Python点滴(一)

以下实例展示了split()函数的使用方法：

<span style="font-size:14px;">#!/usr/bin/python
str = "Line1-abcdef \nLine2-abc \nLine4-abcd";
print str.split( );
print str.split(' ', 1 );</span>

以上实例输出结果如下：

<span style="font-size:14px;">['Line1-abcdef', 'Line2-abc', 'Line4-abcd']</span>

<span style="font-size:14px;">['Line1-abcdef', '\nLine2-abc \nLine4-abcd']</span>

调用mat()函数可以将数组转化为矩阵，

eval()函数十分强大，官方demo解释为：将字符串str当成有效的表达式来求值并返回计算结果；

a = "[[1,2], [3,4], [5,6], [7,8], [9,0]]"

<span style="font-size:14px;"><span style="font-family:Microsoft YaHei;">
b = eval(a)

b
[[1, 2], [3, 4], [5, 6], [7, 8], [9, 0]]</span></span>

dot()的用法，可以理解为矩阵相乘

>>> np.dot(3, 4)
12

Neither argument is complex-conjugated:

>>>

>>> np.dot([2j, 3j], [2j, 3j])
(-13+0j)

For 2-D arrays it is the matrix product:

>>>

>>> a = [[1, 0], [0, 1]]
>>> b = [[4, 1], [2, 2]]
>>> np.dot(a, b)
array([[4, 1],
[2, 2]])

listFromLine[0:0] = ['1']可以在列表开头添加‘1’

enumerate 函数用于遍历序列中的元素以及它们的下标：

>>> for i,j in enumerate(('a','b','c')):

 print i,j

 

0 a

1 b

2 c

ravel的用法

>>> a.ravel() # 平坦化数组  

array([ 7., 5., 9., 3., 7., 2., 7., 8., 6., 8., 3., 2.])  

>>> a.shape= (6, 2)  

>>> a.transpose()  

array([[ 7., 9., 7., 7., 6., 3.],  

           [ 5., 3., 2., 8., 8., 2.]])
 

numpy.fill的用法

>>> a = np.array([1, 2])
>>> a.fill(0)
>>> a
array([0, 0])
>>> a = np.empty(2)
>>> a.fill(1)
>>> a
array([ 1.,  1.])

shape的用法，返回的是（行数，列数）的形式

1 >>> a=mat([[1,2,3],[5,6,9]]);
2 >>> a
3 matrix([[1, 2, 3],
4         [5, 6, 9]])
5 >>> shape(a)[0]          //shape=[2,3]
6 2
7 >>> shape(a)[1]
8 3

range的用法

>>> range(1,10) 　　——>不包括10
　　　　[1, 2, 3, 4, 5, 6, 7, 8, 9]
　　>>>range(1,10,2)　　——>1到10，间隔为2(不包括10)
　　　　[1, 3, 5, 7, 9]
　　>>>range(10)　　　　——>0到10，不包括10
　　[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

Array=[2,3,9,1,4,7,6,8]

>>>Array[0:]　　——>切片从前面序号“0”开始到结尾，包括“0”位
　　[2, 3, 9, 1, 4, 7, 6, 8]

　　>>>Array[:-1]　　——>切片从后面序号“-1”到最前，不包括“-1”位
　　[2, 3, 9, 1, 4, 7, 6]

　　>>>Array[3:-2]　　——>切从前面序号“3”开始(包括)到从后面序号“-2”结束(不包括)
　　[1, 4, 7]

　　>>>Array[3::2]　　——>从前面序号“3”(包括)到最后，其中分隔为“2”
　　[1, 7, 8]

　　>>>Array[::2]　　——>从整列表中切出，分隔为“2”
　　[2, 9, 4, 6]

　　>>> Array[3::]　　——>从前面序号“3”开始到最后，没有分隔
　　[1, 4, 7, 6, 8]

