Tanggal :September 27, 2020

py38. Numpy arrays

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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]]
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