一.基础：Numpy的主要数据类型是ndarray，即多维数组。它有以下几个属性： ndarray.ndim：数组的维数
ndarray.shape：数组每一维的大小
ndarray.size：数组中全部元素的数量
ndarray.dtype：数组中元素的类型（numpy.int32, numpy.int16, and numpy.float64等）
ndarray.itemsize：每个元素占几个字节 例子： >>> import numpy as np
>>> a = np.arange(15).reshape(3, 5)
>>> a
array([[ 0, 1, 2, 3, 4],
[ 5, 6, 7, 8, 9],
[10, 11, 12, 13, 14]])
>>> a.shape
(3, 5)
>>> a.ndim2>>> a.dtype.name'int64'>>> a.itemsize8>>> a.size15>>> type(a)
<type 'numpy.ndarray'>
>>> b = np.array([6, 7, 8])
>>> b
array([6, 7, 8])
>>> type(b)
<type 'numpy.ndarray'>
二.创建数组：使用array函数讲tuple和list转为array： >>> import numpy as np>>> a = np.array([2,3,4])>>> a
array([2, 3, 4])>>> a.dtype
dtype('int64')>>> b = np.array([1.2, 3.5, 5.1])>>> b.dtype
dtype('float64')
多维数组： >>> b = np.array([(1.5,2,3), (4,5,6)])
>>> b
array([[ 1.5, 2. , 3. ],
[ 4. , 5. , 6. ]])
生成数组的同时指定类型： >>> c = np.array( [ [1,2], [3,4] ], dtype=complex )
>>> c
array([[ 1.+0.j, 2.+0.j],
[ 3.+0.j, 4.+0.j]])
生成数组并赋为特殊值：
ones：全1
zeros：全0
empty：随机数，取决于内存情况 >>> np.zeros( (3,4) )
array([[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.]])
>>> np.ones( (2,3,4), dtype=np.int16 ) # dtype can also be specified
array([[[ 1, 1, 1, 1],
[ 1, 1, 1, 1],
[ 1, 1, 1, 1]],
[[ 1, 1, 1, 1],
[ 1, 1, 1, 1],
[ 1, 1, 1, 1]]], dtype=int16)
>>> np.empty( (2,3) ) # uninitialized, output may vary
array([[ 3.73603959e-262, 6.02658058e-154, 6.55490914e-260],
[ 5.30498948e-313, 3.14673309e-307, 1.00000000e+000]])
生成均匀分布的array：
arange（最小值，最大值，步长）（左闭右开）
linspace（最小值，最大值，元素数量） >>> np.arange( 10, 30, 5 )array([10, 15, 20, 25])
>>> np.arange( 0, 2, 0.3 ) # it accepts float argumentsarray([ 0. , 0.3, 0.6, 0.9, 1.2, 1.5, 1.8])
>>> np.linspace( 0, 2, 9 ) # 9 numbers from 0 to 2array([ 0. , 0.25, 0.5 , 0.75, 1. , 1.25, 1.5 , 1.75, 2. ])
>>> x = np.linspace( 0, 2*pi, 100 ) # useful to evaluate function at lots of points
三.基本运算:整个array按顺序参与运算： >>> a = np.array( [20,30,40,50] )>>> b = np.arange( 4 )>>> b
array([0, 1, 2, 3])>>> c = a-b>>> c
array([20, 29, 38, 47])>>> b**2array([0, 1, 4, 9])>>> 10*np.sin(a)
array([ 9.12945251, -9.88031624, 7.4511316 , -2.62374854])>>> a<35array([ True, True, False, False], dtype=bool)
两个二维使用*符号仍然是按位置一对一相乘，如果想表示矩阵乘法，使用dot： >>> A = np.array( [[1,1],
... [0,1]] )
>>> B = np.array( [[2,0],
... [3,4]] )
>>> A*B # elementwise product
array([[2, 0],
[0, 4]])
>>> A.dot(B) # matrix product
array([[5, 4],
[3, 4]])
>>> np.dot(A, B) # another matrix product
array([[5, 4],
[3, 4]])
内置函数（min,max,sum)，同时可以使用axis指定对哪一维进行操作： >>> b = np.arange(12).reshape(3,4)
>>> b
array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
>>>
>>> b.sum(axis=0) # sum of each column
array([12, 15, 18, 21])
>>>
>>> b.min(axis=1) # min of each row
array([0, 4, 8])
>>>
>>> b.cumsum(axis=1) # cumulative sum along each row
array([[ 0, 1, 3, 6],
[ 4, 9, 15, 22],
[ 8, 17, 27, 38]])
Numpy同时提供很多全局函数 >>> B = np.arange(3)
>>> Barray([0, 1, 2])>>> np.exp(B)array([ 1. , 2.71828183, 7.3890561 ])>>> np.sqrt(B)array([ 0. , 1. , 1.41421356])>>> C = np.array([2., -1., 4.])>>> np.add(B, C)array([ 2., 0., 6.])
四.寻址，索引和遍历：一维数组的遍历语法和python list类似： >>> a = np.arange(10)**3>>> aarray([ 0, 1, 8, 27, 64, 125, 216, 343, 512, 729])
>>> a[2]8>>> a[2:5]
array([ 8, 27, 64])
>>> a[:6:2] = -1000 # equivalent to a[0:6:2] = -1000; from start to position 6, exclusive, set every 2nd element to -1000>>> aarray([-1000, 1, -1000, 27, -1000, 125, 216, 343, 512, 729])
>>> a[ : :-1] # reversed aarray([ 729, 512, 343, 216, 125, -1000, 27, -1000, 1, -1000])
>>> for i in a:... print(i**(1/3.))...nan1.0nan3.0nan5.06.07.08.09.0
多维数组的访问通过给每一维指定一个索引，顺序是先高维再低维： >>> def f(x,y):... return 10*x+y
...>>> b = np.fromfunction(f,(5,4),dtype=int)>>> b
array([[ 0, 1, 2, 3],
[10, 11, 12, 13],
[20, 21, 22, 23],
[30, 31, 32, 33],
[40, 41, 42, 43]])>>> b[2,3]23>>> b[0:5, 1] # each row in the second column of barray([ 1, 11, 21, 31, 41])>>> b[ : ,1] # equivalent to the previous examplearray([ 1, 11, 21, 31, 41])>>> b[1:3, : ] # each column in the second and third row of barray([[10, 11, 12, 13],
[20, 21, 22, 23]])
When fewer indices are provided than the number of axes, the missing indices are considered complete slices:
>>>>>> b[-1] # the last row. Equivalent to b[-1,:]array([40, 41, 42, 43])
…符号表示将所有未指定索引的维度均赋为 ： ，：在python中表示该维所有元素： >>> c = np.array( [[[ 0, 1, 2], # a 3D array (two stacked 2D arrays)... [ 10, 12, 13]],... [[100,101,102],... [110,112,113]]])
>>> c.shape
(2, 2, 3)
>>> c[1,...] # same as c[1,:,:] or c[1]array([[100, 101, 102],
[110, 112, 113]])
>>> c[...,2] # same as c[:,:,2]array([[ 2, 13],
[102, 113]])
遍历：
如果只想遍历整个array可以直接使用： >>> for row in b:... print(row)...[0 1 2 3]
[10 11 12 13]
[20 21 22 23]
[30 31 32 33]
[40 41 42 43]
但是如果要对每个元素进行操作，就要使用flat属性，这是一个遍历整个数组的迭代器 >>> for element in b.flat:... print(element)...012310111213202122233031323340414243
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