What is the difference between re.search and re.match?


What is the difference between the search() and match() functions in the Python re module?

I"ve read the documentation (current documentation), but I never seem to remember it. I keep having to look it up and re-learn it. I"m hoping that someone will answer it clearly with examples so that (perhaps) it will stick in my head. Or at least I"ll have a better place to return with my question and it will take less time to re-learn it.

Answer rating: 573

re.match is anchored at the beginning of the string. That has nothing to do with newlines, so it is not the same as using ^ in the pattern.

As the re.match documentation says:

If zero or more characters at the beginning of string match the regular expression pattern, return a corresponding MatchObject instance. Return None if the string does not match the pattern; note that this is different from a zero-length match.

Note: If you want to locate a match anywhere in string, use search() instead.

re.search searches the entire string, as the documentation says:

Scan through string looking for a location where the regular expression pattern produces a match, and return a corresponding MatchObject instance. Return None if no position in the string matches the pattern; note that this is different from finding a zero-length match at some point in the string.

So if you need to match at the beginning of the string, or to match the entire string use match. It is faster. Otherwise use search.

The documentation has a specific section for match vs. search that also covers multiline strings:

Python offers two different primitive operations based on regular expressions: match checks for a match only at the beginning of the string, while search checks for a match anywhere in the string (this is what Perl does by default).

Note that match may differ from search even when using a regular expression beginning with "^": "^" matches only at the start of the string, or in MULTILINE mode also immediately following a newline. The “match” operation succeeds only if the pattern matches at the start of the string regardless of mode, or at the starting position given by the optional pos argument regardless of whether a newline precedes it.

Now, enough talk. Time to see some example code:

# example code:
string_with_newlines = """something

import re

print re.match("some", string_with_newlines) # matches
print re.match("someother", 
               string_with_newlines) # won"t match
print re.match("^someother", string_with_newlines, 
               re.MULTILINE) # also won"t match
print re.search("someother", 
                string_with_newlines) # finds something
print re.search("^someother", string_with_newlines, 
                re.MULTILINE) # also finds something

m = re.compile("thing$", re.MULTILINE)

print m.match(string_with_newlines) # no match
print m.match(string_with_newlines, pos=4) # matches
print m.search(string_with_newlines, 
               re.MULTILINE) # also matches

Answer rating: 60

match is much faster than search, so instead of doing regex.search("word") you can do regex.match((.*?)word(.*?)) and gain tons of performance if you are working with millions of samples.

This comment from @ivan_bilan under the accepted answer above got me thinking if such hack is actually speeding anything up, so let"s find out how many tons of performance you will really gain.

I prepared the following test suite:

import random
import re
import string
import time

LIST_SIZE = 1000000

def generate_word():
    word = [random.choice(string.ascii_lowercase) for _ in range(LENGTH)]
    word = "".join(word)
    return word

wordlist = [generate_word() for _ in range(LIST_SIZE)]

start = time.time()
[re.search("python", word) for word in wordlist]
print("search:", time.time() - start)

start = time.time()
[re.match("(.*?)python(.*?)", word) for word in wordlist]
print("match:", time.time() - start)

I made 10 measurements (1M, 2M, ..., 10M words) which gave me the following plot:

match vs. search regex speedtest line plot

The resulting lines are surprisingly (actually not that surprisingly) straight. And the search function is (slightly) faster given this specific pattern combination. The moral of this test: Avoid overoptimizing your code.

Get Solution for free from DataCamp guru