In [1]:

    

import numpy as np


    

In [2]:

    

a = np.array([1, 2, 3, 4])
b = np.array([1, 2, 1, 2])


    

In [3]:

    

print a + b
print a - b
print a * b
print a / b
print a ** b


    

    




[2 4 4 6]
[0 0 2 2]
[1 4 3 8]
[1 1 3 2]
[ 1  4  3 16]

In [4]:

    

a = np.array([1, 2, 3, 4])
b = 2


    

In [5]:

    

print a + b
print a - b
print a * b
print a / b
print a ** b


    

    




[3 4 5 6]
[-1  0  1  2]
[2 4 6 8]
[0 1 1 2]
[ 1  4  9 16]

In [6]:

    

a = np.array([True, True, False, False])
b = np.array([True, False, True, False])


    

In [7]:

    

print a & b
print a | b
print ~a
    
print a & True
print a & False
    
print a | True
print a | False


    

    




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

In [8]:

    

a = np.array([1, 2, 3, 4, 5])
b = np.array([5, 4, 3, 2, 1])


    

In [9]:

    

print a > b
print a >= b
print a < b
print a <= b
print a == b
print a != b


    

    




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

In [10]:

    

a = np.array([1, 2, 3, 4])
b = 2


    

In [11]:

    

print a > b
print a >= b
print a < b
print a <= b
print a == b
print a != b


    

    




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

In [12]:

    

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'
])


    

In [13]:

    

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
])


    

In [15]:

    

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
])


    

In [20]:

    

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.
    '''
    overall_completion = (female_completion + male_completion) / 2.0
    return overall_completion


    

In [22]:

    

overall_completion_rate(female_completion, male_completion)


    

    
Out[22]:





array([  96.416025,  102.644275,  101.41129 ,   93.316285,  103.455575,
         98.148215,  102.35113 ,   91.77855 ,   92.835475,   89.655755,
         99.218715,   98.484275,   94.172835,  117.335125,   98.275645,
         33.04039 ,   41.905225,   90.962965,   57.08404 ,   93.06015 ])

In [23]:

    

# First 20 countries with employment data
countries = np.array([
    'Afghanistan', 'Albania', 'Algeria', 'Angola', 'Argentina',
    'Armenia', 'Australia', 'Austria', 'Azerbaijan', 'Bahamas',
    'Bahrain', 'Bangladesh', 'Barbados', 'Belarus', 'Belgium',
    'Belize', 'Benin', 'Bhutan', 'Bolivia',
    'Bosnia and Herzegovina'
])

# Employment data in 2007 for those 20 countries
employment = np.array([
    55.70000076,  51.40000153,  50.5       ,  75.69999695,
    58.40000153,  40.09999847,  61.5       ,  57.09999847,
    60.90000153,  66.59999847,  60.40000153,  68.09999847,
    66.90000153,  53.40000153,  48.59999847,  56.79999924,
    71.59999847,  58.40000153,  70.40000153,  41.20000076
])


    

In [32]:

    

values = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])


    

In [35]:

    

def standardize_data(values):
    '''
    Fill in this function to return a standardized version of the given values,
    which will be in a NumPy array. Each value should be translated into the
    number of standard deviations that value is away from the mean of the data.
    (A positive number indicates a value higher than the mean, and a negative
    number indicates a value lower than the mean.)
    '''
    mean_values = values.mean()
    std_values = values.std()
    out_values = (values - mean_values) / std_values
    return out_values


    

In [36]:

    

standardize_data(values)


    

    
Out[36]:





array([-1.5666989 , -1.21854359, -0.87038828, -0.52223297, -0.17407766,
        0.17407766,  0.52223297,  0.87038828,  1.21854359,  1.5666989 ])

In [ ]: