今天學習了一早上正則表達式。如下內容部分轉載自《讀懂正則表達式就這么簡單》

一、什么是正則表達式

正則表達式是一種特殊的字符串模式，用于匹配一組字符串，就好比用模具做產品，而正則就是這個模具，定義一種規則去匹配符合規則的字符。

1.2 常用的正則匹配工具

在線匹配工具：
1. http://www.regexpal.com/
2. http://rubular.com/
3. http://tools.jb51.net/regex/create_reg
4. txt2re ：這個在線網站支持解析一句話，從中可以生成匹配的正則表達式，且可以生成諸多類型的代碼。語言支持：Perl PHP Python Java Javascript ColdFusion C C++ Ruby VB VBScript J#.net C#.net C++.net VB.net

正則匹配軟件

McTracer

用過幾個之后還是覺得這個是最好用的，支持將正則導成對應的語言如java C# js等還幫你轉義了，Copy直接用就行了很方便，另外支持把正則表達式用法解釋，如哪一段是捕獲分組，哪段是貪婪匹配等等，總之用起來 So Happy .

二正則字符簡單介紹

關于這部分建議跳到：《讀懂正則表達式就這么簡單》
里面有很詳細的介紹。

另外關于python的正則表達式，主要使用re模塊。

我們以任務為導向介紹python正則表達式的用法。
假設給我們下面這段話：

I1113 23:35:50.763059  4460 solver.cpp:218] Iteration 400 (27.3075 iter/s, 0.7324s/20 iters), loss = 0.0202583
I1113 23:35:50.763141  4460 solver.cpp:237]     Train net output #0: rpn_cls_loss = 0.00101873 (* 1 = 0.00101873 loss)
I1113 23:35:50.763165  4460 solver.cpp:237]     Train net output #1: rpn_loss_bbox = 0.0192396 (* 1 = 0.0192396 loss)
I1113 23:35:50.763175  4460 sgd_solver.cpp:105] Iteration 400, lr = 0.001
I1113 23:35:51.751206  4460 solver.cpp:218] Iteration 420 (20.2456 iter/s, 0.987868s/20 iters), loss = 0.00228514
I1113 23:35:51.751341  4460 solver.cpp:237]     Train net output #0: rpn_cls_loss = 0.00140554 (* 1 = 0.00140554 loss)
I1113 23:35:51.751379  4460 solver.cpp:237]     Train net output #1: rpn_loss_bbox = 0.000879596 (* 1 = 0.000879596 loss)
I1113 23:35:51.751410  4460 sgd_solver.cpp:105] Iteration 420, lr = 0.001
I1113 23:35:52.523890  4460 solver.cpp:218] Iteration 440 (25.8933 iter/s, 0.772401s/20 iters), loss = 0.0132958
I1113 23:35:52.523974  4460 solver.cpp:237]     Train net output #0: rpn_cls_loss = 0.00312161 (* 1 = 0.00312161 loss)
I1113 23:35:52.523988  4460 solver.cpp:237]     Train net output #1: rpn_loss_bbox = 0.0101742 (* 1 = 0.0101742 loss)
I1113 23:35:52.523998  4460 sgd_solver.cpp:105] Iteration 440, lr = 0.001
I1113 23:35:53.461998  4460 solver.cpp:218] Iteration 460 (21.3325 iter/s, 0.937539s/20 iters), loss = 0.0154897
I1113 23:35:53.462057  4460 solver.cpp:237]     Train net output #0: rpn_cls_loss = 0.00780452 (* 1 = 0.00780452 loss)
I1113 23:35:53.462069  4460 solver.cpp:237]     Train net output #1: rpn_loss_bbox = 0.00768522 (* 1 = 0.00768522 loss)
I1113 23:35:53.462082  4460 sgd_solver.cpp:105] Iteration 460, lr = 0.001
I1113 23:35:54.356657  4460 solver.cpp:218] Iteration 480 (22.3584 iter/s, 0.894517s/20 iters), loss = 0.00275768
I1113 23:35:54.356729  4460 solver.cpp:237]     Train net output #0: rpn_cls_loss = 0.00107937 (* 1 = 0.00107937 loss)
I1113 23:35:54.356739  4460 solver.cpp:237]     Train net output #1: rpn_loss_bbox = 0.00167831 (* 1 = 0.00167831 loss)
I1113 23:35:54.356748  4460 sgd_solver.cpp:105] Iteration 480, lr = 0.001
I1113 23:35:55.153437  4460 solver.cpp:218] Iteration 500 (25.1734 iter/s, 0.79449s/20 iters), loss = 0.0230187
I1113 23:35:55.153519  4460 solver.cpp:237]     Train net output #0: rpn_cls_loss = 0.0105348 (* 1 = 0.0105348 loss)
I1113 23:35:55.153530  4460 solver.cpp:237]     Train net output #1: rpn_loss_bbox = 0.0124839 (* 1 = 0.0124839 loss)
I1113 23:35:55.153542  4460 sgd_solver.cpp:105] Iteration 500, lr = 0.001
I1113 23:35:56.104395  4460 solver.cpp:218] Iteration 520 (21.0352 iter/s, 0.950785s/20 iters), loss = 0.0144106
I1113 23:35:56.104485  4460 solver.cpp:237]     Train net output #0: rpn_cls_loss = 0.00135394 (* 1 = 0.00135394 loss)
I1113 23:35:56.104504  4460 solver.cpp:237]     Train net output #1: rpn_loss_bbox = 0.0130567 (* 1 = 0.0130567 loss)
I1113 23:35:56.104521  4460 sgd_solver.cpp:105] Iteration 520, lr = 0.001
I1113 23:35:56.854631  4460 solver.cpp:218] Iteration 540 (26.6699 iter/s, 0.749909s/20 iters), loss = 0.0167331
I1113 23:35:56.854696  4460 solver.cpp:237]     Train net output #0: rpn_cls_loss = 0.00285695 (* 1 = 0.00285695 loss)
I1113 23:35:56.854710  4460 solver.cpp:237]     Train net output #1: rpn_loss_bbox = 0.0138762 (* 1 = 0.0138762 loss)
I1113 23:35:56.854720  4460 sgd_solver.cpp:105] Iteration 540, lr = 0.001
I1113 23:35:57.824692  4460 solver.cpp:218] Iteration 560 (20.6206 iter/s, 0.969902s/20 iters), loss = 0.00817935
I1113 23:35:57.824774  4460 solver.cpp:237]     Train net output #0: rpn_cls_loss = 0.00557839 (* 1 = 0.00557839 loss)
I1113 23:35:57.824791  4460 solver.cpp:237]     Train net output #1: rpn_loss_bbox = 0.00260096 (* 1 = 0.00260096 loss)
I1113 23:35:57.824806  4460 sgd_solver.cpp:105] Iteration 560, lr = 0.001
I1113 23:35:58.670575  4460 solver.cpp:218] Iteration 580 (23.6486 iter/s, 0.845714s/20 iters), loss = 0.00420315
I1113 23:35:58.670637  4460 solver.cpp:237]     Train net output #0: rpn_cls_loss = 0.0020043 (* 1 = 0.0020043 loss)
I1113 23:35:58.670648  4460 solver.cpp:237]     Train net output #1: rpn_loss_bbox = 0.00219884 (* 1 = 0.00219884 loss)
I1113 23:35:58.670658  4460 sgd_solver.cpp:105] Iteration 580, lr = 0.001
I1114 00:34:17.348683  4460 sgd_solver.cpp:105] Iteration 79980, lr = 0.0001
speed: 0.044s / iter
Wrote snapshot to: /data1/caiyong.wang/program/py-faster-rcnn/output/faster_rcnn_alt_opt/voc_2007_trainval/zf_rpn_stage1_iter_80000.caffemodel

