python实现xlsx文件分析详解
python脚本实现xlsx文件解析，供大家参考，具体内容如下
环境配置：
1.系统环境：Windows 7 64bit
2.编译环境：Python3.4.3
3.依赖库： os sys xlrd re
4.其他工具：none
5.前置条件：待处理的xlsx文件
脚本由来
最近的工作是做测试，而有一项任务呢，就是分析每天机器人巡检时采集的数据，包括各种传感器，CO2、O2、噪声等等，每天的数据也有上千条，通过站控的导出数据功能，会把数据库里面导出成xlsx文件，而这项任务要分析一下当天采集的数据是否在正常范围，要计算摄像头的识别率和识别准确率，自己傻呵呵的每天都在手动操作，突然觉得很浪费时间，索性写个python脚本吧，这样每天一条命令，就能得到自己想看的数据结果。每天至少节省10分钟！
这是要解析的xlsx文件：
一般手动就得筛选、排序、打开计算器计算 - - 繁琐枯燥乏味
还是python大法好
代码浅析
流程图
脚本demo
#-*- coding:utf-8 -*-
import xlrd
import os
import sys
import logging
import re
#logging.basicConfig(level=logging.DEBUG)
xfile = sys.argv[1]
dateList = []
InspectionType = []
InspectionRresult = []
def load_data():
CO2Type = []
O2Type = []
NoiseType = []
SupwareType = []
TowareType = []
TemperatureType = []
HumidityType = []
InfraredType = []
CO2Result = []
O2Result = []
NoiseResult = []
SupwareResult = []
TowareResult = []
TemperatureResult = []
HumidityResult = []
InfraredResult = []
logging.debug(InspectionType)
logging.debug(InspectionRresult)
for index, value in enumerate(InspectionType):
if value == "二氧化碳": #CO2Type
CO2Type.extend(value)
logging.debug(index)
logging.debug("CO2 RESULT: "+InspectionRresult[index])
CO2Result.append(InspectionRresult[index])
if value == "氧气传感器": #O2Type
O2Type.extend(value)
O2Result.append(InspectionRresult[index])
if value == "噪声传感器": #NoiseType
NoiseType.extend(value)
NoiseResult.append(InspectionRresult[index])
if value == "局放(超声波测量)": #SupwareType
SupwareType.extend(value)
SupwareResult.append(InspectionRresult[index])
if value == "局放(地电波测量)": #SupwareType
TowareType.extend(value)
TowareResult.append(InspectionRresult[index])
if value == "温度传感器": #TemperatureType
TemperatureType.extend(value)
TemperatureResult.append(InspectionRresult[index])
if value == "湿度传感器": #TemperatureType
HumidityType.extend(value)
HumidityResult.append(InspectionRresult[index])
if value == "温度(红外测量)": #TemperatureType
InfraredType.extend(value)
InfraredResult.append(InspectionRresult[index])
logging.debug(CO2Result)
logging.debug(O2Result)
logging.debug(NoiseResult)
logging.debug(SupwareResult)
logging.debug(TowareResult)
logging.debug(TemperatureResult)
logging.debug(HumidityResult)
logging.debug(InfraredResult)
return CO2Result,O2Result,NoiseResult,SupwareResult,TowareResult,TemperatureResult,HumidityResult,InfraredResult
def get_data_print(co2,o2,noise,supware,toware,temperature,humidity,infrared):
co2 = list(map(eval,co2))
o2 = list(map(eval,o2))
noise = list(map(eval,noise))
supware = list(map(eval,supware))
toware = list(map(eval,toware))
temperature = list(map(eval,temperature))
humidity = list(map(eval,humidity))
infrared = list(map(eval,infrared))
co2Min = min(co2)
co2Max = max(co2)
logging.debug("CO2 min value :~~"+str(co2Min))
logging.debug("CO2 max value :~~"+str(co2Max))
o2Min = min(o2)
o2Max = max(o2)
noiseMin = min(noise)
noiseMax = max(noise)
supwareMin = min(supware)
supwareMax = max(supware)
towareMin = min(toware)
towareMax = max(toware)
temperatureMin = min(temperature)
temperatureMax = max(temperature)
humidityMin = min(humidity)
humidityMax = max(humidity)
infraredMin = min(infrared)
infraredMax = max(infrared)
