Hi group, I just answered a question on Stackoverflow from some new user, who was bitten by some unexpected behavior when using a list instead of a np.array, see here: http://stackoverflow.com/q/26690480/2647279
The unexpected thing that triggered the error is that multiplying a list with a Numpy scalar behaves like standard integer multiplication of a list as in Python without raising an error, while addition in the same way works as expected: In [1]: import numpy as np In [2]: np.version.version Out[2]: '1.8.2' In [3]: 2 * [1, 2, 3] Out[3]: [1, 2, 3, 1, 2, 3] In [4]: 2.5 * [1, 2, 3] [...] TypeError: can't multiply sequence by non-int of type 'float' In [5]: np.float64(2.5) * [1, 2, 3] # like standard Python list tiling Out[5]: [1, 2, 3, 1, 2, 3] In [6]: np.float64(2.5) + [1, 2, 3] # element-wise addition Out[6]: array([ 3.5, 4.5, 5.5]) I would have expected that the multiplication would have either raised an exception, or would have given the element-wise result np.array([2.5, 5.0, 7.5]). Is there any good reason why multiplication and addition behave so different? I am using standard, up-to-date python2.7/numpy under 64-bit Ubuntu 14.04. Cheers, Bas _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
