argmax?

[jseabold]

Before I make a PR, is there an existing argmax anywhere? I often find myself wanting an easier way of asking for the argmax in a series, and I don't see a way to do it easily. From experience, this doesn't mean it doesn't exist.

[jreback]

Series.idxmax

[jseabold]

Thought so. Odd name. Thanks.

[mt-digital]

An idea for a possibly more memorable name: findmax, a la

array_max_idx = matplotlib.mlab.find( max( array ) )

On Tue, Mar 5, 2013 at 7:38 AM, Skipper Seabold [email protected]:

Thought so. Odd name.

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Matthew Turner
Data Engineer
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[lodagro]

The names were originally argmin and argmax, but were changed to idxmin and idxmax to avoid overloading the ndarray functions (see also #286).

[jseabold]

That makes sense. I didn't even see argmax/argmin before, though how I missed these is a mystery because I swear I looked...

[jreback]

the original argument for not naming argmax is kind of bogus; there are many methods that override the numpy methods on purpose (e.g. to handle nans)..... e.g.where, abs, astype, reshape, repeat, unique, cumsum/prod, diff, clip, sort....etc

[mt-digital]

argmin and argmax are pretty standard imho, esp in optimization where
finding the argmin (argmax) of a function is the problem itself (rando
search turned up http://ttic.uchicago.edu/~karthik/convex.pdf). I think
argmin and argmax are much more appropriate names. I also think the name
is intuitive--they are the argument (i.e. to a function, or an index to an
array) that maximizes that thing.

On Tue, Mar 5, 2013 at 1:13 PM, jreback [email protected] wrote:

the original argument for not naming argmax is kind of bogus; there are
many methods that override the numpy methods on purpose (e.g. to handle
nans).....e.g.where, abs, astype, reshape, repeat, unique, cumsum/prod,
diff, clip, sort....etc

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Matthew Turner
Data Engineer
Economic Modeling Specialists, Int'l
585-350-8649 (mobile)��

[email protected]

[lodagro]

@jreback idxmin/idxmax return label whereas argmin/argmax return location. I think this was the main reason why Wes did not want to override. Since override is not done, both exist - at least on Series, same names idxmin/idxmax are kept on DataFrame.

In [33]: s = pd.Series([10, 30, 20], list('abc'))

In [34]: s
Out[34]:
a    10
b    30
c    20

In [35]: s.idxmax()
Out[35]: 'b'

In [36]: s.argmax()
Out[36]: 1

[jreback]

I was using an integerr index, so you are correct :)

in any event, I believe np.argmin of a series of datetime64[ns] with NaT is wrong (in 0.11-dev), worked in 0.10.1 becuase shifted datetime64[ns] was object dtype....

so I think we SHOULD add support for argmax/min directly

[lodagro]

created #2982 for datetime64[ns]