[AstroPy] AstroPy Digest, Vol 81, Issue 12

[Timothy Pickering]

> I thought I'd chime in on the pandas discussion :)
> 
> I'm starting to use pandas a bit more in my day-to-day work. The two features most useful to me are:
> 
> 1) Its file parsers are pretty robust and fast. I always try parsing CSV with pandas first
> 
> 2) For tables tables with lots of categorical data, the grouping functionality is very nice. For example, calculations like "the mean age of each spectral type of star in my catalog" are usually one liners like df.groupby(['spectral_type']).age.mean. I spend a lot of time on the "split-apply-combine" page on the pandas docs (http://pandas.pydata.org/pandas-docs/stable/groupby.html). 
> 
> I won't speculate about whether that's enough an asset to warrant a dependency in astropy. I do agree that lots of other pandas features don't translate as well into astronomy use.

i'll add my R0.02 and second the two points given above.  what i find pandas most useful for is combining different sets of data taken with different timestamps and intervals.  e.g., weather data from different stations, telescope telemetry, seeing monitor telemetry, etc. can all be combined into one table in a sane fashion with proper handling of missing/bad data.  i'm not sure pandas should be an astropy dependency, but they're definitely complementary.  it's pretty well integrated with matplotlib and numpy so you can pull data out of pandas tables and manipulate it in the usual numpy way.  what pandas gives you is a higher level interface for handling and managing the data.  i've successfully chunked through multi-GB datasets with it

tim

--
+-------------------------------------------------------------------+
| T. E. Pickering, Ph.D.         | Southern African Large Telescope |
| SALT Astronomer                |                             SAAO |
| tim at saao.ac.za  (520) 305-9823 |                 Observatory Road |
| tim at salt.ac.za +27-71-551-8281 |   7925 Observatory, South Africa |
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