SciPy chisquare applied on continuous data? chi2 seems to be used to compare distributions between categorical features.
Is it possible to use SciPy chisquare function to compare two distributions of continuous data ? 
For example :
v1 = np.random.normal(0,10, size=10)
v2 = np.random.normal(0,1, size=10) 
chisquare(v1,v2)

Here what's returned :
Power_divergenceResult(statistic=14184.095704289084, pvalue=0.0)

And if we want to get :
chisquare(v2,v1)

Here what's returned :
Power_divergenceResult(statistic=17.21881425478222, pvalue=0.045397616560846649)

Is it really something we can't do (or get knowledge from the result)?
 A: For comparision of two samples use either Kolmogorov-Smirnov or Anderson–Darling tests, but the second one is more powerful, here's a short article comparing them: http://www.jaqm.ro/issues/volume-6,issue-3/pdfs/1_engmann_cousineau.pdf
But I also see that you compare with N(0,1), so maybe you're always interested in testing normality, thus I would recommend Shapiro–Wilk test.
Moreover, if you're not only interested in simple null hypothesis testing,
but you would like to know how far from each other this two distributions are, check Kullback–Leibler divergence (remember that KL is not metric) or Bhattacharyya distance.
A: The chisquare test cannot be directly used for continuously distributed data. The distribution of the test statistic assumes that we have a number of bins with a large number of observations per bin. (There are different rules of thumb for how many observations per bin, at least 5 is a common recommendation.)
The chisquare test can be used to compare two histograms with equal bin limits, so it works for binned continuously distributed data.
The most common alternatives for goodness-of-fit tests for continuously distributed data are the Anderson-Darling and the Kolmogorov-Smirnov test which are based on the cumulative distribution function instead of the density or probability mass function.
