1、excel 操作
ID Name InStore Date 0 NaN Book_001 NaN NaN 1 NaN Book_002 NaN NaN 2 NaN Book_003 NaN NaN 3 NaN Book_004 NaN NaN 4 NaN Book_005 NaN NaN 5 NaN Book_006 NaN NaN 6 NaN Book_007 NaN NaN 7 NaN Book_008 NaN NaN 8 NaN Book_009 NaN NaN import pandas as pd from datetime import date,timedelta def add_month(d,md): yd = md // 12 m = d.month + md % 12 if m != 12: yd += m // 12 m = m % 12 return date(d.year + yd,m,d.day) books = pd.read_excel(r'C:\Users\Lenovo\Desktop\excel\Books_data.xlsx',skiprows=3,usecols='C:F',index_col=None ,dtype={'ID':str,'InStore':str,'Date':str}) start = date(2019,1,1) for i in books.index: books['ID'].at[i] = i + 1 books['InStore'].at[i] = 'Yes'if i % 2 == 0 else 'N0' books['Date'].at[i] = start # books['Date'].at[i] = start + timedelta(days=i) # books['Date'].at[i] = date(start.year + i,start.month,start.day) books['Date'].at[i] = add_month(start,i) print(books) ID Name InStore Date 0 1 Book_001 Yes 2019-01-01 1 2 Book_002 N0 2019-02-01 2 3 Book_003 Yes 2019-03-01 3 4 Book_004 N0 2019-04-01 4 5 Book_005 Yes 2019-05-01 5 6 Book_006 N0 2019-06-01 6 7 Book_007 Yes 2019-07-01 7 8 Book_008 N0 2019-08-01 8 9 Book_009 Yes 2019-09-01 9 10 Book_010 N0 2019-10-01 10 11 Book_011 Yes 2019-11-01 11 12 Book_012 N0 2019-12-01 12 13 Book_013 Yes 2020-01-01 13 14 Book_014 N0 2020-02-01 14 15 Book_015 Yes 2020-03-01
2、时间日期处理小结（datetime模块）
https://www.cnblogs.com/tianyiliang/p/8270509.html