Numpy数组维度

Question

我目前正在尝试学习Numpy和Python。给出以下数组：

import numpy as np
a = np.array([[1,2],[1,2]])

是否有一个函数返回a的维度（例如，a是2乘2的数组）？

size()返回4，这无济于事。

Answer 1

是.shape：

  

ndarray。的形状
  数组维度的元组。

因此：

>>> a.shape
(2, 2)

Answer 2

首先：

按照惯例，在Python世界中，numpy的快捷方式是np，所以：

In [1]: import numpy as np

In [2]: a = np.array([[1,2],[3,4]])

第二

在Numpy中，维度，轴/轴，形状是相关的，有时也是类似的概念：

尺寸

在数学/物理中，维度或维度被非正式地定义为指定空间中任何点所需的最小坐标数。但是在 Numpy 中，根据numpy doc，它与轴/轴相同：

  

在Numpy中，尺寸称为轴。轴数是等级。

In [3]: a.ndim  # num of dimensions/axes, *Mathematics definition of dimension*
Out[3]: 2

轴线/轴

nth 坐标，用于索引Numpy中的array。多维数组每个轴可以有一个索引。

In [4]: a[1,0]  # to index `a`, we specific 1 at the first axis and 0 at the second axis.
Out[4]: 3  # which results in 3 (locate at the row 1 and column 0, 0-based index)

形状

描述每个可用轴上的数据（或范围）。

In [5]: a.shape
Out[5]: (2, 2)  # both the first and second axis have 2 (columns/rows/pages/blocks/...) data

Answer 3

import numpy as np   
>>> np.shape(a)
(2,2)

如果输入不是numpy数组而是列表列表

，也可以使用

>>> a = [[1,2],[1,2]]
>>> np.shape(a)
(2,2)

或元组元组

>>> a = ((1,2),(1,2))
>>> np.shape(a)
(2,2)

Answer 4

您可以使用.shape

In: a = np.array([[1,2,3],[4,5,6]])
In: a.shape
Out: (2, 3)
In: a.shape[0] # x axis
Out: 2
In: a.shape[1] # y axis
Out: 3

Answer 5

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shape

Answer 6

您可以使用.ndim来标注尺寸，而.shape可以知道确切的尺寸

var = np.array([[1,2,3,4,5,6], [1,2,3,4,5,6]])

var.ndim
# displays 2

var.shape
# display 6, 2

您可以使用.reshape函数更改尺寸

var = np.array([[1,2,3,4,5,6], [1,2,3,4,5,6]]).reshape(3,4)

var.ndim
#display 2

var.shape
#display 3, 4

Answer 7

a.shape只是np.info()的有限版本。检查一下：

import numpy as np
a = np.array([[1,2],[1,2]])
np.info(a)

出

class:  ndarray
shape:  (2, 2)
strides:  (8, 4)
itemsize:  4
aligned:  True
contiguous:  True
fortran:  False
data pointer: 0x27509cf0560
byteorder:  little
byteswap:  False
type: int32

Answer 8

rows = a.shape[0] # 2 
cols = a.shape[1] # 2
a.shape #(2,2)
a.size # rows * cols = 4