TK_Phoenix的博客

# 用第一个表的产品代码vlookup第二个表的长宽高重量import pandas as pdorder=pd.read_excel('D:/202006最终订单表格.xlsx') #读取第一个表product=pd.read_excel('D:/newestproduct.xlsx') #读取第二个表product0=product[['产品SKU','长','宽','高','重量']] #取第二个表想要的字段file1=pd.merge(order,product0,how='le

'''插入数据''' fh = open('自定义sku报表本月.xlsx', 'rb') data = pd.read_excel(fh, skiprows=[0, 1, 2, 3, 4]) data = data_deal(data) conn = create_engine('mysql+pymysql://root:1234@1xx.xx.0.135:3306/skykey?charset=utf8') ...

sku=sku.sort_values(by='数量',ascending=False,axis=0)

#打开我要保存的Excel文件writer = pd.ExcelWriter(r'D:/众结资料/amazon日报/0海运业绩/'+today+'/海运业绩202106公式.xlsx') writer_book = openpyxl.load_workbook(writer.path) # 加载指定的excel文件writer.book = writer_book#找到每个想要保存到的sheet名字索引 idx = writer_book.sheetnames.index('订单信息')

mx_stock= stock[stock['仓库'].str.contains(r'美西')]

import osimport datetimenow = datetime.datetime.now() # 现在的时间today = datetime.datetime.strftime(now, '%Y%m%d')paths = u'D:/众结资料/1日常工作内容/eBay日报数据/'+today+'/eBay易仓数据/'file_list = []def find_file_path(path, file_list): files = os.listdir(path)

import pandas as pdimport numpy as npdata = r'H:/资料/1.销售开发业绩信息总结/3、2019整月业绩修订版/2019eBay订单/'data1 = os.listdir(data)file=pd.DataFrame() #先定义for i in range(0,len(data1)): df=pd.read_csv((data +'/' + data1[i]),encoding='utf_8_sig') file=pd.concat

import pandas as pdimport osdata = r'H:/资料/1.销售开发业绩信息总结/3、2019整月业绩修订版/2019eBay订单/'data1 = os.listdir(data) # 文件夹下所有的文件 data1是一个文件名的listfor i in range(0,len(data1)): print('读取第' + str(i+1) + '个文件') file = pd.read_csv((data +'/' + data1[i]),en

Sub 复制费用()'' 复制费用 宏'''寻找广告费adverst = Dir("D:\众结资料\1日常工作内容\每日销售开发业绩(Python)\" & Application.WorksheetFunction.Text(Date, "yyyymmdd") & "\每日广告费.csv")If adverst = "" Then MsgBox "未找到广...

'找到平台销售模板，并修改模板名称platmc = Dir(ipath & "\plat业绩*.xlsx")platoldname = ipath & "\" & platmcplatnewname = ipath & "\plat业绩" & Format(Date, "yyyymmdd") & ".xlsx"If platnewname &...

import pandas as pdimport ospath='C:/Users/Administrator/Downloads/重复/嘿嘿/'list1=os.listdir(path)data1=pd.DataFrame()for i in list1: data=pd.read_csv(path+i, skiprows=[0, 1, 2, 3, 4]) #skip...