nanmin no longer works with series

[rstoneback]

Using NumPy's nanmin on a pandas Series worked as expected, now however the Series is returned with each element set to the minimum, rather than just getting the minimum. Not sure if this is a Numpy of Pandas issue.

np.nanmin(pds.Series([1,2,3,4]))
Out[14]: 
0    1
1    1
2    1
3    1
dtype: int64

np.min(pds.Series([1,2,3,4]))
Out[15]: 1

[jreback]

its a numpy issue I think. It doesn't respect an array-like but non-ndarray subclass.

You can use s.min() is better/faster anyhow
or np.nanmin(s.values)

[jreback]

you should always post the versions of python/pandas/numpy you are using FYI

closing as a numpy issue

[tacaswell]

cross posted as numpy/numpy#5114

I would argue that this is a pandas issue as it was changes on this side that broke things, not changes on the numpy side.

[jreback]

@tacaswell disagree, if np.min acts one way and np.nanmin acts another?

This has to do with how numpy is or IS NOT following ndarray-like objects, e.g. calling (or not) __array_wrap__

[rstoneback]

I reported the same issue to numpy and the immediate response was that it is likely a pandas issue.

On Sep 24, 2014, at 3:14 PM, jreback <notifications@git.colasdn.topmailto:notifications@github.com> wrote:

you should always post the versions of python/pandas/numpy you are using FYI

closing as a numpy issue

—
Reply to this email directly or view it on GitHubhttps://github.com//issues/8383#issuecomment-56730533.

[jreback]

@rstoneback I know, read my comments. It is a numpy abusing the API issue. We didnt' change anything. They added the function and it has a different behavior/API than other functions.

In any event, you should simply use .min() and pass skipna=True if you don't want to skip NA's (the default is skipna=False).

[rstoneback]

My last comment about reporting the issue to numpy didn't post for some time (I replied to github email). I've switched to using the pandas functions.

[rstoneback]

Using the series min function works great. Thanks for the quick responses.

Cheers.

[dalejung]

np.min works because it implicitly calls Series.min. If you remove the pd.Series.min you get the same behavior as nanmin.

s = pd.Series(range(10))
np.nanmin(s)

In the above example, __array_wrap__ actually gets a 0 ndim np scalar but we elongate it by maintaining index. I guess we're supposed to have a check for scalar types in __array_wrap__?

http://nbviewer.ipython.org/gist/dalejung/56b73c3cfd71b02ec414 also an ndarray subclass example that replicates the same issue we're having.

[jreback]

you can try changing array_wrap to not wrap scalars
prob easier just to define nanmin = min

[dalejung]

Yeah, I'm gonna see about not wrapping scalars. Want to poke around and figure out the history on why a.min() exists.

[jreback]

@dalejung if you think best soln here is to simply add nanmim=min or change __array_wrap__, pls ping and we can reopen this issue (or if not, can defer to numpy to 'fix')