How to work with data indexed by floats in pandas

Question

I use pandas DataFrame with hierarhical index, and in one particular case it is indexed by float values.

Here is example:

example_data = [
    {'a': 1.2, 'b':30, 'v':123},
    {'a': 1.2, 'b':60, 'v':1234},
    {'a': 3, 'b':30, 'v':12345},
    {'a': 3, 'b':60, 'v':123456},
]
frame = pd.DataFrame(example_data)
frame.set_index(['a', 'b'])

Now I'd like to use partial indexing to select frame with a==1.2 and then display it. Documentation shows how to do this for string index, but this approach obviously doesn't work for floats, irrevelant whether I try frame.loc[1.2] i get error about 1.2 being imporper for Int64Index which is obviously true since i use float for indexing.

Is there any way to work with float index in pandas? How can I fix my Hierarhical Index?

Actual error message was:

TypeError: the label [1.2] is not a proper indexer for this index type (Int64Index)

Answer 1

Pandas has no issue if the index level is a single level so not a multi index:

In [178]:

frame = frame.set_index(['a'])
frame.loc[1.2]
Out[178]:
      b     v
a            
1.2  30   123
1.2  60  1234

If you do have a multi-index then you can get generate a mask using the index level 0 (the first) and use this to select the values:

In [180]:

mask = frame.index.get_level_values(0)
frame.loc[mask == 1.2]
Out[180]:
           v
a   b       
1.2 30   123
    60  1234

The mask itself contains all the level 0 values for each row:

In [181]:

mask
Out[181]:
Float64Index([1.2, 1.2, 3.0, 3.0], dtype='float64')

It is better and more explicit to specify the level using the name:

mask = frame.index.get_level_values('a')