　　>>> Array[3::-2]　　——>从前面序号“3”开始，往回数第二个，因为分隔为“-2”
　　[1, 3]
　　
　　>>> Array[-1]　　　　——>此为切出最后一个
　　8

　　>>>Array[::-1]　　　　——>此为倒序
　　[8, 6, 7, 4, 1, 9, 3, 2]

fig.add_subplot()的用法

import matplotlib.pyplot as plt
from numpy import *
fig = plt.figure()
ax = fig.add_subplot(349)
ax.plot(x,y)
plt.show()

参数349的意思是：将画布分割成3行4列，图像画在从左到右从上到下的第9块，如下图：

import matplotlib.pyplot as plt
from numpy import *
fig = plt.figure()
ax = fig.add_subplot(2,1,1)
ax.plot(x,y)
ax = fig.add_subplot(2,2,3)
ax.plot(x,y)
plt.show()

ax.scatter用来画散点图

以下展示了使用 uniform() 方法的实例：

#!/usr/bin/python
# -*- coding: UTF-8 -*-

import random

print "uniform(5, 10) 的随机数为 : ",  random.uniform(5, 10)

print "uniform(7, 14) 的随机数为 : ",  random.uniform(7, 14)

以上实例运行后输出结果为：

uniform(5, 10) 的随机数为 :  6.98774810047
uniform(7, 14) 的随机数为 :  12.2243345905

def __init__(self) 是类实体化时自动运行的初始化函数，
def __init__(self)是初始化基类，
self 就相当于一个对象，把每个变量初始化，下边就是调用方法了 ， 对象.属性，对象.赋值。

set可以用来消除海量列表里的重复元素

>>> a = [11,22,33,44,11,22]  
>>> b = set(a)  
>>> b  
set([33, 11, 44, 22])  

矩阵相乘：





exp()的用法：

import math

math.exp( x )

sum中axis=1求和的意义



numpy中repeat的用法

<span style="font-size:14px;">>>> repeat(1,4)
array([1, 1, 1, 1])
>>>
>>> a=[10,20]
>>> repeat(a,[3,2])
array([10, 10, 10, 20, 20])
>>> b=[[10,20],[30,40]]
>>> b
[[10, 20], [30, 40]]
>>> repeat(b,[3,2],axis=0)
array([[10, 20],
[10, 20],
[10, 20],
[30, 40],
[30, 40]])
>>> repeat(b,[3,2],axis=1)
array([[10, 10, 10, 20, 20],
[30, 30, 30, 40, 40]])</span>

<span style="font-size:14px;">>>> a
[[1, 2, 3], [4, 5, 6]]
>>> numpy.argmax(a,axis=0)       //axis=0表示一列一列的来进行比较输出最大值所对应的 行
array([1, 1, 1], dtype=int64)
>>> numpy.argmax(a,axis=1)       //axis=1表示一行一行的来进行比较输出最大值所对应的 列
array([2, 2], dtype=int64)
>>> </span>

<span style="font-size:14px;">>>> mat([[1.0,1.2,2.0]])*(mat([[2.0,1.0,3.0]]).transpose())
matrix([[ 9.2]])                       //注意始终是要有两个中括号的
>>> m=mat([[1.0,1.2,2.0]])*(mat([[2.0,1.0,3.0]]).transpose())
>>> m
matrix([[ 9.2]])
>>> m.argmax(1)
<strong>matrix([[0]], dtype=int64)</strong>
>>> m.argmax(1)[0,0]
0
>>>
>>> n=mat([[4,5,6,7]])                  //一列一列的寻找最大值
>>> n.argmax(1)[0,0]                    //返回的依然是下角标
3
>>> </span>

数组array与列表list中的元素删除是不同的：

<span style="font-size:14px;">>>> import numpy
>>> numpy.arange(5)
array([0, 1, 2, 3, 4])
>>>
>>> a=[1,2,3]
>>> a.remove(1)
>>> a
[2, 3]
>>>
>>> a.pop(0)   //角标位置
2
>>> a
[3]
>>>
>>>
>>> b=numpy.arange(5)
>>> b
array([0, 1, 2, 3, 4])
>>> b.pop(2)
Traceback (most recent call last):
File "<pyshell#13>", line 1, in <module>
b.pop(2)
AttributeError: 'numpy.ndarray' object has no attribute 'pop'
>>> help('numpy.delete')
Help on function delete in numpy:

numpy.delete = delete(arr, obj, axis=None)
Return a new array with sub-arrays along an axis deleted. For a one
dimensional array, this returns those entries not returned by
`arr[obj]`.

