Python 如何为timeseries数据帧添加行？

我正在编写一个程序，将timeseries excel文件加载到数据框中，然后使用一些基本计算创建几个新列。我的程序有时会读取excel文件，其中一些记录缺少几个月。所以在下面的例子中，我有两个不同商店的月度销售数据。这些商店在不同的月份营业，因此它们的第一个月结束日期会有所不同。但在2020年9月30日之前，两家公司都应该有月末数据。在我的档案中，BBB商店没有2020年8月31日和2020年9月30日的记录，因为这两个月没有销售

商场 月初 陈述 城市 月底日期 销售额 AAA 5/31/2020 纽约 纽约 5/31/2020 1000 AAA 5/31/2020 纽约 纽约 6/30/2020 5000 AAA 5/31/2020 纽约 纽约 7/30/2020 3000 AAA 5/31/2020 纽约 纽约 8/31/2020 4000 AAA 5/31/2020 纽约 纽约 9/30/2020 2000 BBB 6/30/2020 计算机断层扫描 哈特福德 6/30/2020 100 BBB 6/30/2020 计算机断层扫描 哈特福德 7/30/2020 200

只需尝试日期时间索引的

upsample

。参考：

请注意：

7/30/2020

不是7月底<代码>2020年7月31日。因此，使用此方法将是一个问题（将月末日期转换为真正的月末日期）

---

下面是一步一步的方法。如果你有问题，请告诉我

import pandas as pd
pd.set_option('display.max_columns', None)
c = ['Store','Month Opened','State','City','Month End Date','Sales']
d = [['AAA','5/31/2020','NY','New York','5/31/2020',1000],
['AAA','5/31/2020','NY','New York','6/30/2020',5000],
['AAA','5/31/2020','NY','New York','7/30/2020',3000],
['AAA','5/31/2020','NY','New York','8/31/2020',4000],
['AAA','5/31/2020','NY','New York','9/30/2020',2000],
['BBB','6/30/2020','CT','Hartford','6/30/2020',100],
['BBB','6/30/2020','CT','Hartford','7/30/2020',200],
['CCC','3/31/2020','NJ','Cranbury','3/31/2020',1500]]

df = pd.DataFrame(d,columns = c)
df['Month Opened'] = pd.to_datetime(df['Month Opened'])
df['Month End Date'] = pd.to_datetime(df['Month End Date'])

#select last entry for each Store
df1 = df.sort_values('Month End Date').drop_duplicates('Store', keep='last').copy()

#delete all rows that have 2020-09-30. We want only ones that are less than 2020-09-30
df1 = df1[df1['Month End Date'] != '2020-09-30']

#set target end date to 2020-09-30
df1['Target_End_Date'] = pd.to_datetime ('2020-09-30')

#calculate how many rows to repeat
df1['repeats'] = df1['Target_End_Date'].dt.to_period('M').astype(int) - df1['Month End Date'].dt.to_period('M').astype(int)

#add 1 month to month end so we can start repeating from here
df1['Month End Date'] = df1['Month End Date'] + pd.DateOffset(months =1)

#set sales value as 0 per requirement
df1['Sales'] = 0

#repeat each row by the value in column repeats
df1 = df1.loc[df1.index.repeat(df1.repeats)].reset_index(drop=True)

#reset repeats to start from 0 thru n using groupby cumcouunt
#this will be used to calculate months to increment from month end date
df1['repeats'] = df1.groupby('Store').cumcount()

#update month end date based on value in repeats
df1['Month End Date'] = df1.apply(lambda x: x['Month End Date'] + pd.DateOffset(months = x['repeats']), axis=1)

#set end date to last day of the month
df1['Month End Date'] = pd.to_datetime(df1['Month End Date']) + pd.offsets.MonthEnd(0)

#drop columns that we don't need anymore. required before we concat dfs
df1.drop(columns=['Target_End_Date','repeats'],inplace=True)

#concat df and df1 to get the final dataframe
df = pd.concat([df, df1], ignore_index=True)

#sort values by Store and Month End Date
df = df.sort_values(by=['Store','Month End Date'],ignore_index=True)

print (df)

其输出为：

   Store Month Opened State      City Month End Date  Sales
0    AAA   2020-05-31    NY  New York     2020-05-31   1000
1    AAA   2020-05-31    NY  New York     2020-06-30   5000
2    AAA   2020-05-31    NY  New York     2020-07-30   3000
3    AAA   2020-05-31    NY  New York     2020-08-31   4000
4    AAA   2020-05-31    NY  New York     2020-09-30   2000
5    BBB   2020-06-30    CT  Hartford     2020-06-30    100
6    BBB   2020-06-30    CT  Hartford     2020-07-30    200
7    BBB   2020-06-30    CT  Hartford     2020-08-30      0
8    BBB   2020-06-30    CT  Hartford     2020-09-30      0
9    CCC   2020-03-31    NJ  Cranbury     2020-03-31   1500
10   CCC   2020-03-31    NJ  Cranbury     2020-04-30      0
11   CCC   2020-03-31    NJ  Cranbury     2020-05-31      0
12   CCC   2020-03-31    NJ  Cranbury     2020-06-30      0
13   CCC   2020-03-31    NJ  Cranbury     2020-07-31      0
14   CCC   2020-03-31    NJ  Cranbury     2020-08-31      0
15   CCC   2020-03-31    NJ  Cranbury     2020-09-30      0

注：我又添加了一个带有CCC的条目，以显示更多的变化

   Store Month Opened State      City Month End Date  Sales
0    AAA   2020-05-31    NY  New York     2020-05-31   1000
1    AAA   2020-05-31    NY  New York     2020-06-30   5000
2    AAA   2020-05-31    NY  New York     2020-07-30   3000
3    AAA   2020-05-31    NY  New York     2020-08-31   4000
4    AAA   2020-05-31    NY  New York     2020-09-30   2000
5    BBB   2020-06-30    CT  Hartford     2020-06-30    100
6    BBB   2020-06-30    CT  Hartford     2020-07-30    200
7    BBB   2020-06-30    CT  Hartford     2020-08-30      0
8    BBB   2020-06-30    CT  Hartford     2020-09-30      0
9    CCC   2020-03-31    NJ  Cranbury     2020-03-31   1500
10   CCC   2020-03-31    NJ  Cranbury     2020-04-30      0
11   CCC   2020-03-31    NJ  Cranbury     2020-05-31      0
12   CCC   2020-03-31    NJ  Cranbury     2020-06-30      0
13   CCC   2020-03-31    NJ  Cranbury     2020-07-31      0
14   CCC   2020-03-31    NJ  Cranbury     2020-08-31      0
15   CCC   2020-03-31    NJ  Cranbury     2020-09-30      0