import numpy
# it will compare the second value to each element in the vector
# If the value are equal, the Python interpreter returns True; otherwise, it return False
vector = numpy.array([5,10,15,20])
vector == 10
array([False, True, False, False])
matrix = numpy.array([
[5,10,15],
[20,25,30],
[35,40,45]])
matrix == 25
array([[False, False, False],
[False, True, False],
[False, False, False]])
# 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]
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,:]) # return the raw corresponding to True
[False True False]
[[20 25 30]]
# We can also perform comparisons with multipe 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]
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]
# We can convert the date 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)
<U1
['1' '2' '3']
float64
[1. 2. 3.]
vector = numpy.array([5,10,15,20])
vector.sum()
50
# The axis dictates which dimension we perform the operator on
# 1 menas 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)
array([ 30, 75, 120])
matrix = numpy.array([[5,10,15],
[20,25,30],
[35,40,45]])
matrix.sum(axis=0)
array([60, 75, 90])
