Append a level to a pandas MultiIndex

问题

Say I have a pandas dataframe with three indices 'a', 'b' and 'c' - how can I add a fourth index from an array and set its name to 'd' at the same time?

This works:

df.set_index(fourth_index, append=True, inplace=True)
df.index.set_names(['a','b','c','d'], inplace=True)

But I'm looking for something that doesn't require me to also name the first three indices again, e.g. (this doesn't work):

df.set_index({'d': fourth_index}, append=True, inplace=True)

Am I missing some function here?

回答1:

Add fourth_index as a column and then call set_index. The name is preserved.

df = df.assign(d=fourth_index).set_index('d', append=True)

Note, if you're worried about memory, what you're doing is fine as is. No point sacrificing performance for a fewer characters.

Demo

df
          a   b   c   d
l1  l2                 
bar one  24  13   8   9
    two  11  30   7  23
baz one  21  31  12  30
    two   2   5  19  24
foo one  15  18   3  16
    two   2  24  28  11
qux one  23   9   6  12
    two  29  28  11  21

df.assign(l3=1).set_index('l3', append=True)

             a   b   c   d
l1  l2  l3                
bar one 1   24  13   8   9
    two 1   11  30   7  23
baz one 1   21  31  12  30
    two 1    2   5  19  24
foo one 1   15  18   3  16
    two 1    2  24  28  11
qux one 1   23   9   6  12
    two 1   29  28  11  21

回答2:

Why not just save the names of the previous values from before i.e.

old_names = df.index.names
df.set_index(fourth_index, append=True, inplace=True)
df.index.set_names(old_names + ['d'], inplace=True)

This then keeps the benefits of the good performance and doesn't require you to re-type the old names.

来源：https://stackoverflow.com/questions/50609175/append-a-level-to-a-pandas-multiindex

标签

python

pandas

indexing

multi-index