Stage A-Lesson 2

convention for importing numpy.py

# convention for importing numpy
import numpy as np

arr = [6, 7, 8, 9]
print(type(arr)) # prints <class 'list'>

a = np.array(arr)
print(type(a))  # prints <class 'numpy.ndarray'>
print(a.shape)  # prints (4,) - a is a 1d array with 4 items
print(a.dtype)  # prints int64

# get the dimension of a with ndim 
print(a.ndim)   # prints 1

b = np.array([[1, 2, 3], [4, 5, 6]])
print(b)        # prints [[1 2 3]
                         [4 5 6]]
print(b.ndim)   # prints 2
b.shape         # prints (2, 3) - b a 2d array with 2 rows and     3 columns
import numpy as np

arr = [6, 7, 8, 9]
print(type(arr)) # prints <class 'list'>

a = np.array(arr)
print(type(a))  # prints <class 'numpy.ndarray'>
print(a.shape)  # prints (4,) - a is a 1d array with 4 items
print(a.dtype)  # prints int64

# get the dimension of a with ndim 
print(a.ndim)   # prints 1

b = np.array([[1, 2, 3], [4, 5, 6]])
print(b)        # prints [[1 2 3]
                         [4 5 6]]
print(b.ndim)   # prints 2
b.shape         # prints (2, 3) - b a 2d array with 2 rows and     3 columns

Inbuilt functions that can be used to initialize numpy.py

# a 2x3 array with random values
np.random.random((2, 3)) = 
                      array([[0.60793904, 0.02881965, 0.73022145],
                            [0.34183628, 0.63274067, 0.07945224]])
# a 2x3 array of zeros
np.zeros((2, 3)) = array([[0., 0., 0.],[0., 0., 0.]])
# a 2x3 array of ones
np.zeros((2, 3)) = array([[1., 1., 1.], [1., 1., 1.]])
# a 2x3 array of ones
np.zeros((2, 3)) = array([[1., 1., 1.], [1., 1., 1.]])
# a 3x3 identity matrix 
np.zeros(3) = array([[1., 0., 0.],[0., 1., 0.],[0., 0., 1.]])

Indexing with arrays & Using arrays for data processing.py

#The elements in the example arrays above can be accessed by indexing like lists in Python such that:
a[0] = 6,  a[3] = 9, b[0, 0] = 1 , b[1, 2] = 6 c[0, 1] = 8.  

#Elements in arrays  can also be retrieved by slicing rows and columns or a combination of indexing and slicing.
d[1,  0:2] = array([9., 8.])

e = np.array([[10, 11, 12],[13, 14, 15], 
              [16, 17, 18],[19, 20, 21]])

# slicing
e[:3, :2] = array([[10, 11], [13, 14],[16, 17]])

#There are other advanced methods of indexing which are shown below.
# integer indexing
e[[2, 0, 3, 1],[2, 1, 0, 2]] = array([18, 11, 19, 15])

# boolean indexing meeting a specified condition
e[e>15] = array([16, 17, 18, 19, 20, 21])

Intra-operability of arrays and scalars.py

c = np.array([[9.0, 8.0, 7.0], [1.0, 2.0, 3.0]])
d = np.array([[4.0, 5.0, 6.0], [9.0, 8.0, 7.0]])


c + d = array([[13., 13., 13.],    c * d = array([[36., 40., 42.],
               [ 10., 10., 10.]])                 [9., 16., 21.]])
               
5 / d = array([[1.25      , 1.        , 0.83333333],
               [0.55555556, 0.625     , 0.71428571]])

c ** 2 = array([[81., 64., 49.],[ 1.,  4.,  9.]])

It took me a while to get familiar with the lessons, the explanations here really went a long way in making things easier for me. Muchas gracias.

who can explain this for me

#The elements in the example arrays above can be accessed by indexing like lists in Python such that:
a[0] = 6, a[3] = 9, b[0, 0] = 1 , b[1, 2] = 6 c[0, 1] = 8.

#Elements in arrays can also be retrieved by slicing rows and columns or a combination of indexing and slicing.
d[1, 0:2] = array([9., 8.])

e = np.array([[10, 11, 12],[13, 14, 15],
[16, 17, 18],[19, 20, 21]])

slicing

e[:3, :2] = array([[10, 11], [13, 14],[16, 17]])

#There are other advanced methods of indexing which are shown below.

integer indexing

e[[2, 0, 3, 1],[2, 1, 0, 2]] = array([18, 11, 19, 15])

boolean indexing meeting a specified condition

e[e>15] = array([16, 17, 18, 19, 20, 21])