In [1]:

    

import numpy as np


    

In [2]:

    

print(np.__version__)


    

    




1.17.3

In [3]:

    

a_bool = np.array([True, True, True])
b_bool = np.array([True, False, False])


    

In [4]:

    

# if a_bool:
#     pass
# ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()


    

In [5]:

    

# a_bool and b_bool
# ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()


    

In [6]:

    

# a_bool or b_bool
# ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()


    

In [7]:

    

# not b_bool
# ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()


    

In [8]:

    

print(bool([0, 1, 2]))


    

    




True

In [9]:

    

print(bool([]))


    

    




False

In [10]:

    

print(not [0, 1, 2])


    

    




False

In [11]:

    

print(not [])


    

    




True

In [12]:

    

# bool(a_bool)
# ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()


    

In [13]:

    

print(a_bool.all())


    

    




True

In [14]:

    

print(a_bool.any())


    

    




True

In [15]:

    

print(b_bool.all())


    

    




False

In [16]:

    

print(b_bool.any())


    

    




True

In [17]:

    

a_bool_2d = np.array([[True, True, True], [True, False, False]])
print(a_bool_2d)


    

    




[[ True  True  True]
 [ True False False]]

In [18]:

    

print(a_bool_2d.all())


    

    




False

In [19]:

    

print(a_bool_2d.all(axis=0))


    

    




[ True False False]

In [20]:

    

print(a_bool_2d.all(axis=1))


    

    




[ True False]

In [21]:

    

print(type(a_bool_2d.all(axis=0)))


    

    




<class 'numpy.ndarray'>

In [22]:

    

print(a_bool.size)


    

    




3

In [23]:

    

print(a_bool.size == 0)


    

    




False

In [24]:

    

print(a_bool & b_bool)


    

    




[ True False False]

In [25]:

    

print(a_bool | b_bool)


    

    




[ True  True  True]

In [26]:

    

print(~b_bool)


    

    




[False  True  True]

In [27]:

    

print(a_bool ^ b_bool)


    

    




[False  True  True]

In [28]:

    

a_int = np.array([0, 1, 3])  # [0b00 0b01 0b11]
b_int = np.array([1, 0, 2])  # [0b01 0b00 0b10]


    

In [29]:

    

print(a_int & b_int)


    

    




[0 0 2]

In [30]:

    

print(a_int | b_int)


    

    




[1 1 3]

In [31]:

    

print(a_int ^ b_int)


    

    




[1 1 1]

In [32]:

    

print(~a_int)


    

    




[-1 -2 -4]

In [33]:

    

a = np.arange(12).reshape(3, 4)
print(a)


    

    




[[ 0  1  2  3]
 [ 4  5  6  7]
 [ 8  9 10 11]]

In [34]:

    

print(a > 3)


    

    




[[False False False False]
 [ True  True  True  True]
 [ True  True  True  True]]

In [35]:

    

print(a % 2 == 0)


    

    




[[ True False  True False]
 [ True False  True False]
 [ True False  True False]]

In [36]:

    

# print(a > 3 & a % 2 == 0)
# ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()


    

In [37]:

    

# print(a > (3 & (a % 2)) == 0)
# ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()


    

In [38]:

    

print((a > 3) & (a % 2 == 0))


    

    




[[False False False False]
 [ True False  True False]
 [ True False  True False]]

In [39]:

    

x = 10


    

In [40]:

    

print(x > 3)


    

    




True

In [41]:

    

print(x % 2 == 1)


    

    




False

In [42]:

    

print(x > 3 or x % 2 == 1)


    

    




True

In [43]:

    

print((x > 3) or (x % 2 == 1))


    

    




True

In [44]:

    

a_single = np.array([0])
b_single = np.array([1])
c_single = np.array([2])


    

In [45]:

    

print(bool(a_single))


    

    




False

In [46]:

    

print(bool(b_single))


    

    




True

In [47]:

    

print(bool(c_single))


    

    




True

In [48]:

    

print(b_single and c_single)


    

    




[2]

In [49]:

    

print(c_single and b_single)


    

    




[1]

In [50]:

    

print(b_single or c_single)


    

    




[1]

In [51]:

    

print(c_single or b_single)


    

    




[2]

In [52]:

    

print(b_single & c_single)


    

    




[0]

In [53]:

    

print(b_single | c_single)


    

    




[3]

In [54]:

    

print(not a_single)


    

    




True

In [55]:

    

print(not b_single)


    

    




False

In [56]:

    

print(not c_single)


    

    




False

In [57]:

    

print(~a_single)


    

    




[-1]

In [58]:

    

print(~b_single)


    

    




[-2]

In [59]:

    

print(~c_single)


    

    




[-3]

In [60]:

    

a_empty = np.array([])
print(a_empty)


    

    




[]

In [61]:

    

print(bool(a_empty))


    

    




False






    




/usr/local/lib/python3.7/site-packages/ipykernel_launcher.py:1: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.
  """Entry point for launching an IPython kernel.