Calculate Overall Completion Rate

Question:

Start Quiz:

import numpy as np

# Change False to True for each block of code to see what it does

# Arithmetic operations between 2 NumPy arrays
if False:
    a = np.array([1, 2, 3, 4])
    b = np.array([1, 2, 1, 2])
    
    print a + b
    print a - b
    print a * b
    print a / b
    print a ** b
    
# Arithmetic operations between a NumPy array and a single number
if False:
    a = np.array([1, 2, 3, 4])
    b = 2
    
    print a + b
    print a - b
    print a * b
    print a / b
    print a ** b
    
# Logical operations with NumPy arrays
if False:
    a = np.array([True, True, False, False])
    b = np.array([True, False, True, False])
    
    print a & b
    print a | b
    print ~a
    
    print a & True
    print a & False
    
    print a | True
    print a | False
    
# Comparison operations between 2 NumPy Arrays
if False:
    a = np.array([1, 2, 3, 4, 5])
    b = np.array([5, 4, 3, 2, 1])
    
    print a > b
    print a >= b
    print a < b
    print a <= b
    print a == b
    print a != b
    
# Comparison operations between a NumPy array and a single number
if False:
    a = np.array([1, 2, 3, 4])
    b = 2
    
    print a > b
    print a >= b
    print a < b
    print a <= b
    print a == b
    print a != b
    
# First 20 countries with school completion data
countries = np.array([
       'Algeria', 'Argentina', 'Armenia', 'Aruba', 'Austria','Azerbaijan',
       'Bahamas', 'Barbados', 'Belarus', 'Belgium', 'Belize', 'Bolivia',
       'Botswana', 'Brunei', 'Bulgaria', 'Burkina Faso', 'Burundi',
       'Cambodia', 'Cameroon', 'Cape Verde'
])

# Female school completion rate in 2007 for those 20 countries
female_completion = np.array([
    97.35583,  104.62379,  103.02998,   95.14321,  103.69019,
    98.49185,  100.88828,   95.43974,   92.11484,   91.54804,
    95.98029,   98.22902,   96.12179,  119.28105,   97.84627,
    29.07386,   38.41644,   90.70509,   51.7478 ,   95.45072
])

# Male school completion rate in 2007 for those 20 countries
male_completion = np.array([
     95.47622,  100.66476,   99.7926 ,   91.48936,  103.22096,
     97.80458,  103.81398,   88.11736,   93.55611,   87.76347,
    102.45714,   98.73953,   92.22388,  115.3892 ,   98.70502,
     37.00692,   45.39401,   91.22084,   62.42028,   90.66958
])

def overall_completion_rate(female_completion, male_completion):
    '''
    Fill in this function to return a NumPy array containing the overall
    school completion rate for each country. The arguments are NumPy
    arrays giving the female and male completion of each country in
    the same order.
    '''
    return None

Solution:

INSTRUCTOR NOTE:

Bitwise Operations

See this article for more information about bitwise operations.

In NumPy, a & b performs a bitwise and of a and b. This is not necessarily the same as a logical and, if you wanted to see if matching terms in two integer vectors were non-zero. However, if a and b are both arrays of booleans, rather than integers, bitwise and and logical and are the same thing. If you want to perform a logical and on integer vectors, then you can use the NumPy function np.logical_and(a, b) or convert them into boolean vectors first.

Similarly, a | b performs a bitwise or, and ~a performs a bitwise not. However, if your arrays contain booleans, these will be the same as performing logical or and logical not. NumPy also has similar functions for performing these logical operations on integer-valued arrays.

For the quiz, assume that the number of males and females are equal i.e. we can take a simple average to get an overall completion rate.

In the solution, we may want to / 2. instead of just / 2. This is because in Python 2, dividing an integer by another integer (2) drops fractions, so if our inputs are also integers, we may end up losing information. If we divide by a float (2.) then we will definitely retain decimal values.

Erratum: The output of cell [3] in the solution video is incorrect: it appears that the male variable has not been set to the proper value set in cell [2]. All values except for the first will be different. The correct output in cell Out[3]: should instead start with:

array([ 192.83205,  205.28855,  202.82258,  186.63257,  206.91115,