标签归档:dimensions

块数组尺寸

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。

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

.shape

ndarray。 数组尺寸的形状
元组。

从而:

>>> a.shape
(2, 2)

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numpynp,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

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)
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

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

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

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]])

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)

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

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