根据官方文档主要内容如下：
Array conversion(数据转换)
Array shape manipulation(形状操作)
Array Item selection and manipulation(数据选择与操作)
Array Calculation(数据计算)
Array Special methods(主要涉及算数运算)
由于涉及的方法很多主要介绍联系一些实用的即可，其他由特殊需求可直接查看官方文档
Array conversion
| ndarray.item(args) | Copy an element of an array to a standard Python scalar and return it. |
| ndarray.tolist() | Return the array as an a.ndim-levels deep nested list of Python scalars. |
| ndarray.itemset(args) | Insert scalar into an array (scalar is cast to array’s dtype, if possible) |
| ndarray.tostring([order]) | Construct Python bytes containing the raw data bytes in the array. |
| ndarray.tobytes([order]) | Construct Python bytes containing the raw data bytes in the array. |
| ndarray.tofile(fid[, sep, format]) | Write array to a file as text or binary (default). |
| ndarray.dump(file) | Dump a pickle of the array to the specified file. |
| ndarray.dumps() | Returns the pickle of the array as a string. |
| ndarray.astype(dtype[, order, casting, …]) | Copy of the array, cast to a specified type. |
| ndarray.byteswap([inplace]) | Swap the bytes of the array elements |
| ndarray.copy([order]) | Return a copy of the array. |
| ndarray.view([dtype, type]) | New view of array with the same data. |
| ndarray.getfield(dtype[, offset]) | Returns a field of the given array as a certain type. |
| ndarray.setflags([write, align, uic]) | Set array flags WRITEABLE, ALIGNED, (WRITEBACKIFCOPY and UPDATEIFCOPY), respectively. |
| ndarray.fill(value) | Fill the array with a scalar value. |
Shape manipulation
For reshape, resize, and transpose, the single tuple argument may be replaced with n integers which will be interpreted as an n-tuple.
| ndarray.reshape(shape[, order]) | Returns an array containing the same data with a new shape. |
| ndarray.resize(new_shape[, refcheck]) | Change shape and size of array in-place. |
| ndarray.transpose(*axes) | Returns a view of the array with axes transposed. |
| ndarray.swapaxes(axis1, axis2) | Return a view of the array with axis1 and axis2 interchanged. |
| ndarray.flatten([order]) | Return a copy of the array collapsed into one dimension. |
| ndarray.ravel([order]) | Return a flattened array. |
| ndarray.squeeze([axis]) | Remove single-dimensional entries from the shape of a. |
#先定义一个随机数数组和一个一维数组
import numpy as np
rmatrix = np.random.randint(10,size=(3,3))
D1 = np.array([1])
print(D1)
print(rmatrix)
[1]
[[7 4 0]
[4 5 3]
[4 3 6]]
# item函数的使用返回数组值
print(D1.item()) #item方法没有设置参数这只对size=1的数组有效
print(rmatrix.item(0)) #item方法中其实内部调用了flat方法将数组降成一维然后在取值
print(rmatrix.item(4))
print(rmatrix.item(8))
print(rmatrix.item((0,0))) #注意用下表组合的形式时不能超过数组维数
print(rmatrix.item()) #注意此种方式是错误的
1
7
5
6
7
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-8-c84aee7a18f1> in <module>
5 print(rmatrix.item(8))
6 print(rmatrix.item((0,0)))
----> 7 print(rmatrix.item()) #注意此种方式是错误的
ValueError: can only convert an array of size 1 to a Python scalar
# itemset进行值设置
rmatrix.itemset(0,0)
rmatrix.itemset((1,1),0)
rmatrix.itemset((2,2),0)
print(rmatrix)
[[0 4 0]
[4 0 3]
