Adding attribute "keys" to concatenated dataframes - python-3.x

Question

I am concatenating two dataframes along axis = 1 (columns) and try to use "keys" to later be able to distinguish between the columns of the two dataframes that have the same name.
df1 = pd.DataFrame({'tl': ['x1', 'x2', 'x3', 'x4'],
'ff': ['y1', 'y2', 'y3', 'y4'],
'dd': ['z1', 'z2', 'z3', 'z4']},
index=[2016-01-01, 2016-01-02, 2016-01-03, 2016-01-04])
df2 = pd.DataFrame({'tl': ['x1', 'x2', 'x3', 'x4'],
'ff': ['y1', 'y2', 'y3', 'y4'],
'rf': ['z1', 'z2', 'z3', 'z4']},
index=[2016-01-01, 2016-01-02, 2016-01-03, 2016-01-04])
data = pd.concat([df1, df2],keys=['snow','wind'], axis=1, ignore_index=True)
However, when trying to print all the columns belonging to one of the keys as suggested by #YashTD in Pandas add keys while concatenating dataframes at column level
print(comb_data.snow.tl)
I get the following error message:
AttributeError: 'DataFrame' object has no attribute 'snow'
I think, the keys are just not being added to the dataframe, but I don't know why. They also don't show up wenn printing the dataframe head() at they should be suggested by
Pandas add keys while concatenating dataframes at column level
Do you know how to add the keys to the dataframe?

Answer 1

First remove parameter ignore_index=True for MultiIndex in columns and then select by tuple:
data = pd.concat([df1, df2],keys=['snow','wind'], axis=1)
print (data)
snow wind
tl ff dd tl ff rf
2016-01-01 x1 y1 z1 x1 y1 z1
2016-01-02 x2 y2 z2 x2 y2 z2
2016-01-03 x3 y3 z3 x3 y3 z3
2016-01-04 x4 y4 z4 x4 y4 z4
print (data[('snow','tl')])
2016-01-01 x1
2016-01-02 x2
2016-01-03 x3
2016-01-04 x4
Name: (snow, tl), dtype: object