根据列日期在数据框中添加每个月的行

2022-01-11 00:00:00 python pandas datetime calendar

问题描述

我正在处理需要推断不同月份的财务数据.这是我的数据框:

I am dealing with financial data which i need to extrapolate for different months. Here is my dataframe:

invoice_id,date_from,date_to
30492,2019-02-04,2019-09-18

我想将它分解为 date_from 和 date_to 之间的不同月份.因此，我需要为每个月添加行，从开始日期到结束日期.最终输出应如下所示:

I want to break this up for different months between date_from and date_to. Hence i need to add rows for each month with month starting date to ending date. Final output should look like:

invoice_id,date_from,date_to
30492,2019-02-04,2019-02-28
30492,2019-03-01,2019-03-31
30492,2019-04-01,2019-04-30
30492,2019-05-01,2019-05-31
30492,2019-06-01,2019-06-30
30492,2019-07-01,2019-07-31
30492,2019-08-01,2019-08-30
30492,2019-09-01,2019-09-18

还需要处理闰年的情况.pandas datetime 包中是否有任何本机方法可以用来实现所需的输出?

Need to take care of leap year scenario as well. Is there any native method already available in pandas datetime package which i can use to achieve the desired output ?

解决方案

使用:

print (df)
   invoice_id  date_from    date_to
0       30492 2019-02-04 2019-09-18
1       30493 2019-01-20 2019-03-10

#added months between date_from and date_to
df1 = pd.concat([pd.Series(r.invoice_id,pd.date_range(r.date_from, r.date_to, freq='MS')) 
                 for r in df.itertuples()]).reset_index()
df1.columns = ['date_from','invoice_id']

#added starts of months - sorting for correct positions
df2 = (pd.concat([df[['invoice_id','date_from']], df1], sort=False, ignore_index=True)
         .sort_values(['invoice_id','date_from'])
         .reset_index(drop=True))

#added MonthEnd and date_to  to last rows
mask = df2['invoice_id'].duplicated(keep='last')
s = df2['invoice_id'].map(df.set_index('invoice_id')['date_to'])
df2['date_to'] = np.where(mask, df2['date_from'] + pd.offsets.MonthEnd(), s)

print (df2)
    invoice_id  date_from    date_to
0        30492 2019-02-04 2019-02-28
1        30492 2019-03-01 2019-03-31
2        30492 2019-04-01 2019-04-30
3        30492 2019-05-01 2019-05-31
4        30492 2019-06-01 2019-06-30
5        30492 2019-07-01 2019-07-31
6        30492 2019-08-01 2019-08-31
7        30492 2019-09-01 2019-09-18
8        30493 2019-01-20 2019-01-31
9        30493 2019-02-01 2019-02-28
10       30493 2019-03-01 2019-03-10

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