Pandas: Assign MultiIndex Column from DataFrame

I have a DataFrame with multiIndex columns. Suppose it is this:

index = pd.MultiIndex.from_tuples([('one', 'a'), ('one', 'b'),
('two', 'a'), ('two', 'b')])
df = pd.DataFrame('col': np.arange(1.0, 5.0), index=index)

df = df.unstack(1)


(I know this definition could be more direct). I now want to set a new level 0 column based on a DataFrame. For example

df['col2'] = df['col'].applymap(lambda x: int(x < 3))


This does not work. The only method I have found so far is to add each column seperately:
Pandas: add a column to a multiindex column dataframe
, or some sort of convoluted joining process.

The desired result is a new level 0 column 'col2' with two level 1 subcolumns: 'a' and 'b'

'col2'

'a'

'b'

Any help would be much appreciated, Thank you.

1 Answer
1

I believe need solution with no unstack and stack - filter by boolean indexing, rename values for avoid duplicates and last use DataFrame.append:

unstack

stack

boolean indexing

rename

DataFrame.append

df2 = df[df['col'] < 3].rename('one':'one1', 'two':'two1', level=0)
print (df2)
col
one1 a 1.0
b 2.0

df = df.append(df2)
print (df)
col
one a 1.0
b 2.0
two a 3.0
b 4.0
one1 a 1.0
b 2.0


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