food           burger  hotdog  pizza
Name variable                       
Bob  1/1/2018     2.0     1.5    0.0
     2/1/2018     0.0     0.0    1.0
     3/1/2018     2.0     1.5    0.0
     4/1/2018     2.0     1.5    0.0
Mike 1/1/2018     0.0     0.0    1.0
     2/1/2018     3.0     0.0    0.0
     3/1/2018     0.0     0.0    1.0
     4/1/2018     0.0     0.0    1.0