How to normalize an array in NumPy?

I would like to have the norm of one NumPy array. More specifically, I am looking for an equivalent version of this function

    def normalize(v):
        norm = np.linalg.norm(v)
        if norm == 0: 
           return v
        return v / norm

Is there something like that in skearn or numpy?

This function works in a situation where v is the 0 vector.

If you're using scikit-learn you can use sklearn.preprocessing.normalize:

    import numpy as np
    from sklearn.preprocessing import normalize

    x = np.random.rand(1000)*10
    norm1 = x / np.linalg.norm(x)
    norm2 = normalize(x[:,np.newaxis], axis=0).ravel()
    print np.all(norm1 == norm2)
    # True