numpy python tutorial

numpy documentation tutorial

>>> np.zeros( (3,4) )
array([[ 0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.]])
>>> np.ones( (2,3,4), dtype=np.int16 )                # dtype can also be specified
array([[[ 1, 1, 1, 1],
        [ 1, 1, 1, 1],
        [ 1, 1, 1, 1]],
       [[ 1, 1, 1, 1],
        [ 1, 1, 1, 1],
        [ 1, 1, 1, 1]]], dtype=int16)
>>> np.empty( (2,3) )                                 # uninitialized, output may vary
array([[  3.73603959e-262,   6.02658058e-154,   6.55490914e-260],
       [  5.30498948e-313,   3.14673309e-307,   1.00000000e+000]])

Posted by: Guest on June-18-2020

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x = 3 print(type(x)) # Prints "<class 'int'>" print(x) # Prints "3" print(x + 1) # Addition; prints "4" print(x - 1) # Subtraction; prints "2" print(x * 2) # Multiplication; prints "6" print(x ** 2) # Exponentiation; prints "9" x += 1 print(x) # Prints "4" x *= 2 print(x) # Prints "8" y = 2.5 print(type(y)) # Prints "<class 'float'>" print(y, y + 1, y * 2, y ** 2) # Prints "2.5 3.5 5.0 6.25"

t = True f = False print(type(t)) # Prints "<class 'bool'>" print(t and f) # Logical AND; prints "False" print(t or f) # Logical OR; prints "True" print(not t) # Logical NOT; prints "False" print(t != f) # Logical XOR; prints "True"

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