Parameters
----------
arr : array_like
Input array.
obj : slice, int or array of ints
Indicate which sub-arrays to remove.
axis : int, optional
The axis along which to delete the subarray defined by `obj`.
<strong>If `axis` is None, `obj` is applied to the flattened array.</strong>

Returns
-------
out : ndarray
A copy of `arr` with the elements specified by `obj` removed. Note
that `delete` does not occur in-place. If `axis` is None, `out` is
a flattened array.

See Also
--------
insert : Insert elements into an array.
append : Append elements at the end of an array.

Notes
-----
Often it is preferable to use a boolean mask. For example:

>>> mask = np.ones(len(arr), dtype=bool)
>>> mask[[0,2,4]] = False
>>> result = arr[mask,...]

Is equivalent to `np.delete(arr, [0,2,4], axis=0)`, but allows further
use of `mask`.

Examples
--------
>>> arr = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])
>>> arr
array([[ 1,  2,  3,  4],
[ 5,  6,  7,  8],
[ 9, 10, 11, 12]])
>>> np.delete(arr, 1, 0)        //一列一列的看
array([[ 1,  2,  3,  4],
[ 9, 10, 11, 12]])

>>> np.delete(arr, np.s_[::2], 1) //隔两个切片
array([[ 2,  4],
[ 6,  8],
[10, 12]])
>>> np.delete(arr, [1,3,5], None)
array([ 1,  3,  5,  7,  8,  9, 10, 11, 12])

>>> numpy.delete(b,[2],None)
array([0, 1, 3, 4])
>>>
>>>
>>> c=numpy.array([1,2,3],[4,5,6])
Traceback (most recent call last):
File "<pyshell#18>", line 1, in <module>
c=numpy.array([1,2,3],[4,5,6])
TypeError: data type not understood
>>>
>>> c=numpy.array([[1,2,3],[4,5,6]])
>>> c
array([[1, 2, 3],
[4, 5, 6]])
>>> numpy.mat(c)
matrix([[1, 2, 3],
[4, 5, 6]])             // 两个看起来比较相似
>>> </span>

<span style="font-size:14px;">>>>> a = [[1,2,3],[4,5,6],[7,8,9]]        //列表形式
>>>> print a
> [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
>>>> b = a.pop(1)
>>>> print a
> [[1, 2, 3], [7, 8, 9]]
>>>> print b
> [4, 5, 6]
>>>> from numpy import array
>>>> a = array([[1,2,3],[4,5,6],[7,8,9]])
>>>> print a
> [[1 2 3]
> [4 5 6]
> [7 8 9]]
>>>> b = a.pop(1)
>
> Traceback (most recent call last):
>  File "<pyshell#14>", line 1, in <module>
>    b = a.pop(1)
> AttributeError: <strong>'numpy.ndarray' object has no attribute 'pop'</strong>
>>>>
>
> So, what's the "right" way of doing this in numpy?

