DOC: GH2072 add example and warning on fallback indexing for float indexes.
https://github.com/pydata/pandas/issues/2072#issuecomment-9454035

can you make the doc lines wrap, no more than 80 char width. (I get flagged for this all the time!)

Looks good otherwise

@y-p ?
 also, maybe add an example of using the 'float' selection, e.g

say A is your previous indexer column
something like this? 

```
df[(df.A>=1.0) & (df.A<2.0)]
```
 `instead use use` - needs fixing.

**very bad idea** is not the best pharsing IMO. explain the problem. explain the caveats, 
recommend a better way.

Also, I can't parse:
`If you do have a float index, an integer label will fallback to`
it seems disjointed.

I haven't followed this issue, so i'm looking at the doc with fresh eyes... ;)
 Oh, re 80 char limit on documentation - stop the PEP8 insanity! it's not even code!
 Good point @y-p. How about this?
 looks good to me (and now you are taking style points from @y-p )....!
 Hey- if you don't want pedantry, don't ask for my opinion on docs.
 and if you wouldn't mind, pls rebase into 1 commit 

```
git rebase -i <first commit>
```

change the pick to fixup (or squash if you want) for all but the first

then

```
git push <your fork> <this branch name> -f
```
 @jreback - done. I wondered how that worked in git - thanks.
 this looks great

I'll leave open for a couple of days in case you want to change anything
(I find that I read doc updates and then I edit them the next day)
 yep, this is good.
 @adamgreenhall 
in http://pandas.pydata.org/pandas-docs/dev/indexing.html#fallback-indexing

I realized that the last part of your example is correct, but not illustrating what _I think_ you want.

`df_new.index` is the _index_ in this case, but you want to illustrate `df_new['index']`

```
In [14]: df_new
Out[14]: 
   index         A         B         C         D
0      1 -1.164567  0.054407  0.619462  1.936210
1      2  0.047871 -1.776328 -0.590250 -0.399167
2      3  0.214890 -0.593469 -1.115490  0.259086
3      4  0.089140 -0.998506  0.514325 -0.719256
```

```
In [15]: df_new[df_new.index==1.0]
Out[15]: 
   index         A         B        C         D
1      2  0.047871 -1.776328 -0.59025 -0.399167

In [16]: df_new[df_new['index']==1.0]
Out[16]: 
   index         A         B         C        D
0      1 -1.164567  0.054407  0.619462  1.93621
```

Same here

```
In [17]: df_new[(df_new['index']>=1.0) & (df_new['index']<2)]
Out[17]: 
   index         A         B         C        D
0      1 -1.164567  0.054407  0.619462  1.93621

```

Doing something like this might make the example more clear (then you can use `df_new.my_float_index`
that's my 2c

```
df_new = df.reset_index().rename(columns = dict(index = 'my_float_index'))
```
