Python Numpy 数组的初始化和基本操作

Python 是一种高级的,动态的,多泛型的编程语言。Python代码很多时候看起来就像是伪代码一样,因此你可以使用很少的几行可读性很高的代码来实现一个非常强大的想法。

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.ndim

2

>>> a.dtype.name

'int64'

>>> a.itemsize

8

>>> a.size

15

>>> 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 arguments

array([ 0. , 0.3, 0.6, 0.9, 1.2, 1.5, 1.8])

>>> np.linspace( 0, 2, 9 ) # 9 numbers from 0 to 2

array([ 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**2

array([0, 1, 4, 9])

>>> 10*np.sin(a)

array([ 9.12945251, -9.88031624, 7.4511316 , -2.62374854])

>>> a<35

array([ 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)

>>> B

array([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

>>> a

array([ 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

>>> a

array([-1000, 1, -1000, 27, -1000, 125, 216, 343, 512, 729])

>>> a[ : :-1] # reversed a

array([ 729, 512, 343, 216, 125, -1000, 27, -1000, 1, -1000])

>>> for i in a:

... print(i**(1/3.))

...

nan

1.0

nan

3.0

nan

5.0

6.0

7.0

8.0

9.0

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 b

array([ 1, 11, 21, 31, 41])

>>> b[ : ,1] # equivalent to the previous example

array([ 1, 11, 21, 31, 41])

>>> b[1:3, : ] # each column in the second and third row of b

array([[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)

...

总结

以上所述是小编给大家介绍的Python Numpy 数组的初始化和基本操作,希望对大家有所帮助,如果大家有任何疑问请给我留言,小编会及时回复大家的。在此也非常感谢大家对网站的支持!

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