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In this article Xiaobian for you to introduce in detail "how to use the vector trinomial operator in numpy", the content is detailed, the steps are clear, and the details are handled properly. I hope that this article "how to use the vector trinomial operator in numpy" can help you solve your doubts.

The record is as follows:

In [76]: xarr = np.array ([1.1 1In 1.2) In [77]: yarr = xarr + 1In [78]: xarrOut [78]: array [79]: yarrOut [79]: array ([2.1,2.2,2.3,2.4,2.5]) In [80]: cond = np.array ([True,False,True,True]) False]) In [81]: condOut [81]: array ([True, False, True, True, False], dtype=bool) In [82]: result1 = [(x if c else y) for x journal c in zip (xarr,yarr,cond)] In [83]: result1Out [83]: [1.100000000001,2.200000000002,1.3,1.3999999999999999992.5] In [84]: result2 = np.where (cond,xarr,yarr) In [85]: result2Out [85]: array ([1.1,2.2]) 1.3, 1.4, 2.5])

From the floating point representation, there is a small difference between the two, multi-digits after the decimal point, usually can be ignored in the numerical representation. However, we still need to make a judgment on the consistency of the two results, because we have seen the differences in Python's floating-point expression due to machines before.

The test results are as follows:

In [87]: result1 = = result2Out [87]: array ([True, True], dtype=bool)

As can be seen from the above results, the two calculation results are consistent.

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