In [1]:

    

import numpy as np


    

In [2]:

    

print(np.__version__)


    

    




1.17.3

In [3]:

    

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


    

In [4]:

    

print(a_int.dtype)


    

    




int64

In [5]:

    

print(b_int.dtype)


    

    




int64

In [6]:

    

print(a_int & b_int)


    

    




[0 0 2]

In [7]:

    

print(a_int | b_int)


    

    




[1 1 3]

In [8]:

    

print(a_int ^ b_int)


    

    




[1 1 1]

In [9]:

    

print(~a_int)


    

    




[-1 -2 -4]

In [10]:

    

print(a_int << b_int)


    

    




[ 0  1 12]

In [11]:

    

print(a_int >> b_int)


    

    




[0 1 0]

In [12]:

    

print(np.bitwise_and(a_int, b_int))


    

    




[0 0 2]

In [13]:

    

print(np.bitwise_or(a_int, b_int))


    

    




[1 1 3]

In [14]:

    

print(np.bitwise_xor(a_int, b_int))


    

    




[1 1 1]

In [15]:

    

print(np.bitwise_not(a_int))


    

    




[-1 -2 -4]

In [16]:

    

print(np.invert(a_int))


    

    




[-1 -2 -4]

In [17]:

    

print(~a_int)


    

    




[-1 -2 -4]

In [18]:

    

print(-(a_int + 1))


    

    




[-1 -2 -4]

In [19]:

    

a_uint8 = np.array([0, 1, 3], dtype=np.uint8)


    

In [20]:

    

print(~a_uint8)


    

    




[255 254 252]

In [21]:

    

a_uint16 = np.array([0, 1, 3], dtype=np.uint16)


    

In [22]:

    

print(~a_uint16)


    

    




[65535 65534 65532]

In [23]:

    

c_int_2d = np.arange(6).reshape(2, 3)
print(c_int_2d)


    

    




[[0 1 2]
 [3 4 5]]

In [24]:

    

print(c_int_2d & a_int)


    

    




[[0 1 2]
 [0 0 1]]

In [25]:

    

print(np.bitwise_and(c_int_2d, a_int))


    

    




[[0 1 2]
 [0 0 1]]

In [26]:

    

print(c_int_2d & 2)


    

    




[[0 0 2]
 [2 0 0]]

In [27]:

    

print(np.bitwise_and(c_int_2d, 2))


    

    




[[0 0 2]
 [2 0 0]]

In [28]:

    

d_float = np.array([0, 1, 3], dtype=float)


    

In [29]:

    

# print(a_int & d_float)
# TypeError: ufunc 'bitwise_and' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''


    

In [30]:

    

# print(~d_float)
# TypeError: ufunc 'invert' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''


    

In [31]:

    

# print(np.bitwise_and(a_int, d_float))
# TypeError: ufunc 'bitwise_and' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''


    

In [32]:

    

# print(np.bitwise_not(d_float))
# TypeError: ufunc 'invert' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''


    

In [33]:

    

e_bool = np.array([True, False, True])


    

In [34]:

    

print(a_int & e_bool)


    

    




[0 0 1]

In [35]:

    

print((a_int & e_bool).dtype)


    

    




int64

In [36]:

    

print(np.bitwise_and(a_int, e_bool))


    

    




[0 0 1]

In [37]:

    

print(np.bitwise_and(a_int, e_bool).dtype)


    

    




int64

In [38]:

    

print(~e_bool)


    

    




[False  True False]

In [39]:

    

print(np.logical_not(e_bool))


    

    




[False  True False]

In [40]:

    

print(np.bitwise_not(e_bool))


    

    




[False  True False]

In [41]:

    

print(np.invert(e_bool))


    

    




[False  True False]