About the usage of Python's Numpy library reshape() function

1 Introduction

Change the shape of an array without changing the original array

2. Grammar

a = np.reshape(mat, newshape, order = 'C')
a : new array of newshape shape
mat : original array
newshape: (1, 2)/ 1, 2 can be changed to an array of 1 row and 2 columns
order: The rules for reading the original array, the default is C (C row first, F in a certain way, but not column first!)
The order is temporarily understood in this way.

3. use

1. b = np.reshape(a, newshape)
2. b = a.reshape(newshape)

key: There can be a parameter -1 in newshape, which means fuzzy speculation, such as (-1, 2) I don’t care if you have rows, just modify it to a two-dimensional array with 2 columns; such as (3, -1) I don’t care about you There are several columns, just modify it to a two-dimensional array of 3 rows

3.1 Reference example of order

row first:

import numpy as np

a = np.array([[1, 2, 3, 10], [4, 5, 6, 11], [7, 8, 9, 12]])
print("原数组：")
print(a)
# 修改为1，行12列数组，顺序读取
b = a.reshape(1, 12, order='C')
print("修改后：")
print(b)

F way to read

import numpy as np

a = np.array([[1, 2, 3, 10], [4, 5, 6, 11], [7, 8, 9, 12]])
print("原数组：")
print(a)
# 修改为1行12列，按列优先读取
b = a.reshape(1, 12, order='F')
print("修改后：")
print(b)

non-column-first

3.2 Actual usage (the general order is the default value)

given shape

import numpy as np

# 3行4列的二维数组
a = np.array([[1, 2, 3, 10], [4, 5, 6, 11], [7, 8, 9, 12]])
print("原数组：")
print(a)
# 此时中间只剩newshape，2行6列
b = a.reshape(2,6)
print("修改后：")
print(b)

fuzzy speculation

import numpy as np

# 3行4列的二维数组
a = np.array([[1, 2, 3, 10], [4, 5, 6, 11], [7, 8, 9, 12]])
print("原数组：")
print(a)
# 此时中间只剩newshape，修改为6行的数组就行，多少列我不知道
b = a.reshape(6, -1)
print("修改后：")
print(b)

fuzzy speculation

import numpy as np

# 3行4列的二维数组
a = np.array([[1, 2, 3, 10], [4, 5, 6, 11], [7, 8, 9, 12]])
print("原数组：")
print(a)
# 此时中间只剩newshape，修改为3列的数组就行，多少行我不知道
b = a.reshape(-1, 3)
print("修改后：")
print(b)

fuzzy speculation

import numpy as np

# 3行4列的二维数组
a = np.array([[1, 2, 3, 10], [4, 5, 6, 11], [7, 8, 9, 12]])
print("原数组：")
print(a)
# 此时中间只剩newshape，修改为3行2列的子数组，多少行我不知道
b = a.reshape((-1, 3, 2))
print("修改后：")
print(b)

key: At the beginning of the array, the number of square brackets is the number of dimensions. The original array is a two-dimensional array, and the modified array is a three-dimensional array

The above is the usage of reshape, and it may be added in the future. Welcome to discuss in the comment area!