CDN_IP对应地址-Pandas-Python数据分析(18)

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所属分类：python

1.1. Pandas分析步骤

载入日志数据
载入area_ip数据
将 CDN_IP请求数进行 COUNT。类似如下SQL:

SELECT inet_aton(l.cdn_ip),
    count(*),
    a.addr
FROM log AS l
INNER JOIN area_ip AS a
    ON a.start_ip_num <= inet_aton(l.cdn_ip)
    AND a.end_ip_num >= inet_aton(l.cdn_ip)
GROUP BY cdn_ip
ORDER BY count(*)
LIMIT 0, 100;

SELECT inet_aton(l.cdn_ip),

count(*),

a.addr

FROM log AS l

INNER JOIN area_ip AS a

ON a.start_ip_num <= inet_aton(l.cdn_ip)

AND a.end_ip_num >= inet_aton(l.cdn_ip)

GROUP BY cdn_ip

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 _ip2num(self, ip):
        """用于IP转化为数字"""
        ip_num = -1
        try:
            # 将IP转化成INT/LONG 数字
            ip_num = socket.ntohl(struct.unpack("I",socket.inet_aton(str(ip)))[0])
        except:
            pass
        finally:
           return ip_num
 
    def _get_addr_by_ip(self, ip):
        """通过给的IP获得地址"""
        ip_num = self._ip2num(ip)
 
        try:
            addr_df = self.ip_addr_df[(self.ip_addr_df.ip_start_num <= ip_num) & 
                                      (ip_num <= self.ip_addr_df.ip_end_num)]
            addr = addr_df.at[addr_df.index.tolist()[0], 'addr']
            return addr
        except:
            return None
                     
    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 uv_cdn_ip(self, top = 100):
        """统计cdn ip量"""
        group_by_cols = ['cdn_ip'] # 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['cdn_ip'])
        return url_req_grp.agg(['count'])['cdn_ip'].nlargest(top, 'count')
         
    def uv_cdn_ip_addr(self, top = 100):
        """统计cdn ip量 地址"""
        cnt_df = self.uv_cdn_ip(top)
         
        # 添加 ip 地址 列
        cnt_df.insert(len(cnt_df.columns),
                      'addr',
                      cnt_df.index.map(self._get_addr_by_ip))
         
    def load_ip_addr(self, path):
        """加载IP"""
        cols = ['id', 'ip_start_num', 'ip_end_num',
                'ip_start', 'ip_end', 'addr', 'operator']
        self.ip_addr_df = pd.read_csv(path, sep='\t', names=cols, index_col='id')
        return self.ip_addr_df
 
def main():
    file_pathes = ['www.ttmark.com.access.log']
 
    pd_ng_log_stat = PDNgLogStat()
    pd_ng_log_stat.load_data(file_pathes)
 
    # 加载 ip 地址
    area_ip_path = 'area_ip.csv'
    pd_ng_log_stat.load_ip_addr(area_ip_path)
 
    # 统计 CDN IP 访问量 和 地址
    print pd_ng_log_stat.uv_cdn_ip_addr()
 
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 _ip2num(self, ip):

"""用于IP转化为数字"""

ip_num = -1

try:

# 将IP转化成INT/LONG 数字

ip_num = socket.ntohl(struct.unpack("I",socket.inet_aton(str(ip)))[0])

except:

pass

finally:

return ip_num

def _get_addr_by_ip(self, ip):

"""通过给的IP获得地址"""

ip_num = self._ip2num(ip)

try:

addr_df = self.ip_addr_df[(self.ip_addr_df.ip_start_num <= ip_num) &

(ip_num <= self.ip_addr_df.ip_end_num)]

addr = addr_df.at[addr_df.index.tolist()[0], 'addr']

return addr

except:

return None

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 uv_cdn_ip(self, top = 100):

"""统计cdn ip量"""

group_by_cols = ['cdn_ip'] # 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['cdn_ip'])

return url_req_grp.agg(['count'])['cdn_ip'].nlargest(top, 'count')

def uv_cdn_ip_addr(self, top = 100):

"""统计cdn ip量地址"""

cnt_df = self.uv_cdn_ip(top)

# 添加 ip 地址列

cnt_df.insert(len(cnt_df.columns),

'addr',

cnt_df.index.map(self._get_addr_by_ip))

def load_ip_addr(self, path):

"""加载IP"""

cols = ['id', 'ip_start_num', 'ip_end_num',

'ip_start', 'ip_end', 'addr', 'operator']

self.ip_addr_df = pd.read_csv(path, sep='\t', names=cols, index_col='id')

return self.ip_addr_df

def main():

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

pd_ng_log_stat = PDNgLogStat()

pd_ng_log_stat.load_data(file_pathes)

# 加载 ip 地址

area_ip_path = 'area_ip.csv'

pd_ng_log_stat.load_ip_addr(area_ip_path)

# 统计 CDN IP 访问量和地址

print pd_ng_log_stat.uv_cdn_ip_addr()

if __name__ == '__main__':

main()

运行统计和输出结果

python pd_ng_log_stat.py
 
                 count    addr
cdn_ip                        
140.205.127.2    31943  浙江省杭州市
101.200.101.203  26306    None
101.200.101.214  24667    None
......
140.205.253.155   4065  浙江省杭州市
140.205.253.174   4048  浙江省杭州市
140.205.253.131   3972  浙江省杭州市
 
[100 rows x 2 columns]

python pd_ng_log_stat.py

count addr

cdn_ip

140.205.127.2 31943 浙江省杭州市

101.200.101.203 26306 None

101.200.101.214 24667 None

......

140.205.253.155 4065 浙江省杭州市

140.205.253.174 4048 浙江省杭州市

140.205.253.131 3972 浙江省杭州市

[100 rows x 2 columns]

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1.1. Pandas分析步骤

1.2. 代码

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