关于panda中dataframe的与&运算*(stackoverflow高票答案)

mylist1 = [True, True, True, False, True] mylist2 = [False, True, False, True, False] >>> len(mylist1) == len(mylist2) True # ---- Example 1 ---- >>>mylist1 and mylist2 [False, True, False, True, False] #I am confused: I would have expected [False, True, False, False, False] # ---- Example 2 ---- >>>mylist1 & mylist2 *** TypeError: unsupported operand type(s) for &: ‘list‘ and ‘list‘ #I am confused: Why not just like example 1? # ---- Example 3 ---- >>>import numpy as np >>> np.array(mylist1) and np.array(mylist2) *** ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all() #I am confused: Why not just like Example 4? # ---- Example 4 ---- >>> np.array(mylist1) & np.array(mylist2) array([False, True, False, False, False], dtype=bool) #This is the output I was expecting!

and tests whether both expressions are logically True while & (when used with True/False values) tests if both are True.

In Python, empty built-in objects are typically treated as logically False while non-empty built-ins are logically True. This facilitates the common use case where you want to do something if a list is empty and something else if the list is not. Note that this means that the list [False] is logically True:

>>> if [False]:
...    print ‘True‘
...
True

So in Example 1, the first list is non-empty and therefore logically True, so the truth value of the and is the same as that of the second list. (In our case, the second list is non-empty and therefore logically True, but identifying that would require an unnecessary step of calculation.)

For example 2, lists cannot meaningfully be combined in a bitwise fashion because they can contain arbitrary unlike elements. Things that can be combined bitwise include: Trues and Falses, integers.

NumPy objects, by contrast, support vectorized calculations. That is, they let you perform the same operations on multiple pieces of data.

Example 3 fails because NumPy arrays (of length > 1) have no truth value as this prevents vector-based logic confusion.

Example 4 is simply a vectorized bit and operation.

Bottom Line

If you are not dealing with arrays and are not performing math manipulations of integers, you probably want and.
If you have vectors of truth values that you wish to combine, use numpy with &.

4

Example 1 only appears to give the correct output. It actually just returns the second list unaltered. Try some other lists, in particular anything where the second list contains a True in a position that‘s False in the first list: Boolean logic dictates a False output at that position, but you‘ll get a True. – user395760 Mar 25 ‘14 at 21:22
@delnan Thanks for noticing the ambiguity in my examples. I have updated my examples to highlight my confusion and focus on the aspect of this behavior that I do not understand. I‘m clearly missing something important, because I did not expect the output of Example 1. – rysqui Mar 25 ‘14 at 21:37
2

In Numpy there‘s np.bitwise_and() and np.logical_and() and friends to avoid confusion. – Dietrich Mar 25 ‘14 at 21:54
In example 1, mylist1 and mylist2 does not output the same result as mylist2 and mylist1, since what is being returned is the second list as pointed out by delnan. – user2015487 Feb 16 ‘16 at 17:58
1

Possible duplicate of Python: Boolean operators vs Bitwise operators – Oliver Ni Nov 6 ‘16 at 16:09

关于panda中dataframe的与&运算*(stackoverflow高票答案)

7 Answers