希望我們解析出

Iteration 500 (25.1734 iter/s, 0.79449s/20 iters), loss = 0.0230187

中的Iteration與loss值。 其實這是faster rcnn生成的log文件一部分。

我們通過上面的語法學習，在MTracer中生成了正則表達式：

\bIteration\s(?<Iteration>\d+)\s\(.*\).*loss\s=\s(?<loss>\d*\.*\d+)\b

注：.*表示除換行符以外的任意字符，*表示0個或多個
并且采用多行模式解析出了我們想要的結果。
上面的表達式中，我們使用了捕獲分組。
如下圖：

那么如何轉化成python代碼？
正確的代碼如下：

import repattern = re.compile(r'\bIteration\s(?P<Iteration>\d+)\s\(.*\).*loss\s=\s(?P<loss>\d*\.*\d+)\b')
arr=pattern.search("I1113 23:35:50.763059  4460 solver.cpp:218] Iteration 400 (27.3075 iter/s, 0.7324s/20 iters), loss = 0.0202583")
arr.groups()
arr.group()
arr.group("Iteration")
arr.group("loss")

結果為：

arr.groups()
Out[147]: ('400', '0.0202583')arr.group()
Out[148]: 'Iteration 400 (27.3075 iter/s, 0.7324s/20 iters), loss = 0.0202583'arr.group("Iteration")
Out[149]: '400'arr.group("loss")
Out[150]: '0.0202583'

這里python的命名組與以往的不一樣，使用的是 (?P<name>exp) 取代(?<name>exp)

而且compile里面必須加上r。

參考文獻：

http://blog.csdn.net/lwnylslwnyls/article/details/8901273
https://www.cnblogs.com/tk091/p/3702307.html
PYTHON的RE模塊理解（RE.COMPILE、RE.MATCH、RE.SEARCH）

三 python正則表達式的其他用法。

多行匹配：

python多行匹配

r = re.compile("需要的正則表達式", re.M)

匹配到需要的字符，可以獲取紅括號內的數字

r = re.compile("r([0-9]{5,})")

舉個例子：需要獲取20462和24729連個數字

import re
data = """r24062 line1hello word !!!!r24729 line2revision:24181"""r = re.compile("^r([0-9]{5,})", re.M)nums = r.findall(data)print nums---------------------
output:["24062", "24729"]

注：{5,}表示至少重復5次
2. 無捕獲分組
當你要將一部分規則作為一個整體對它進行某些操作，比如指定其重復次數時，你需要將這部分規則用