[4 3 0]]
# tolist,tostring,tobytes进行数据结构转换
print(rmatrix.tolist())
print(rmatrix.tostring())
print(rmatrix.tobytes())
print(rmatrix.tofile('matrix.text',sep = '@',format = '%03d'))
#第一个参数可以是：文件名 与 一个打开的文件描述符对象。
#文件名的位置：如果不使用绝对位置，相对位置就是保存在ipynb文件作为相对位置。
#输出结果：000@004@000@004@000@003@004@003@000
[[0, 4, 0], [4, 0, 3], [4, 3, 0]]
b'\x00\x00\x00\x00\x04\x00\x00\x00\x00\x00\x00\x00\x04\x00\x00\x00\x00\x00\x00\x00\x03\x00\x00\x00\x04\x00\x00\x00\x03\x00\x00\x00\x00\x00\x00\x00'
b'\x00\x00\x00\x00\x04\x00\x00\x00\x00\x00\x00\x00\x04\x00\x00\x00\x00\x00\x00\x00\x03\x00\x00\x00\x04\x00\x00\x00\x03\x00\x00\x00\x00\x00\x00\x00'
None
# astype对数组进行数据处理后return一个新数组
a = np.array([[1.1,2.2,3.3],[4.4,5.5,6.6],[7.7,8.8,9.9]],np.float)
print(a)
print(a.astype(np.int32))
# copy方法使用
print(a.copy()) # 直接copy一个数组
[[1.1 2.2 3.3]
[4.4 5.5 6.6]
[7.7 8.8 9.9]]
[[1 2 3]
[4 5 6]
[7 8 9]]
[[1.1 2.2 3.3]
[4.4 5.5 6.6]
[7.7 8.8 9.9]]
# 数据视图view
print(a.view(dtype=np.int32)) #dtype设置数据类型
print(a.view(type=np.ndarray) #数组类型
print(a.view(type=np.matrix)) #矩阵类型
[[-1717986918 1072798105 -1717986918 1073846681 1717986918 1074423398]
[-1717986918 1074895257 0 1075183616 1717986918 1075471974]
[ -858993459 1075760332 -1717986918 1075943833 -858993459 1076088012]]
[[1.1 2.2 3.3]
[4.4 5.5 6.6]
[7.7 8.8 9.9]]
[[1.1 2.2 3.3]
[4.4 5.5 6.6]
[7.7 8.8 9.9]]
c = a.view(type=np.ndarray)
d = a.view(type=np.matrix)
print(type(c))
print(type(d))
print(c.shape)
print(d.shape)
# 注意数组能是任意维数的但是矩阵只能是二维的
print(c.reshape(1,3,3))
print(d.reshape(1,3,3))
<class 'numpy.ndarray'>
<class 'numpy.matrix'>
(3, 3)
(3, 3)
[[[1.1 2.2 3.3]
[4.4 5.5 6.6]
[7.7 8.8 9.9]]]
[[1.1 2.2 3.3]
[4.4 5.5 6.6]
[7.7 8.8 9.9]]
print(np.array([[[1]]]))#数组能是任意维不会报错
print(np.mat([[[1]]]))#矩阵只能是2维会报错
[[[1]]]
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-56-8f3de4ef06e7> in <module>
1 print(np.array([[[1]]]))#数组能是任意维不会报错
----> 2 print(np.mat([[[1]]]))#矩阵只能是2维会报错
D:\Anaconda\lib\site-packages\numpy\matrixlib\defmatrix.py in asmatrix(data, dtype)
69
70 """
---> 71 return matrix(data, dtype=dtype, copy=False)
72
73
D:\Anaconda\lib\site-packages\numpy\matrixlib\defmatrix.py in __new__(subtype, data, dtype, copy)
149 shape = arr.shape
150 if (ndim > 2):
--> 151 raise ValueError("matrix must be 2-dimensional")
152 elif ndim == 0:
153 shape = (1, 1)
ValueError: matrix must be 2-dimensional
#现在我们来看一下view与copy的差别在哪里
print(a)
e = a.view()
c = a.copy()
print(a)
print(c)
[[1.1 2.2 3.3]
[4.4 5.5 6.6]
[7.7 8.8 9.9]]
[[1.1 2.2 3.3]
[4.4 5.5 6.6]
[7.7 8.8 9.9]]
[[1.1 2.2 3.3]
[4.4 5.5 6.6]
[7.7 8.8 9.9]]
c[0][0] = 0
print(c)
print(a) #修改copy数组的值不会影响到原来的值
[[0. 2.2 3.3]
[4.4 5.5 6.6]
[7.7 8.8 9.9]]
[[1.1 2.2 3.3]
[4.4 5.5 6.6]
[7.7 8.8 9.9]]
e[0][0] = 0 #因为视图的数据与原数组的数据空间是一样的
print(e) #修改视图数据会改变原数组数据
print(a)
print(a.reshape(1,9))
[[0. 2.2 3.3]
[4.4 5.5 6.6]
[7.7 8.8 9.9]]
[[0. 2.2 3.3]
[4.4 5.5 6.6]
[7.7 8.8 9.9]]
#原数组视图copy数组他们的结构存储地址都是不同的
#原数组的数据和视图数据的引用是一致的
#改变视图的shape不会改变原数组的shape
print(id(a))
print(id(c))
print(id(e))
2253826302000
2253826304880
2253826304160
#填充数据fill
a.fill(6)
print(a)
[[6. 6. 6.]
