Can't slice float indices, but can slice integer indices?

[mrjbq7]

Using pandas 0.14, the slicing changed strangely. I can slice rows using start/end with an integer index, but not a float index:

In [18]: df = pandas.DataFrame(np.random.randn(10, 5), index=np.arange(10, 20))

In [19]: df
Out[19]: 
           0         1         2         3         4
10 -1.878123 -0.581537  0.189536  0.173014  0.132059
11  1.229246 -0.988689  0.632404  0.939126 -0.186367
12  0.376735  0.329723  1.480293 -0.209164  0.080897
13  0.461558  0.303541 -0.669196 -1.032077  1.634512
14 -0.972455  0.657357 -0.566609  0.154165 -0.561543
15 -2.502244  1.022540 -1.019376 -0.934582  1.751852
16 -1.875567  0.504288 -0.524922  0.048277 -1.587904
17  0.636652  0.441224 -1.391552  0.650876  0.374673
18 -1.503102  0.822411  1.776667 -0.879583  1.035291
19 -0.620467  0.319855 -0.779280 -0.168827  0.502470

In [20]: df[3:5]
Out[20]: 
           0         1         2         3         4
13  0.461558  0.303541 -0.669196 -1.032077  1.634512
14 -0.972455  0.657357 -0.566609  0.154165 -0.561543

In [21]: df.index = [float(x) for x in df.index]

In [22]: df
Out[22]: 
           0         1         2         3         4
10 -1.878123 -0.581537  0.189536  0.173014  0.132059
11  1.229246 -0.988689  0.632404  0.939126 -0.186367
12  0.376735  0.329723  1.480293 -0.209164  0.080897
13  0.461558  0.303541 -0.669196 -1.032077  1.634512
14 -0.972455  0.657357 -0.566609  0.154165 -0.561543
15 -2.502244  1.022540 -1.019376 -0.934582  1.751852
16 -1.875567  0.504288 -0.524922  0.048277 -1.587904
17  0.636652  0.441224 -1.391552  0.650876  0.374673
18 -1.503102  0.822411  1.776667 -0.879583  1.035291
19 -0.620467  0.319855 -0.779280 -0.168827  0.502470

In [23]: df[3:5]
Out[23]: 
Empty DataFrame
Columns: [0, 1, 2, 3, 4]
Index: []

In [24]: df.index
Out[24]: Float64Index([10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0], dtype='float64')

In [25]: pandas.__version__
Out[25]: '0.14.0'

Was this an intentional change, or a bug?

[hayd]

Is this supposed to act like loc or ix, or something else?

Behaviour of In [20]: df[3:5] seems so wrong/unstable!

[cpcloud]

[] is supposed to act like ix IIRC

[cpcloud]

In which case, this is a bug.

[mrjbq7]

If you're suggesting slicing by rows is undesirable, what is the new way to do that?

[cpcloud]

You should use loc for label-based indexing and use iloc for position based indexing

[jreback]

@mrjbq7 You are slicing the columns

df.loc[10:12] works just fine, or df.iloc[3:5] as well

or df.loc[10.0:12.0]

[mrjbq7]

@jreback the In[20] shows the results of slicing rows, however.

[cpcloud]

@jreback he's slicing the rows

[mrjbq7]

Okay, I can use iloc, however the behavior still seems buggy and inconsistent! :)

[cpcloud]

it's definitely a bug, probably introduced by the small (ish) refactoring of Float64Index to use the float64 dtype instead of object.

[jreback]

In[20] has always been that way, its a fallback indexing