Add Float64Index class?

[wesm]

Idea from conversation with @CRP in #235

[lodagro]

Same idea came up on this mailing list thread.

[kghose]

Yes, I would voice support for a general index that keeps the original index dtype. I used a float as index (it was time in seconds) and was delighted when everything, including df.plot() worked swimmingly. But then I wasted 30min figuring out why pylab.exp(df.index.values) was failing with the mysterious AttributeError: exp. pandas and Python normally make things so pleasant, but unexpected behavior like this reminds me of my dark days debugging c :(

[baldwint]

@kghose: I agree. I also use indices to store things like the time in seconds (e.g. oscilloscope traces), and am constantly having to do array(df.index.values, dtype=float) in place of a simple df.index.values to get an array that I can use with scipy fitting functions. It's an awkward idiom.

[cpcloud]

+1 here. Matplotlib issue has tripped me up a number of times when I needed to make custom plots.

[jreback]

see http://pandas.pydata.org/pandas-docs/dev/indexing.html#fallback-indexing, it is rarely necessary to actually use a float index; you are often better off served by using a column. The point of the index is to make individual elements faster, e.g. df[1.0], but this is quite tricky; this is the reason for having an issue about this.

[kghose]

Yes, its true because whether two floats are the same depends on precision, but its nice to be able to have that as a time index.

[cpcloud]

In my cases I don't really care about being able to select via get item ish style indexing I usually want to loop over the index series pairs or I have them in frame that I want to show as an image with the index in the columns. The object dtype makes matplotlib show the index to full precision which is really annoying since I then have to go in and format the tick labels by hand. I wholeheartedly agree that float indexes are to be avoided but sometimes they make sense. My cases are mostly plotting issues which only matters when I can't use pandas plotting abilities which thankfully isn't that often.

[jreback]

@kghose consider using a datetime64[ns] index (if you are dealing with time), or as I said, use it as a column; you can do nearly everything you need (with an ocassional set_index/reset_index). what you are you trying to do? as @cpcloud indicates, the only real issue with not having a FloatIndex typed as float is for plotting (w/o manual conversion)

[cpcloud]

General index dtype retention is probably not worth the amount of complexity and code that it would require to do it right. Datetime indexes are your friend. @jreback what about attempting coercion of object indexes when accessing the values attribute?

[jreback]

Something like this is pretty easy (@cpcloud, can't change the way values works or everything breaks)

Is this useful?

In [1]: idx = pd.Index(np.arange(10).astype('float64'))

In [3]: idx
Out[3]: Index([0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0], dtype=object)

In [4]: idx.inferred_values
Out[4]: array([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9.])

[jreback]

Of course for datetimes you get this

In [1]: idx = date_range('20130101',periods=5)

In [2]: idx
Out[2]: 
<class 'pandas.tseries.index.DatetimeIndex'>
[2013-01-01 00:00:00, ..., 2013-01-05 00:00:00]
Length: 5, Freq: D, Timezone: None

In [3]: idx.inferred_values
Out[3]: 
array([1356998400000000000, 1357084800000000000, 1357171200000000000,
       1357257600000000000, 1357344000000000000])

[cpcloud]

Well it's consistent... But it looks like it would only be useful in the float case... What would strings return?

[cpcloud]

Shouldn't dates return array of date time?

[jreback]

I could return anything...(e.g. a datetime64[ns]) numpy array for example, is easy enough, strings will return the same (an object array)..