Ok thanks, I figured np.isnan(data) is True is what we want but wasn't certain. I think it should describe the same thing.
np.all(~np.isnan(data)) >>> False Seems to be all non-nan On Mon, Oct 29, 2012 at 2:12 AM, eat <e.antero.ta...@gmail.com> wrote: > Hi, > > On Mon, Oct 29, 2012 at 11:01 AM, Larry Paltrow > <larry.palt...@gmail.com>wrote: > >> np.isnan(data) is True >> >>> False >> > Check with: > np.all(~np.isnan(x)) > > My 2 cents, > -eat > >> >> >> On Mon, Oct 29, 2012 at 1:50 AM, Pauli Virtanen <p...@iki.fi> wrote: >> >>> Larry Paltrow <larry.paltrow <at> gmail.com> writes: >>> > I'm having some trouble using the linalg.lstsq() function >>> > with certain data and trying to understand why. It's >>> > always returning nans when I feed it this numpy array of float64 data: >>> > >>> > data = df.close.values #coming from a pandas dataframe >>> > >>> > type(data) >>> > >>> numpy.ndarray >>> >>> Maybe you have NaNs in the array? (i.e. np.isnan(data) is True) >>> >>> -- >>> Pauli Virtanen >>> >>> >>> _______________________________________________ >>> NumPy-Discussion mailing list >>> NumPy-Discussion@scipy.org >>> http://mail.scipy.org/mailman/listinfo/numpy-discussion >>> >> >> >> _______________________________________________ >> NumPy-Discussion mailing list >> NumPy-Discussion@scipy.org >> http://mail.scipy.org/mailman/listinfo/numpy-discussion >> >> > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion > >
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