Appending Multi-index column headers to existing dataframe

Question

I'm looking to append a multi-index column headers to an existing dataframe, this is my current dataframe.

Name = pd.Series(['John','Paul','Sarah'])
Grades = pd.Series(['A','A','B'])
HumanGender = pd.Series(['M','M','F'])

DogName = pd.Series(['Rocko','Oreo','Cosmo'])
Breed = pd.Series(['Bulldog','Poodle','Golden Retriever'])
Age = pd.Series([2,5,4])
DogGender = pd.Series(['F','F','F'])

SchoolName = pd.Series(['NYU','UCLA','UCSD'])
Location = pd.Series(['New York','Los Angeles','San Diego'])

df = (pd.DataFrame({'Name':Name,'Grades':Grades,'HumanGender':HumanGender,'DogName':DogName,'Breed':Breed,
'Age':Age,'DogGender':DogGender,'SchoolName':SchoolName,'Location':Location}))

I want add 3 columns on top of the existing columns I already have. For example, columns [0,1,2,3] should be labeled 'People', columns [4,5,6] should be labeled 'Dogs', and columns [7,8] should be labeled 'Schools'. In the final result, it should be 3 columns on top of 9 columns. Thanks!

Quang Hoang · Accepted Answer · 2020-04-18 05:19:09Z

2

IIUC, you can do:

newlevel = ['People']*4 + ['Dogs']*3 + ['Schools']*2
df.columns = pd.MultiIndex.from_tuples([*zip(newlevel, df.columns)])

Note [*zip(newlevel, df.columns)] is equivalent to

[(a,b) for a,b in zip(new_level, df.columns)]

edited Apr 18, 2020 at 5:19

answered Apr 18, 2020 at 5:13

Quang Hoang

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1 Comment

dko512 Over a year ago

Hey, yes this is what I meant, can you explain what the " * " does in the zip?

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