S1 is not (100) but (100,). What does the second parameter mean? Thank you
import numpy as np
s1=np.array([])
for i in range(0,100):
s1=np.append(s1,np.random.normal(0,1))
print(s1.shape)
====
(100,)
S1 is not (100) but (100,). What does the second parameter mean? Thank you
import numpy as np
s1=np.array([])
for i in range(0,100):
s1=np.append(s1,np.random.normal(0,1))
print(s1.shape)
====
(100,) shape () returns the length of each dimension when the data is used as a matrix.
for example:
arr1 = np.array([
[1, 2],
[1, 2]
])
arr1.shape -sharp (2, 2)
arr2 = np.array([
[1, 2, 3],
[1, 2, 3]
])
arr2.shape -sharp (2, 3)
arr3 = np.array([
[
[1, 2, 3, 4],
[1, 2, 3, 4],
[1, 2, 3, 4]
],
[
[1, 2, 3, 4],
[1, 2, 3, 4],
[1, 2, 3, 4]
]
])
arr3.shape -sharp (2, 3, 4) the example you gave is an one-dimensional array, so the length of shape is only 1, and the outermost length is 100, so shape= (100,) .
reference:
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