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Editor to share with you the example analysis of RE regular expressions in python3, I believe that most people do not know much about it, so share this article for your reference, I hope you can learn a lot after reading this article, let's go to know it!

1. Introduce regular module (Regular Expression)

To use RE in python3, re module must be introduced.

Import re # introduces regular expressions

two。 The main method used is match (), which matches from left to right

# pattern is the rule to be verified # str is the string result = re.match (pattern, str) to be verified # if result is not None, the group method extracts data from result

3. Regular expression

1 ️single character matching rule

Character function. Match any one character (except\ n) [] match the characters enumerated in [] match numbers, that is, 0-9\ D matches non-numeric characters, that is, match non-numeric characters\ s match blank characters, that is, space\ tab\ S matches non-white space characters,\ s invert\ w accompany word characters, amusic z, AME Z, 0-9, _\ W match non-word characters \ W reverse

2 ️rules for quantity

Character function * match the previous character 0 times or unlimited times, optional, more or less + match the previous character 1 times or indefinitely until once? Matching the previous character once or 0 times, either once or without {m} matching the previous character m times {m} matching the previous character at least m times {m ~ n} matching the previous character m to n times

Example 1: verify whether the mobile phone number conforms to the rules (regardless of boundary issues)

# first of all, know the rule of mobile phone number # 1. It's all the number two. The length is 11.3. The first place is 14. The second bit is a bit in 35678 pattern = "1 [35678]\ d {9}" phoneStr = "18230092223" result = re.match (pattern, phoneStr) result.group () # the execution result is as follows:

4. For the original string raw, let's take a look at the following example:

In the image above: after adding "r" before assigning "\ nabc" to str, the python interpreter automatically adds a "\" to the value "\ nabc" of str.

Enables str to keep the value of the original string "\ nabc" printed when it is printed.

Example 2: (application of original string in regular expression)

If there is no original self-pay r, we have to do the following: add double "\" to pattern to avoid reducing "\" in escape characters. It will be troublesome.

When we use the r original string, we do not have to consider the string transfer problem, and it is easier to solve the character matching problem.

5. Represent the boundary

Character function ^ match string beginning $match string end\ b match the boundary of a word\ B match non-word boundary

Example 3: boundary (making rules to match str= "ho ve r")

Import re # defines rules to match str= "ho ve r" # 1. Start with the letter # 2. There is an empty character # 3 in the middle. Ve defines the matching word boundary pattern = r "^\ w +\ s\ bve\ b\ sr" str = "ho ve r" result = re.match (pattern, str) result.group ()

6. Matching grouping

Character function | match any expression left or right (ab) use the characters in parentheses as a grouping\ num references the string matched by the grouping num (? P) group aliases (? P=name) refer to the string matched by the name group

Example 4: match the number between 0 and 100

Import re # matches the numbers between 0 and 100. first of all, regular matches start from left to 100. after analysis, 0-100 can be divided into three parts: # 1. 0 "0 $" # 2. 100 "100 $" # 3. 1-99 "[1-9]\ d {0Magin1} $" # so integrate the following pattern = r "0 $| 100$ | [1-9]\ d {0parentin 1} $" # Test data is 0Jing 3Mo 27100123 result = re.match (pattern) "27") result.group () # takes 0 into account on 1-99. The above pattern can also be abbreviated as: pattern=r "100 $| [1-9]?\ d {0jol 1} $" # Test results are shown below:

Example 5: match the grouping to get the content in the tags in the page

Import re# matches the grouping to get the content in the page tags. Crawlers will use str = "hello world!" pattern = r "(. *)" result = re.match (pattern, str) result.group () # execute as shown below

Example 6: group references to accurately obtain the contents of multiple tags

Import re # reference grouping to accurately get the content within multiple tags # "\ 1" is a reference to the first grouping, similarly. Str = "hello world!" pattern = r ". *" result = re.match (pattern, str) result.groups () # as shown below:

Example 7-2: alias for grouping

Import re # is grouped with the alias str = "hello world!" pattern = "(? P.*)" result = re.match (pattern, str) result.groups () # as shown below:

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