The closest thing I can come up with, is

In [1]: from numpy import *
In [2]: a = array([[1,2,3],[4,5,6],[7,8,9]])
In [3]: b = a[1]
In [4]: a = delete(a,[1], axis=0)</span>

list dictionary

<span style="font-size:14px;">>>> L=[1,2,3]
>>> D={'a':1,'b':2}
>>> A=L[:]
>>> B=D.copy()
>>> A[1]='Ni'
>>> A
[1, 'Ni', 3]
>>> L
[1, 2, 3]
>>> B['c']='spam'
>>> B
{'a': 1, 'c': 'spam', 'b': 2}
>>> D
{'a': 1, 'b': 2}
</span>

键盘原始输入

<span style="font-size:14px;">>>> while True:
...  reply=raw_input('Enter text:')
...  if reply=='stop':break
...  print reply.upper()
...
Enter text:jj
JJ
Enter text:kk
KK
Enter text:ll
LL
Enter text:jj
JJ
Enter text:aa
AA
Enter text:bb
BB
Enter text:c
C
Enter text:c
C
Enter text:e
E
Enter text:f
F
Enter text:stop
>>>
</span>

<span style="font-size:14px;">>>> L=[1,2,3,4]
>>> while L:
...  front,L=L[0],L[1:]
...  print front,L
...
1 [2, 3, 4]
2 [3, 4]
3 [4]
4 []
</span>

字符串处理合并：

<span style="font-size:14px;">>>> S='spam'
>>> S+='SPAM'
>>> S
'spamSPAM'
</span>

<span style="font-size:14px;">>>> L=[1,2,3,4]
>>> L
[1, 2, 3, 4]
>>> L.extend([5,6])
>>> L
[1, 2, 3, 4, 5, 6]
>>> L+=[7,8]
>>> L
[1, 2, 3, 4, 5, 6, 7, 8]
>>>
</span>

输入输出流

<span style="font-size:14px;">>>> import sys
>>> temp=sys.stdout
>>> sys.stdout=open('log.txt','a')
>>> print 'spam'
>>> print 1,2,3
>>> sys.stdout.close()
>>> sys.stdout=temp
>>> print'back here'
back here
>>> print open('log.txt').read()
spam
1 2 3
</span>

<span style="font-size:14px;">>>> range(5),range(2,5),range(0,10,2)
([0, 1, 2, 3, 4], [2, 3, 4], [0, 2, 4, 6, 8])
>>>
>>>
>>> L=[1,2,3,4,5]
>>> for i in range(len(L)):
...  L[i]+=1
...
>>> L
[2, 3, 4, 5, 6]
</span>

<span style="font-size:14px;">字典压缩合成</span>

<span style="font-size:14px;">>>> keys=['spam','eggs','toast']
>>> vals=[1,3,5]
>>> zip(keys,vals)
[('spam', 1), ('eggs', 3), ('toast', 5)]
</span>

<span style="font-size:14px;">>>> D2={}
>>> for (k,v) in zip(keys,vals): D2[k]=v
...
>>> D2
{'toast': 5, 'eggs': 3, 'spam': 1}
>>>
>>>
>>> f=open('script1.py')
>>> lines=f.readline()
>>> lines
'import sys\n'
>>> lines=[line.rstrip() for line in lines]
>>> lines
['i', 'm', 'p', 'o', 'r', 't', '', 's', 'y', 's', '']
</span>

<span style="font-size:14px;">>>> def f1():
...  x=88
...  f2(x)
...
>>> def f2(x):
...  print x
...
>>> f1()
88
>>>
...
>>>
>>> def func():
...  x=4
...  action=(lambda n:x**n)
...  return action
...
>>>
>>> x=func()          //实例化func()为x    然后自动传参
>>> print x(2)
16
>>>
>>> def f(a,b=2,c=3): print a,b,c
...
>>> f(4)              //自动传参
4 2 3
>>>
>>> def f(a,*pargs,**kargs): print a,pargs,kargs
...
>>> f(1,2,3,x=1,y=2)
1 (2, 3) {'y': 2, 'x': 1}              //元组的形式 字典的形式</span>

*args传参

<span style="font-size:14px;">>>> def min1(*args):
res=args[0]
for arg in args[1:]:
if arg<res:
res=arg
return res

>>> args=(2,3,4,5)
>>> min1(args)
(2, 3, 4, 5)
>>> </span>