print("CO2 values :",co2Min,'~~~~~~~',co2Max)
print("o2 values :",o2Min,'~~~~~~~',o2Max)
print("noise values :",noiseMin,'~~~~~~~',noiseMax)
print("supware values :",supwareMin,'~~~~~~~',supwareMax)
print("toware values :",towareMin,'~~~~~~~',towareMax)
print("temperature values :",temperatureMin,'~~~~~~~',temperatureMax)
print("humidity values :",humidityMin,'~~~~~~~',humidityMax)
print("infrared values :",infraredMin,'~~~~~~~',infraredMax)
def cal_picture():
result7to19List = []
result19to7List = []
count7to19List = []
count19to7List = []
count7to19Dict = {}
count19to7Dict = {}
failfind7to19cnt = 0
failfind19to7cnt = 0
photoType = []
photoDateList = []
allPhotoResult = []
for index,value in enumerate(InspectionType): #按照巡检类型筛选出视觉类，通过索引值同步时间、巡检结果
if value == "开关(视觉识别)" or value == "旋钮(视觉识别)" or \
value == "电流表(视觉识别)" or value == "电压表(视觉识别)":
photoType.extend(value)
photoDateList.append(dateList[index])
allPhotoResult.append(InspectionRresult[index])
for index,value in enumerate(photoDateList):
if value[-8:] > '07:00:00' and value[-8:] < '19:00:00':
result7to19List.append(allPhotoResult[index])
if value[-8:] > '19:00:00' or value[-8:] < '7:00:00':
result19to7List.append(allPhotoResult[index])
logging.debug(result7to19List[-20:])
logging.debug(result19to7List[:20])
noduplicate7to19Set=set(result7to19List) #里面无重复项
for item in noduplicate7to19Set:
count7to19List.append(result7to19List.count(item))
logging.debug(count7to19List)
count7to19Dict= dict(zip(list(noduplicate7to19Set),count7to19List))
noduplicate19to7Set=set(result19to7List)
for item in noduplicate19to7Set:
count19to7List.append(result19to7List.count(item))
count19to7Dict= dict(zip(list(noduplicate19to7Set),count19to7List))
logging.debug(count7to19Dict)
None7to19cnt = count7to19Dict['']
all7to19cnt = len(result7to19List)
None19to7cnt = count19to7Dict['']
all19to7cnt = len(result19to7List)
logging.debug(None7to19cnt)
for key in count7to19Dict:
if count7to19Dict[key] == 1 :
failfind7to19cnt = failfind7to19cnt+1
if re.match('识别失败:*',key):
failfind7to19cnt = failfind7to19cnt+ count7to19Dict[key]
for key in count19to7Dict:
if count19to7Dict[key] == 1 :
failfind19to7cnt = failfind19to7cnt+1
if re.match('识别失败:*',key):
failfind19to7cnt = failfind19to7cnt+count19to7Dict[key]
logging.debug(all19to7cnt)
print("7:00 ~~~ 19:00 识别率：",(all7to19cnt-None7to19cnt)/all7to19cnt)
print("7:00 ~~~ 19:00 识别准确率：",(all7to19cnt-None7to19cnt-failfind7to19cnt)/(all7to19cnt-None7to19cnt))
print("19:00 ~~~ 7:00 识别率：",(all19to7cnt-None19to7cnt)/all19to7cnt)
print("19:00 ~~~ 7:00 识别准确率：",(all19to7cnt-None19to7cnt-failfind19to7cnt)/(all19to7cnt-None19to7cnt))
#读取xlsx文件
xlsxdata=xlrd.open_workbook(xfile)
tablepage=xlsxdata.sheets()[0]
dateList.extend(tablepage.col_values(5))
InspectionType.extend(tablepage.col_values(3))
InspectionRresult.extend(tablepage.col_values(6))
cal_picture()
co2,o2,noise,supware,toware,temperature,humidity,infrared=load_data()
get_data_print(co2,o2,noise,supware,toware,temperature,humidity,infrared)
结果图
回顾与总结
渐渐体会到python脚本的优势所在。
python在代码保密上可能是解释性语言共有的小小缺陷，做项目还是C/C++，当然是指传统项目
写python很开心啊
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