浏览工具排名-Pandas-Python数据分析(11)

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所属分类：python

1.1. Pandas分析步骤

载入数据
将浏览工具排名进行 COUNT。类似如下SQL:

SELECT browser,
    count(*)
FROM log
GROUP BY browser
ORDER BY count(*)
LIMIT 0, 100;

SELECT browser,

count(*)

FROM log

GROUP BY browser

ORDER BY count(*)

LIMIT 0, 100;

1.2. 代码

cat pd_ng_log_stat.py
#!/usr/bin/env <a href="http://www.ttlsa.com/python/" title="Python"target="_blank">python</a>
#-*- coding: utf-8 -*-
 
from ng_line_parser import NgLineParser
 
import pandas as pd
import socket
import struct
 
class PDNgLogStat(object):
 
    def __init__(self):
        self.ng_line_parser = NgLineParser()
 
    def _log_line_iter(self, pathes):
        """解析文件中的每一行并生成一个迭代器"""
        for path in pathes:
            with open(path, 'r') as f:
                for index, line in enumerate(f):
                    self.ng_line_parser.parse(line)
                    yield self.ng_line_parser.to_dict()
 
    def load_data(self, path):
        """通过给的文件路径加载数据生成 DataFrame"""
        self.df = pd.DataFrame(self._log_line_iter(path))
 
    def browser_stat(self):
        """统计不同浏览器访问次数"""
        group_by_cols = ['browser'] # 需要分组的列,只计算和显示该列
         
        # 直接统计次数
        url_req_grp = self.df[group_by_cols].groupby(
                                     self.df['browser'])
        return url_req_grp.agg(['count'])['browser'].nlargest(100, 'count')
 
def main():
    file_pathes = ['www.ttmark.com.access.log']
 
    pd_ng_log_stat = PDNgLogStat()
    pd_ng_log_stat.load_data(file_pathes)
 
    # 统计 统计不同浏览器访问次数
    print pd_ng_log_stat.browser_stat()
 
if __name__ == '__main__':
    main()

cat pd_ng_log_stat.py

#!/usr/bin/env python

#-*- coding: utf-8 -*-

from ng_line_parser import NgLineParser

import pandas as pd

import socket

import struct

class PDNgLogStat(object):

def __init__(self):

self.ng_line_parser = NgLineParser()

def _log_line_iter(self, pathes):

"""解析文件中的每一行并生成一个迭代器"""

for path in pathes:

with open(path, 'r') as f:

for index, line in enumerate(f):

self.ng_line_parser.parse(line)

yield self.ng_line_parser.to_dict()

def load_data(self, path):

"""通过给的文件路径加载数据生成 DataFrame"""

self.df = pd.DataFrame(self._log_line_iter(path))

def browser_stat(self):

"""统计不同浏览器访问次数"""

group_by_cols = ['browser'] # 需要分组的列,只计算和显示该列

# 直接统计次数

url_req_grp = self.df[group_by_cols].groupby(

self.df['browser'])

return url_req_grp.agg(['count'])['browser'].nlargest(100, 'count')

def main():

file_pathes = ['www.ttmark.com.access.log']

pd_ng_log_stat = PDNgLogStat()

pd_ng_log_stat.load_data(file_pathes)

# 统计统计不同浏览器访问次数

print pd_ng_log_stat.browser_stat()

if __name__ == '__main__':

main()

运行统计和输出结果

python pd_ng_log_stat.py
 
                                                     count
browser                                                   
Googlebot/2.1; +http://www.google.com/bot.html)     104533
Chrome/47.0.2526.106 larbin2.6.3@unspecified.mail   101013
bingbot/2.0; +http://www.bing.com/bingbot.htm)       57072
......
P1 4.1.2)                                              613
Safari/534.30 OppoBrowser/3.9.2                        610
9.3.2; zh_CN)                                          601
 
[100 rows x 1 columns]

python pd_ng_log_stat.py

count

browser

Googlebot/2.1; +http://www.google.com/bot.html) 104533

Chrome/47.0.2526.106 larbin2.6.3@unspecified.mail 101013

bingbot/2.0; +http://www.bing.com/bingbot.htm) 57072

......

P1 4.1.2) 613

Safari/534.30 OppoBrowser/3.9.2 610

9.3.2; zh_CN) 601

[100 rows x 1 columns]

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1.1. Pandas分析步骤

1.2. 代码

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