Creating DataFrame with Hierarchical Columns

Question

What is the easiest way to create a DataFrame with hierarchical columns?

I am currently creating a DataFrame from a dict of names -> Series

Answer 1

I know the question is really old but for pandas version 0.19.1 one can use direct dict-initialization:

d = {('a','b'):[1,2,3,4], ('a','c'):[5,6,7,8]}
df = pd.DataFrame(d, index=['r1','r2','r3','r4'])
df.columns.names = ('l1','l2')
print df

l1  a   
l2  b  c
r1  1  5
r2  2  6
r3  3  7
r4  4  8

Answer 2

Im not sure but i think the use of a dict as input for your DF and a MulitIndex dont play well together. Using an array as input instead makes it work.

I often prefer dicts as input though, one way is to set the columns after creating the df:

import pandas as pd

data = {'a': [1,2,3,4], 'b': [10,20,30,40],'c': [100,200,300,400]}
df = pd.DataFrame(np.array(data.values()).T, index=['r1','r2','r3','r4'])

tups = zip(*[['Estimates']*len(data),data.keys()])

df.columns = pd.MultiIndex.from_tuples(tups, names=['l1','l2'])

l1          Estimates         
l2          a   c    b
r1          1  10  100
r2          2  20  200
r3          3  30  300
r4          4  40  400

Or when using an array as input for the df:

data_arr = np.array([[1,2,3,4],[10,20,30,40],[100,200,300,400]])

tups = zip(*[['Estimates']*data_arr.shape[0],['a','b','c'])
df = pd.DataFrame(data_arr.T, index=['r1','r2','r3','r4'], columns=pd.MultiIndex.from_tuples(tups, names=['l1','l2']))

Which gives the same result.

Answer 3

This appears to work:

import pandas as pd

data = {'a': [1,2,3,4], 'b': [10,20,30,40],'c': [100,200,300,400]}

df = pd.concat({"Estimates": pd.DataFrame(data)}, axis=1, names=["l1", "l2"])

l1  Estimates         
l2          a   b    c
0           1  10  100
1           2  20  200
2           3  30  300
3           4  40  400