In [9]:

    

import numpy
#it will compare the second value to each element in the vector
# If the values are equal, the Python interpreter returns True; otherwise, it returns False
vector = numpy.array([5, 10, 15, 20])
vector == 10


    

    
Out[9]:





array([False,  True, False, False], dtype=bool)

In [10]:

    

matrix = numpy.array([
                    [5, 10, 15], 
                    [20, 25, 30],
                    [35, 40, 45]
                 ])
matrix == 25


    

    
Out[10]:





array([[False, False, False],
       [False,  True, False],
       [False, False, False]], dtype=bool)

In [6]:

    

#Compares vector to the value 10, which generates a new Boolean vector [False, True, False, False]. It assigns this result to equal_to_ten
vector = numpy.array([5, 10, 15, 20])
equal_to_ten = (vector == 10)
print equal_to_ten
print(vector[equal_to_ten])


    

    




[False  True False False]
[10]

In [8]:

    

matrix = numpy.array([
                [5, 10, 15], 
                [20, 25, 30],
                [35, 40, 45]
             ])
second_column_25 = (matrix[:,1] == 25)
print second_column_25
print(matrix[second_column_25, :])


    

    




[False  True False]
[[20 25 30]]

In [11]:

    

#We can also perform comparisons with multiple conditions
vector = numpy.array([5, 10, 15, 20])
equal_to_ten_and_five = (vector == 10) & (vector == 5)
print equal_to_ten_and_five


    

    




[False False False False]

In [12]:

    

vector = numpy.array([5, 10, 15, 20])
equal_to_ten_or_five = (vector == 10) | (vector == 5)
print equal_to_ten_or_five


    

    




[ True  True False False]

In [13]:

    

vector = numpy.array([5, 10, 15, 20])
equal_to_ten_or_five = (vector == 10) | (vector == 5)
vector[equal_to_ten_or_five] = 50
print(vector)


    

    




[50 50 15 20]

In [12]:

    

matrix = numpy.array([
            [5, 10, 15], 
            [20, 25, 30],
            [35, 40, 45]
         ])
second_column_25 = matrix[:,1] == 25
print second_column_25
matrix[second_column_25, 1] = 10
print matrix


    

    




[False  True False]
[[ 5 10 15]
 [20 10 30]
 [35 40 45]]

In [14]:

    

#We can convert the data type of an array with the ndarray.astype() method.
vector = numpy.array(["1", "2", "3"])
print vector.dtype
print vector
vector = vector.astype(float)
print vector.dtype
print vector


    

    




|S1
['1' '2' '3']
float64
[ 1.  2.  3.]

In [19]:

    

vector = numpy.array([5, 10, 15, 20])
vector.sum()


    

    
Out[19]:





50

In [20]:

    

# The axis dictates which dimension we perform the operation on
#1 means that we want to perform the operation on each row, and 0 means on each column
matrix = numpy.array([
                [5, 10, 15], 
                [20, 25, 30],
                [35, 40, 45]
             ])
matrix.sum(axis=1)


    

    
Out[20]:





array([ 30,  75, 120])

In [21]:

    

matrix = numpy.array([
                [5, 10, 15], 
                [20, 25, 30],
                [35, 40, 45]
             ])
matrix.sum(axis=0)


    

    
Out[21]:





array([60, 75, 90])

In [25]:

    

#replace nan value with 0
world_alcohol = numpy.genfromtxt("world_alcohol.txt", delimiter=",")
#print world_alcohol
is_value_empty = numpy.isnan(world_alcohol[:,4])
#print is_value_empty
world_alcohol[is_value_empty, 4] = '0'
alcohol_consumption = world_alcohol[:,4]
alcohol_consumption = alcohol_consumption.astype(float)
total_alcohol = alcohol_consumption.sum()
average_alcohol = alcohol_consumption.mean()
print total_alcohol
print average_alcohol


    

    




1137.78
1.14006012024

In [ ]:

    




    

In [ ]: