外链点击数-Pandas-Python数据分析(9)

1.1. Pandas分析步骤

1. 载入数据
2. 将 外链点击数 进行 COUNT。类似如下SQL:

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

1.2. 代码

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 url_ref_stat(self):
        """统计外链点击情况"""
        group_by_cols = ['reference_url'] # 需要分组的列,只计算和显示该列
         
        # 直接统计次数
        url_ref_grp = self.df[group_by_cols].groupby(
                                     self.df['reference_url'])
        return url_ref_grp.agg(['count'])['reference_url'].sort_values(by='count', ascending=False)
 
 
def main():
    file_pathes = ['www.ttmark.com.access.log']
 
    pd_ng_log_stat = PDNgLogStat()
    pd_ng_log_stat.load_data(file_pathes)
 
    # 统计外链点击情况
    print pd_ng_log_stat.url_ref_stat()
 
if __name__ == '__main__':
    main()

运行统计和输出结果

python pd_ng_log_stat.py
 
                                          count
reference_url                                  
-                                        574546
www.ttmark.com                           331136
m.baidu.com                               32990
......
www.google.fr                               192
www.google.de                               147
www.google.it                               136
 
[231 rows x 1 columns]

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