CODEWITHSUNDEEP
pythonnumpydata-structures
abbath0767
abbath0767

Reputation: 986

How create data structure with similar numpy nd array

i have next function: 

def check_smaller_zeros(v):
   return v < 0

When i create array with numpy i can write next code for element-by-element measurement:

v_1 = numpy.array([1, 2, -4, -1])
result = check_smaller_zeros(v_1)
# result: [False, False, True, True]

but when i try repeat it with tuple, set, frozenset and list/array reise next error:

TypeError: '<' not supported between instances of 'tuple' and 'int'

what exactly allows numpy array to have the capability for this behavior? This looks convenient, but is a bit non-obvious

Upvotes: 1

Views: 151

Answers (2)

yardsale8
yardsale8

Reputation: 945

Python allows you to override operators with special "dunder" (double underscore) methods. For example, let's make a custom data structure that inherits from tuple. To make < perform item-wise comparisons, we simply need to overwrite the __lt__ method.

In [1]: class MyList(tuple):
      :     def __lt__(self, other):
      :         return tuple(v < other for v in self)
      :

In [1]:

In [2]: l = MyList([1,2,3,4,5])

In [3]: l < 3
Out[3]: (True, True, False, False, False)

A list of all such methods can be found in the Python documentation under Data Model.

Upvotes: 2

alec_djinn
alec_djinn

Reputation: 10819

np.array is very different from tuple, lists, etc. If you want to generalize your function, you have to think about each case.

In [111]: def check_smaller_zeros(v):
     ...:    if type(v) is np.array:
     ...:        return v < 0
     ...:    elif type(v) in (list, tuple):
     ...:        return [x<0 for x in v]
     ...:

In [112]: check_smaller_zeros((-1,1,2,3,4,5))
Out[112]: [True, False, False, False, False, False]

In some instance, you may can simply convert the object to a np.array. But it depends form the object and you have to define the use cases in advance.

In [114]: def check_smaller_zeros(v):
     ...:    if type(v) is np.array:
     ...:        return v < 0
     ...:    else:
     ...:        return np.array(v) < 0
     ...:
     ...:

In [115]: check_smaller_zeros((-1,1,2,3,4,5))
Out[115]: array([True, False, False, False, False, False])

Upvotes: -1

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