Pandas style object with multi-index

Question

I am formatting a pandas dataframe with styler to highlight columns and format numbers. I also want to apply multi-index for more clear, pleasant and easy to read. Since I a

Answer 1

For the original question:

data.style.background_gradient(cmap=cm,
                               subset=[c for c in data.columns if c[1] == 'A'])

You could check some condition, substring for example:

data.style.background_gradient(cmap=cm,
                               subset=[c for c in data.columns if 'T' in c[0]])

Or pass a list of subcolumns:

data.style.background_gradient(cmap=cm, 
                               subset=[c for c in data.columns if c[1] in ('A', 'C')])

Answer 2

I think you can use pd.IndexSlice[...] method:

data.style.background_gradient(cmap=cm, subset=pd.IndexSlice[:, pd.IndexSlice[:, 'A']])

Demo:

In [5]: data.loc[pd.IndexSlice[:, pd.IndexSlice[:, 'A']]]
Out[5]:
        One
          A
0 -0.808483
1  0.009371
2  0.977138
3 -0.875554
4 -0.052424

In [6]: data
Out[6]:
        One                 Two
          A         B         C         D
0 -0.808483 -2.280683  0.576145  0.649688
1  0.009371  0.721510  1.013764 -0.157493
2  0.977138  1.441392  1.718618 -0.320826
3 -0.875554 -1.060507  1.457075  0.570195
4 -0.052424 -0.742842 -0.203830 -1.202091

in Jupyter:

Answer 3

If you know what the hierarchy of the index is, for example that 'A' is under 'One' you can use a tuple to reference the column.

data.style.background_gradient(cmap=cm, subset=[('One','A')])

Then the table will be displayed as above.

Should you want to style more than one column with a multi-index you need to provide a list of tuples ie

arrays = [np.hstack([['One']*2, ['Two']*2]) , ['A', 'B', 'C', 'D']]
columns = pd.MultiIndex.from_arrays(arrays)
data =  pd.DataFrame(np.random.randn(5, 4), columns=list('ABCD'))
data.columns = columns 
cm = sns.light_palette("green", as_cmap=True)
data.style.background_gradient(cmap=cm, subset=[('One','A'),('Two','C')])

Displays like this

Answer 4

Here, is another way:

data.style.background_gradient(cmap=cm, subset=data.columns.get_loc_level('A', level=1)[0])

Output: