py38. Numpy arrays

Numpy arrays contain data of one kind, specified by the dtype attribute, which can be changed with astype(), with the proper string identifier.

We can select values by slicing, giving a list of indices, or a logical expression.

The hstack and vstack functions can join arrays horizontally or vertically. The T attribute can be used to find the transpose of a numpy array. A 2D array will correspond to a matrix, however we also have a matrix class in numpy.

# ex38.py
from __future__ import print_function, division
import numpy as np
A = np.arange(5)
print('A =',A)
print('A.dtype =',A.dtype)
B = A.astype('float')
print('B =',B)
print('B.dtype =',B.dtype)
print('A[2:4] =',A[2:4])
print('A[[2,3]] =',A[[2,3]])
print('A[A==2 | A==3] =',A[(A==2) | (A==3)])
M1 = np.hstack((A,A,A))
print('M1 =',M1)
M2 = np.vstack((A,A,A))
print('M2 =',M2)
M3 = M2.T
print('M3 (transpose M2) =',M3)

#A = [0 1 2 3 4]
#A.dtype = int32
#B = [ 0.  1.  2.  3.  4.]
#B.dtype = float64
#A[2:4] = [2 3]
#A[[2,3]] = [2 3]
#A[A==2 | A==3] = [2 3]
#M1 = [0 1 2 3 4 0 1 2 3 4 0 1 2 3 4]
#M2 = [[0 1 2 3 4]
# [0 1 2 3 4]
# [0 1 2 3 4]]
#M3 (transpose M2) = [[0 0 0]
# [1 1 1]
# [2 2 2]
# [3 3 3]
# [4 4 4]]