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
是.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
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
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]])
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
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
