1. 准备慢日志的csv文件
import pandas as pd import matplotlib.pyplot as plt # 选取耗时大于7000的日志 # awk -F '耗时:' '{if (int(substr($2,0,length($2)-2)) >7000) print $0} ' debug.log.2022-11-02.log >7s.log # 用,作为分隔符,使日志文件变成csv格式。awk -F',' 'BEGIN{print "time" "," "log"}{print $1,",",$2,$3,$4}' 7s.log >timestamp_log07.csv log_quality = pd.read_csv("D:\\Users\\usage_pandas\\data\\timestamp_log24.csv")
2. 用matplotlib.pyplot可视化
from pylab import mpl # 设置显示中文字体 mpl.rcParams["font.sans-serif"] = ["SimHei"] #设定绘图的画布 ax = pd.DataFrame(df_time_count.values).plot(grid=True,figsize=(80,12),legend=False) ax.set_xlabel('time_5min') # X轴label ax.set_ylabel('慢日志数数') # Y轴Label ax.set_title('5min_interval_日志数') # 图题 #设定X轴月份显示格式 plt.xticks( range(len(df_time_count.index)), [x.strftime('%H.%M') for x in df_time_count.index], rotation=45) plt.show() # 绘图
标签:log,df,可视化,time,ax,日志,csv,panda From: https://www.cnblogs.com/hixiaowei/p/16875691.html