[6. 6. 6.]
[6. 6. 6.]]
m = np.random.randint(10,size = (3,4))
print(m)
[[6 3 5 7]
[3 3 0 6]
[4 2 3 6]]
print(m.reshape(4,3)) #有返回值不会改变m
print(m)
print(m.resize(2,6)) #resize没有返回值会改变m
print(m)
[[6 3 5]
[7 3 3]
[0 6 4]
[2 3 6]]
[[6 3 5 7 3 3]
[0 6 4 2 3 6]]
None
[[6 3 5 7 3 3]
[0 6 4 2 3 6]]
transpose与swapaxes函数
n = np.random.randint(10,size=(5,6))
print(n)
[[9 7 8 7 4 9]
[0 4 8 8 7 3]
[8 4 6 6 3 7]
[3 2 7 2 5 1]
[3 9 1 2 3 2]]
print(n.transpose())
print(n)
[[9 0 8 3 3]
[7 4 4 2 9]
[8 8 6 7 1]
[7 8 6 2 2]
[4 7 3 5 3]
[9 3 7 1 2]]
[[9 7 8 7 4 9]
[0 4 8 8 7 3]
[8 4 6 6 3 7]
[3 2 7 2 5 1]
[3 9 1 2 3 2]]
print(n.transpose(1,0))
print(n.transpose(0,1))
[[9 0 8 3 3]
[7 4 4 2 9]
[8 8 6 7 1]
[7 8 6 2 2]
[4 7 3 5 3]
[9 3 7 1 2]]
[[9 7 8 7 4 9]
[0 4 8 8 7 3]
[8 4 6 6 3 7]
[3 2 7 2 5 1]
[3 9 1 2 3 2]]
|-transpose中参数说明:
如果是二维数组,第一个参数代表行维度,第二个参数代表是列维度默认情况下
第一个参数值是0,第二个参数值是1,把0和1交换就意味着交换两个维度把行变
成列,列变成行
如果是多维的那么原来数组参数的对应维度取值为0,1,2,3...依次增加,transpose中要想交换维度即进行坐标
轴变换交换将维度取值交换写成参数即可拿下面的三维数组举例
D3 = np.random.randint(10,size=(3,3,3))
print(D3)
[[[2 2 3]
[2 3 9]
[8 6 4]]
[[4 3 2]
[4 3 8]
[7 1 0]]
[[2 9 4]
[5 3 2]
[0 1 5]]]
#下面两个变换等价因为transpose不加参数的时候
#就是将默认参数进行了一次左移
print(D3.transpose())
print(D3.transpose(1,2,0))
[[[2 4 2]
[2 4 5]
[8 7 0]]
[[2 3 9]
[3 3 3]
[6 1 1]]
[[3 2 4]
[9 8 2]
[4 0 5]]]
[[[2 4 2]
[2 3 9]
[3 2 4]]
[[2 4 5]
[3 3 3]
[9 8 2]]
[[8 7 0]
[6 1 1]
[4 0 5]]]
D = D3.transpose(1,2,0)
print(D3[1][2][0])
print(D[2][0][1])
#下面用一段简单的for循环遍历D3看是否与transpose(1,2,0)D对应元素位置相等
L = 0
for i in range(3):
for j in range(3):
for k in range(3):
if D3[i][j][k]==D[j][k][i]:
L = L + 1 #记录对应元素相等的数量结果应该为3x3x3=27
else:
break
pass
