Logic functions



In [1]:

    
import numpy as np



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np.__version__









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'1.11.2'

Truth value testing

Q1. Let x be an arbitrary array. Return True if none of the elements of x is zero. Remind that 0 evaluates to False in python.



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x = np.array([1,2,3])
#

x = np.array([1,0,3])
#









    



True
False

Q2. Let x be an arbitrary array. Return True if any of the elements of x is non-zero.



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x = np.array([1,0,0])
#

x = np.array([0,0,0])
#









    



True
False

Array contents

Q3. Predict the result of the following code.



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x = np.array([1, 0, np.nan, np.inf])
#print np.isfinite(x)

Q4. Predict the result of the following code.



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x = np.array([1, 0, np.nan, np.inf])
#print np.isinf(x)

Q5. Predict the result of the following code.



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x = np.array([1, 0, np.nan, np.inf])
#print np.isnan(x)

Array type testing

Q6. Predict the result of the following code.



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x = np.array([1+1j, 1+0j, 4.5, 3, 2, 2j])
#print np.iscomplex(x)

Q7. Predict the result of the following code.



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x = np.array([1+1j, 1+0j, 4.5, 3, 2, 2j])
#print np.isreal(x)

Q8. Predict the result of the following code.



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#print np.isscalar(3)
#print np.isscalar([3])
#print np.isscalar(True)

Logical operations

Q9. Predict the result of the following code.



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#print np.logical_and([True, False], [False, False])
#print np.logical_or([True, False, True], [True, False, False])
#print np.logical_xor([True, False, True], [True, False, False])
#print np.logical_not([True, False, 0, 1])

Comparison

Q10. Predict the result of the following code.



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#print np.allclose([3], [2.999999])
#print np.array_equal([3], [2.999999])

Q11. Write numpy comparison functions such that they return the results as you see.



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x = np.array([4, 5])
y = np.array([2, 5])
#
#
#
#









    



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

Q12. Predict the result of the following code.



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#print np.equal([1, 2], [1, 2.000001])
#print np.isclose([1, 2], [1, 2.000001])



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