# [SciPy-User] stats.pearsonr divide by zero warning

Skipper Seabold jsseabold@gmail....
Mon Aug 9 15:29:23 CDT 2010

```On Mon, Aug 9, 2010 at 4:18 PM,  <josef.pktd@gmail.com> wrote:
> On Mon, Aug 9, 2010 at 3:46 PM,  <josef.pktd@gmail.com> wrote:
>> On Mon, Aug 9, 2010 at 3:19 PM, Zachary Pincus <zachary.pincus@yale.edu> wrote:
>>> Hello,
>>>
>>> I just svn-up'd scipy, and now find that stats.pearsonr is causing
>>> divide-by-zero warnings foolishly.
>>>
>>> the function contains the following stanzas:
>>>
>>>     rs = np.corrcoef(ar,br,rowvar=axisout)
>>>
>>>     t = rs * np.sqrt((n-2) / ((rs+1.0)*(1.0-rs)))
>>>     prob = distributions.t.sf(np.abs(t),n-2)*2
>>>
>>>     if rs.shape == (2,2):
>>>         return rs[1,0], prob[1,0]
>>>     else:
>>>         return rs, prob
>>>
>>> Given that the diagonal of the correlation matrix returned by corrcoef
>>> will *always* be 1s, the t matrix will have divide-by-zero issues on
>>> the diagonal, and give inf values -- which get zero values for the t-
>>> distribution's survival function, so everything's fine, output-wise.
>>> Presumably, though, the t-calculating line should be flanked by err =
>>> np.seterr(divide='ignore') / np.seterr(**err), right?
>>>
>>> Should I add a bug in the tracker? Someone want to just commit this fix?
>>
>> I guess you mean spearmanr,  pearsonr hasn't been rewritten as far as I can see.
>>
>> The old trick (still used in pearsonr) was to add TINY in the
>> calculation of the test statistic.
>> Maybe we should add TINY to the diagonal, which would keep a zero
>> division warning if any of the series are perfectly correlated.
>>
>> seterr is also fine with me.
>>
>> a ticket is always good, at least for the record so we know what to
>> watch out for. I have warnings turned off globally, so no zero
>> division problems for me.
>>
>> np.corrcoef might throw a warning if there is zero variance, but I'm
>> not sure this applies in this case
>>
>> Josef
>
> just a follow-up because I think there is a similar case in the
> contingency table code
>
> mut_inf = np.nansum(self.probability * np.log(self.observed / self.expected))
>
> Do we really need to protect everywhere for zero division warnings?
>

Is this protecting against a zero division warning?

> I think in statsmodels we worked around the warning in 0*np.log(0) or
> something like this.
>

IIRC, we use a mask for these kind of cases in Shannon entropy to
avoid the ugly warning.  I don't know about speed.  The current
implementation of stats.entropy uses where, but this doesn't avoid the
warning.

Skipper
```