📜  如何在Python使用 NumPy 获取矩阵的维数?

📅  最后修改于: 2022-05-13 01:54:21.228000             🧑  作者: Mango

如何在Python使用 NumPy 获取矩阵的维数?

在本文中,我们将讨论如何使用 NumPy 获取矩阵的维数。可以使用ndarray()方法的ndim参数找到它。

方法:

  • 使用 numpy 包创建一个 n 维矩阵。
  • 使用 numpy 数组可用的ndim属性作为numpy_array_name.ndim来获取维数。
  • 或者,我们可以使用shape属性来获取每个维度的大小,然后使用len()函数获取维度数。
  • 使用 numpy.array()函数将列表转换为 numpy 数组,并使用上述两种方法之一来获取维数。

示例 1:

Python3
import numpy as np
  
  
x = np.arange(12).reshape((3, 4))
    
print(x.ndim)


Python3
import numpy as np
  
  
# create numpy arrays
# 1-d numpy array
_1darr = np.arange(4)      
  
# 2-d numpy array
_2darr = np.arange(15).reshape((5, 3))     
  
# 3-d numpy array
_3darr = np.arange(18).reshape((3, 2, 3))  
  
# printing the type of value obtained using 
# attribute 'ndim'
print("Type of value obtained: ", type(_1darr.ndim))
  
# printing the dimensions of each numpy array
print("Dimensions in _1darr are: ", _1darr.ndim)
print("Dimensions in _2darr are: ", _2darr.ndim)
print("Dimensions in _3darr are: ", _3darr.ndim)
  
# numpy_arr.shape is the number of elements in
# each dimension numpy_arr.shape returns a tuple
# len() of the returned tuple is also gives number
# of dimensions
print("Dimensions in _3darr are: ", len(_3darr.shape))
  
# Use numpy.array() function to convert a list to
# numpy array
__1darr = np.array([5, 4, 1, 3, 2])
__2darr = np.array([[5, 4],[1,2], [4,5]])
print("Dimensions in __1darr are: ", __1darr.ndim)
print("Dimensions in __2darr are: ", __2darr.ndim)


输出:

2

示例 2:

蟒蛇3

import numpy as np
  
  
# create numpy arrays
# 1-d numpy array
_1darr = np.arange(4)      
  
# 2-d numpy array
_2darr = np.arange(15).reshape((5, 3))     
  
# 3-d numpy array
_3darr = np.arange(18).reshape((3, 2, 3))  
  
# printing the type of value obtained using 
# attribute 'ndim'
print("Type of value obtained: ", type(_1darr.ndim))
  
# printing the dimensions of each numpy array
print("Dimensions in _1darr are: ", _1darr.ndim)
print("Dimensions in _2darr are: ", _2darr.ndim)
print("Dimensions in _3darr are: ", _3darr.ndim)
  
# numpy_arr.shape is the number of elements in
# each dimension numpy_arr.shape returns a tuple
# len() of the returned tuple is also gives number
# of dimensions
print("Dimensions in _3darr are: ", len(_3darr.shape))
  
# Use numpy.array() function to convert a list to
# numpy array
__1darr = np.array([5, 4, 1, 3, 2])
__2darr = np.array([[5, 4],[1,2], [4,5]])
print("Dimensions in __1darr are: ", __1darr.ndim)
print("Dimensions in __2darr are: ", __2darr.ndim)

输出:

Type of value obtained:  
Dimensions in _1darr are:  1
Dimensions in _2darr are:  2
Dimensions in _3darr are:  3
Dimensions in _3darr are:  3
Dimensions in __1darr are:  1
Dimensions in __2darr are:  2