【对比Python】日志处理1
任务：每三行记录一条日志，把日志整理成结构化文件
Python
| 1 | import pandas as pd |
| 2 | import numpy as np |
| 3 | log_file = 'E://txt//access_log.txt' |
| 4 | log_info = pd.read_csv(log_file,header=None) |
| 5 | log_g=log_info.groupby(log_info.index//3) |
| 6 | rec_list = [] |
| 7 | for i,g in log_g: |
| 8 | rec = g.values.reshape(1*3) |
| 9 | rec[1] = rec[1].split(":")[-1].replace("#","") |
| 10 | rec="\t".join(rec) |
| 11 | rec = np.array(rec.split("\t")) |
| 12 | rec = rec[[6,7,0,1,3,4,8,5]] |
| 13 | rec_list.append(rec) |
| 14 | rec_df = pd.DataFrame(rec_list,columns=["USERID","UNAME","IP","TIME","URL","BROWER","LOCATION","module"]) |
| 15 | print(rec_df) |
集算器
| A | ||
| 1 | E://txt//access_log.txt | |
| 2 | =file(A1).import@s() | |
| 3 | =A2.group((#-1)\3) | |
| 4 | =A3.(~.(_1).concat("\t").array("\t")) | |
| 5 | =A4.new(~(7):USERID,~(8):UNAME,~(1):IP,~(2):TIME,~(4):URL,~(5):BROWER,~(9):LOCATION,left(~(6).array("\:")(2),-1):module) |
有了按行号分组的机制，就可以用循环每次处理一组数据，简化难度。