当在numpy数组中使用时，等效运算符是什么意思

Question

I am following an example which I found it in the Python Data science handbook , the purpose of this example is to create two array masks to finally output the rainy days in summer , the author supposed that summer starts on 21st June which is the 172th day and it ends 3 months later.

Here I am only interested in only the piece of code where he made the summer interval:

# Construct a mask for all summer days (June 21st is the 172nd day)
summer = (np.arange((365) - 172 < 90 ) & np.arange((365) - 172 > 0)

In another version of the book, I found this code, and I think it leads to the same result:

# construct a mask of all summer days (June 21st is the 172nd day)
days = np.arange(365)
summer = (days > 172) & (days < 262)

Both examples are not clear to me, please help.

Answer 1

Maybe a simple example would help to understand it better.

# sample array
In [19]: week = np.arange(1, 8)

# find middle 3 days of the week
# to do so, we first find boolean masks by performing
# (week > 2) which performs element-wise comparison, so does (week < 6)
# then we simply do a `logical_and` on these two boolean masks
In [20]: middle = (week > 2) & (week < 6)

In [21]: middle
Out[21]: array([False, False,  True,  True,  True, False, False])

# index into the original array to get the days
In [22]: week[middle]
Out[22]: array([3, 4, 5])

---

the & operator is equivalent to numpy.logical_and() whereas the > and < operators are equivalent to numpy.greater() and numpy.less respectively.

# create a boolean mask (for days greater than 2)
In [23]: week > 2
Out[23]: array([False, False,  True,  True,  True,  True,  True])

# create a boolean mask (for days less than 6)
In [24]: week < 6
Out[24]: array([ True,  True,  True,  True,  True, False, False])

# perform a `logical_and`; note that this is exactly same as `middle`
In [25]: np.logical_and((week > 2), (week < 6))
Out[25]: array([False, False,  True,  True,  True, False, False])

In [26]: mid = np.logical_and((week > 2), (week < 6))

# sanity check again
In [27]: week[mid]
Out[27]: array([3, 4, 5])