利用 Python 插件 xlwings 读写 Excel

Python 通过 xlwings 读取 Excel 数据

去年底公司让我做设备管理，多次委婉拒绝，最终还是做了。其实我比较喜欢技术。做管理后发现现场没有停机率统计，而原始数据有，每次要自己在Excel中填写数据，感觉特麻烦了，就写了这个自动化操作Excel的程序，以前要半天的工作量，用Python不到10s。程序比较简单，直接上程序。

# -*- coding: utf-8 -*-

import xlwings as xw
import pandas as pd
from datetime import datetime

# 统计时间, 只有时间要改
START_TIME = '2018-07-01 00:00:00'
END_TIME = '2018-07-31 23:59:00'

START_ROW = 2 # 处理Excel文件开始行
END_ROW = 200 # 处理Excel结束行

# 天数 * 每天工作时间 * 分钟
WORK_TIME = 30 * 22 * 60

# 关键设备清单
key_machine = ['609', '610', '621', '622', '623', '624',
               '627', '628', '636', '638', '667', '670', '675', '689']
persons = ['张三', '李四', '王五']

app = xw.App(visible=True, add_book=False)
wb_source = app.books.open('downTimeData.xls') # 打开Excel文件 downTimeData.xls
sheet = wb_source.sheets[0]  # 选择第0个表单

# 需每月修改时间
start_datetime = datetime.strptime(START_TIME, '%Y-%m-%d %H:%M:%S') # 把开始统计时间转换为DateTime
end_datetime = datetime.strptime(END_TIME, '%Y-%m-%d %H:%M:%S') # 把结束统计时间转换为DateTime

result = []

for row in range(START_ROW, END_ROW):
    row_content = []
    row_str = str(row)

    time_str = sheet.range('C' + row_str).value.strip()
    create_datetime = datetime.strptime(time_str, '%Y-%m-%d %H:%M:%S')
    if start_datetime <= create_datetime <= end_datetime:

        machine = sheet.range('A' + row_str).value
        machine_number = machine[-4:-1]
        if machine_number in key_machine:

            if sheet.range('G' + row_str).value.strip() in persons:
                row_content.append(machine_number)

                row_content.append(create_datetime)

                response_time_str = sheet.range('D' + row_str).value
                complete_time_str = sheet.range('E' + row_str).value
                row_content.append(response_time_str + complete_time_str)

                solution_str = sheet.range('H' + row_str).value.strip()
                row_content.append(solution_str)

                comments = sheet.range('I' + row_str).value.strip()
                row_content.append(comments)

                result.append(row_content)

# count the times and downtime on the same machine and put it in dictionary
# 统计每台设备的停机次数
dict_result = {}
for _, [name, _, downtime, _, _] in enumerate(result):
    if name in dict_result:
        dict_result[name] = (dict_result[name][0] + 1,
                             dict_result[name][1] + downtime)
    else:
        dict_result[name] = (1, downtime)

# fill the result and write it on excel
target_name = START_TIME[5:7]
wb_target = app.books.open('analysis2018.xlsx')  # 打开Excel文件，把结果写入

index = 3
for key in key_machine:
    if key not in dict_result:
        wb_target.sheets[target_name].range('B' + str(index)).value = 0
        wb_target.sheets[target_name].range('C' + str(index)).value = 0
        wb_target.sheets[target_name].range('D' + str(index)).value = WORK_TIME
        wb_target.sheets[target_name].range('E' + str(index)).value = 0
        wb_target.sheets[target_name].range('F' + str(index)).value = 0
    else:
        wb_target.sheets[target_name].range(
            'B' + str(index)).value = dict_result[key][0]
        wb_target.sheets[target_name].range(
            'C' + str(index)).value = dict_result[key][1]
        wb_target.sheets[target_name].range(
            'D' + str(index)).value = (WORK_TIME - dict_result[key][1]) / dict_result[key][0]
        wb_target.sheets[target_name].range(
            'E' + str(index)).value = dict_result[key][1] / dict_result[key][0]
        wb_target.sheets[target_name].range(
            'F' + str(index)).value = dict_result[key][1] / WORK_TIME
    index += 1

# write the comment and solution on excel
result.sort()  # 故障信息排序，用于最后输出
df = pd.DataFrame(result, columns=['编号',
                                   '故障时间',
                                   '停机时间', '解决方案', '备注'])

wb_target.sheets[target_name].range('H2').value = df
wb_target.sheets[target_name].autofit('c')