pass
pass
print(L)
7
7
27
|-由上面的代码示例可以很清楚的了解transpose中参数的含义:
比如三维的transpose(2,1,0),因为原来数组的参数值是0,1,2
现在变成了2,1,0这就相当于是说原来数组中坐标为[i][j][k]的元素
在变换后的数组中坐标为[k][j][i]就这么个意思
其实反映在空间直角坐标系中x,y,z轴就是三个axis,transpose也就相当于是
进行坐标轴变换
# ndarray.swapaxes(axis1,axis2)交换两维度参数顺序任意
k = np.random.randint(10,size=(2,3,4))
print(k)
[[[0 8 1 9]
[4 9 1 0]
[9 3 2 4]]
[[1 3 1 3]
[9 4 3 3]
[1 0 6 8]]]
print(k.transpose(0,2,1))
print(k.swapaxes(1,2))
[[[0 4 9]
[8 9 3]
[1 1 2]
[9 0 4]]
[[1 9 1]
[3 4 0]
[1 3 6]
[3 3 8]]]
[[[0 4 9]
[8 9 3]
[1 1 2]
[9 0 4]]
[[1 9 1]
[3 4 0]
[1 3 6]
[3 3 8]]]
print(k.swapaxes(2,1))
[[[0 4 9]
[8 9 3]
[1 1 2]
[9 0 4]]
[[1 9 1]
[3 4 0]
[1 3 6]
[3 3 8]]]
#一下三式是想等的
print(k.swapaxes(0,1))
print(k.swapaxes(1,0))
print(k.transpose(1,0,2))
[[[0 8 1 9]
[1 3 1 3]]
[[4 9 1 0]
[9 4 3 3]]
[[9 3 2 4]
[1 0 6 8]]]
[[[0 8 1 9]
[1 3 1 3]]
[[4 9 1 0]
[9 4 3 3]]
[[9 3 2 4]
[1 0 6 8]]]
[[[0 8 1 9]
[1 3 1 3]]
[[4 9 1 0]
[9 4 3 3]]
[[9 3 2 4]
[1 0 6 8]]]
flatten,ravel,squezze函数的使用
P = np.random.randint(10,size=(3,4,1))
print(P)
[[[8]
[3]
[6]
[3]]
[[2]
[5]
[1]
[4]]
[[2]
[7]
[0]
[4]]]
#flatten与ravel的作用一样的都是转化成一维
#注意flat函数是变成一维迭代器
print(P.flatten())
print(P.ravel())
print(P)
[8 3 6 3 2 5 1 4 2 7 0 4]
[8 3 6 3 2 5 1 4 2 7 0 4]
[[[8]
[3]
[6]
[3]]
[[2]
[5]
[1]
[4]]
[[2]
[7]
[0]
[4]]]
print(P.squeeze()) #删除长度为一的维度
[[8 3 6 3]
[2 5 1 4]
[2 7 0 4]]
print(P.shape)
(3, 4, 1)
#如果有多个维度为1则需要指明否则全部删除
print(P.squeeze(axis=2))
[[8 3 6 3]
[2 5 1 4]
[2 7 0 4]]
j = np.random.randint(10,size=(3,1,1))
print(j)
print(j.squeeze(axis=2))
print(j.squeeze())
[[[7]]
[[7]]
[[7]]]
[[7]
[7]
[7]]
[7 7 7]
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