numpy - Two-sample Kolmogorov-Smirnov Test in Python Scipy -

i can't figure out how two-sample ks test in scipy.

after reading documentation scipy kstest

i can see how test distribution identical standard normal distribution

from scipy.stats import kstest import numpy np  x = np.random.normal(0,1,1000) test_stat = kstest(x, 'norm') #>>> test_stat #(0.021080234718821145, 0.76584491300591395) 

which means @ p-value of 0.76 can not reject null hypothesis 2 distributions identical.

however, want compare 2 distributions , see if can reject null hypothesis identical, like:

from scipy.stats import kstest import numpy np  x = np.random.normal(0,1,1000) z = np.random.normal(1.1,0.9, 1000) 

and test whether x , z identical

i tried naive:

test_stat = kstest(x, z) 

and got following error:

typeerror: 'numpy.ndarray' object not callable 

is there way two-sample ks test in python? if so, how should it?

thank in advance

you're using one-sample ks test. want ks_2samp:

>>> scipy.stats import ks_2samp >>> import numpy np >>>  >>> np.random.seed(12345678) >>> x = np.random.normal(0, 1, 1000) >>> y = np.random.normal(0, 1, 1000) >>> z = np.random.normal(1.1, 0.9, 1000) >>>  >>> ks_2samp(x, y) ks_2sampresult(statistic=0.022999999999999909, pvalue=0.95189016804849647) >>> ks_2samp(x, z) ks_2sampresult(statistic=0.41800000000000004, pvalue=3.7081494119242173e-77)