Reference link: numpy.empty in Python
array: create an array dtype: specify the data type empty: create data close to 0 zeros: create data all 0ones: create data all 1arrange: create data in a specified range linspace: create line segments
import numpy as np #For the convenience of using numpy, use np abbreviation
array = np.array([[1,2,3],[2,3,4]]) #The list is converted to a matrix
print(array)
"""
[[1 2 3]
[2 3 4]]
"""
print('number of dim:',array.ndim) # dimension
# number of dim: 2
print('shape :',array.shape) # number of rows and columns
# shape : (2, 3)
print('size:',array.size) # number of elements
# size: 6
a = np.empty((3,4)) # The data is empty, 3 rows and 4 columns, creating a completely empty array, in fact, each value is a number close to zero
"""
array([[ 0.00000000e+000, 4.94065646e-324, 9.88131292e-324,
1.48219694e-323),
(1.97626258e-323, 2.47032823e-323, 2.96439388e-323,
3.45845952e-323),
(3.95252517e-323, 4.44659081e-323, 4.94065646e-323,
5.43472210e-323]])
"""
a = np.zeros((3,4)) # data is all 0, 3 rows and 4 columns
"""
array([[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.]])
"""
a = np.ones((3,4),dtype = np.int) # The data is 1, 3 rows and 4 columns
"""
array([[1, 1, 1, 1],
[1, 1, 1, 1],
[1, 1, 1, 1]])
"""
Pay attention to the difference between arange and linspace
a = np.arange(1,106,5.5).reshape((5,4)) # Data with interval [1, 106), step size 5.5
>>> a
array([[ 1. , 6.5, 12. , 17.5],
[ 23. , 28.5, 34. , 39.5],
[ 45. , 50.5, 56. , 61.5],
[ 67. , 72.5, 78. , 83.5],
[ 89. , 94.5, 100. , 105.5]])
a = np.linspace(1,105.5,20).reshape((5,4)) # start end 1, end end 105.5, and divide into 20 data, generate line segment
>>> a
array([[ 1. , 6.5, 12. , 17.5],
[ 23. , 28.5, 34. , 39.5],
[ 45. , 50.5, 56. , 61.5],
[ 67. , 72.5, 78. , 83.5],
[ 89. , 94.5, 100. , 105.5]])