我有一个包含 1,001,623 行格式的日志文件:
[02/Jan/2012:09:07:32] "GET /click?id=162&prod=5475 HTTP/1.1" 200 4352
每一个都换行
我使用正则表达式对其进行循环并提取我需要的信息(日期、ID、产品)
for txt in logfile:
m = rg.search(txt)
if m:
l1=m.group(1)
l2=m.group(2)
l3=m.group(3)
dt=dt.append(pd.Series([l1]))
art=art.append(pd.Series([l2]))
usr=usr.append(pd.Series([l3]))
这在测试中效果很好,我只使用了一个小样本,但当我使用整个集合时,它已经运行了 12 个小时,没有显示任何进展。然后我将创建一个数据框来进行一些分析。有更好的方法吗?
编辑:
这就是我打开日志文件的方式。
logfile = open("data/access.log", "r")
正则表达式
re1='.*?' # Non-greedy match on filler
re2='((?:(?:[0-2]?\\d{1})|(?:[3][01]{1}))[-:\\/.](?:Jan(?:uary)?|Feb(?:ruary)?|Mar(?:ch)?|Apr(?:il)?|May|Jun(?:e)?|Jul(?:y)?|Aug(?:ust)?|Sep(?:tember)?|Sept|Oct(?:ober)?|Nov(?:ember)?|Dec(?:ember)?)[-:\\/.](?:(?:[1]{1}\\d{1}\\d{1}\\d{1})|(?:[2]{1}\\d{3})))(?![\\d])' # DDMMMYYYY 1
re3='.*?' # Non-greedy match on filler
re4='\\d+' # Uninteresting: int
re5='.*?' # Non-greedy match on filler
re6='\\d+' # Uninteresting: int
re7='.*?' # Non-greedy match on filler
re8='\\d+' # Uninteresting: int
re9='.*?' # Non-greedy match on filler
re10='(\\d+)' # Integer Number 1
re11='.*?' # Non-greedy match on filler
re12='(\\d+)' # Integer Number 2
rg = re.compile(re1+re2+re3+re4+re5+re6+re7+re8+re9+re10+re11+re12,re.IGNORECASE|re.DOTALL)
m = rg.search(txt)
最佳答案
您可以使用 pandas
。首先通过 strip
剥离 []
然后转换 to_datetime
.
然后解析id
和prod
最后通过concat
合并在一起:
import pandas as pd
import io
temp=u"""[02/Jan/2012:09:07:32] "GET /click?id=162&prod=5475 HTTP/1.1" 200 4352
[02/Jan/2012:09:07:32] "GET /click?id=162&prod=5475 HTTP/1.1" 200 4352
[02/Jan/2012:09:07:32] "GET /click?id=162&prod=5475 HTTP/1.1" 200 4352
[02/Jan/2012:09:07:32] "GET /click?id=162&prod=5475 HTTP/1.1" 200 4352
[02/Jan/2012:09:07:32] "GET /click?id=162&prod=5475 HTTP/1.1" 200 4352"""
#change io.StringIO(temp) to 'filename.csv'
df = pd.read_csv(io.StringIO(temp), sep="\s*", engine='python', header=None,
names=['date','get','data','http','no1','no2'])
#format - http://strftime.org/
df['date'] = pd.to_datetime(df['date'].str.strip('[]'), format="%d/%b/%Y:%H:%M:%S")
#split Dataframe
df1 = pd.DataFrame([ x.split('=') for x in df['data'].tolist() ], columns=['c','id','prod'])
#split Dataframe
df2 = pd.DataFrame([ x.split('&') for x in df1['id'].tolist() ], columns=['id', 'no3'])
print df
date get data http no1 no2
0 2012-01-02 09:07:32 "GET /click?id=162&prod=5475 HTTP/1.1" 200 4352
1 2012-01-02 09:07:32 "GET /click?id=162&prod=5475 HTTP/1.1" 200 4352
2 2012-01-02 09:07:32 "GET /click?id=162&prod=5475 HTTP/1.1" 200 4352
3 2012-01-02 09:07:32 "GET /click?id=162&prod=5475 HTTP/1.1" 200 4352
4 2012-01-02 09:07:32 "GET /click?id=162&prod=5475 HTTP/1.1" 200 4352
print df1
c id prod
0 /click?id 162&prod 5475
1 /click?id 162&prod 5475
2 /click?id 162&prod 5475
3 /click?id 162&prod 5475
4 /click?id 162&prod 5475
print df2
id no3
0 162 prod
1 162 prod
2 162 prod
3 162 prod
4 162 prod
df = pd.concat([df['date'], df1['prod'], df2['id']], axis=1)
print df
date prod id
0 2012-01-02 09:07:32 5475 162
1 2012-01-02 09:07:32 5475 162
2 2012-01-02 09:07:32 5475 162
3 2012-01-02 09:07:32 5475 162
4 2012-01-02 09:07:32 5475 162
关于python - 解析大日志文件,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/34707158/