内容 隐藏
问题：块数组尺寸
回答 0
回答 1
第一：
第二：
尺寸
轴/轴
形状
First:
Second：
dimension
axis/axes
shape
回答 2
回答 3
回答 4
回答 5
回答 6

问题：块数组尺寸

我目前正在尝试学习Numpy和Python。给定以下数组：

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

有没有返回尺寸的函数a（ega是2 x 2数组）？

size() 返回4并没有太大帮助。

点击查看英文原文

I’m currently trying to learn Numpy and Python. Given the following array:

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

Is there a function that returns the dimensions of a (e.g.a is a 2 by 2 array)?

size() returns 4 and that doesn’t help very much.

回答 0

是.shape：

ndarray。 数组尺寸的形状
元组。

从而：

>>> a.shape
(2, 2)
点击查看英文原文

It is .shape:

ndarray.shape
Tuple of array dimensions.

Thus:

>>> a.shape
(2, 2)

回答 1

第一：

按照惯例，在Python世界中，的快捷方式numpy是np，因此：

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，有时是相似的概念：

尺寸

在“ 数学/物理学”中，维或维数被非正式地定义为指定空间中任何点所需的最小坐标数。但在numpy的，根据numpy的文档，这是相同的轴线/轴：

在Numpy中，尺寸称为轴。轴数为等级。

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

轴/轴

在Numpy中索引an 的第n个坐标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

描述沿每个可用轴有多少数据（或范围）。

In [5]: a.shape
Out[5]: (2, 2)  # both the first and second axis have 2 (columns/rows/pages/blocks/...) data
点击查看英文原文

First:

By convention, in Python world, the shortcut for numpy is np, so:

In [1]: import numpy as np

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

Second：

In Numpy, dimension, axis/axes, shape are related and sometimes similar concepts:

dimension

In Mathematics/Physics, dimension or dimensionality is informally defined as the minimum number of coordinates needed to specify any point within a space. But in Numpy, according to the numpy doc, it’s the same as axis/axes:

In Numpy dimensions are called axes. The number of axes is rank.

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

axis/axes

the nth coordinate to index an array in Numpy. And multidimensional arrays can have one index per axis.

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)

shape

describes how many data (or the range) along each available axis.

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

回答 2

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

如果输入不是numpy数组而是列表列表，则也可以使用

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

或元组的元组

>>> a = ((1,2),(1,2))
>>> np.shape(a)
(2,2)
点击查看英文原文 
import numpy as np   
>>> np.shape(a)
(2,2)

Also works if the input is not a numpy array but a list of lists

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

Or a tuple of tuples

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

回答 3

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
点击查看英文原文

You can use .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

回答 4

您可以使用.ndim尺寸并.shape知道确切尺寸

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功能更改尺寸

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
点击查看英文原文

You can use .ndim for dimension and .shape to know the exact dimension

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

var.ndim
# displays 2

var.shape
# display 6, 2

You can change the dimension using .reshape function

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

回答 5

该shape方法要求它a是一个Numpy ndarray。但是Numpy还可以计算纯python对象的可迭代对象的形状：

np.shape([[1,2],[1,2]])
点击查看英文原文

The shape method requires that a be a Numpy ndarray. But Numpy can also calculate the shape of iterables of pure python objects:

np.shape([[1,2],[1,2]])

回答 6

a.shape只是的受限版本np.info()。看一下这个：

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
点击查看英文原文

a.shape is just a limited version of np.info(). Check this out:

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

Out

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
声明：本站所有文章，如无特殊说明或标注，均为本站原创发布。任何个人或组织，在未征得本站同意时，禁止复制、盗用、采集、发布本站内容到任何网站、书籍等各类媒体平台。如若本站内容侵犯了原著者的合法权益，可联系我们进行处理。