按日期合并数据框

发布于 2025-01-11 13:32:09 字数 679 浏览 0 评论 0原文

我有以下 2 个 df：

lst=[['2021-01-01','A'],['2021-01-01','B'],['2021-02-01','A'],['2021-02-01','B'],['2021-03-01','A'],['2021-03-01','B']]
df1=pd.DataFrame(lst,columns=['Date','Pf'])

lst=[['2021-02-01','A','New']]
df22=pd.DataFrame(lst,columns=['Date','Pf','Status'])

我想将它们合并以获得以下 df：

lst=[['2021-01-01','A','NaN'],['2021-01-01','B','NaN'],['2021-02-01','A','New'],['2021-02-01','B','NaN'],['2021-03-01','A','New'],['2021-03-01','B','NaN']]
df3=pd.DataFrame(lst,columns=['Date','Pf','Status'])

在 2021 年 2 月 1 日期间，可以应用合并公式。但是，我希望通过更改等于或大于 2021-02-01 的日期，只要出现与 df2 中相同的 Pf 即可获得相同的“新”状态，

您知道我如何解决这个问题吗？感谢您的帮助

原文

I have got the below 2 df:

lst=[['2021-01-01','A'],['2021-01-01','B'],['2021-02-01','A'],['2021-02-01','B'],['2021-03-01','A'],['2021-03-01','B']]
df1=pd.DataFrame(lst,columns=['Date','Pf'])

lst=[['2021-02-01','A','New']]
df22=pd.DataFrame(lst,columns=['Date','Pf','Status'])

I would like to merge them in order to obtain the df below:

lst=[['2021-01-01','A','NaN'],['2021-01-01','B','NaN'],['2021-02-01','A','New'],['2021-02-01','B','NaN'],['2021-03-01','A','New'],['2021-03-01','B','NaN']]
df3=pd.DataFrame(lst,columns=['Date','Pf','Status'])

For the period 2021-02-01 one could apply the merge formula. However, I would like to get the same status "New" as soon the same Pf as in df2 appears by changing dates equal and bigger than 2021-02-01

Do you have any idea how I could solve this question?
Thank you for your help

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妄想挽回 2025-01-18 13:32:09

使用 merge_asof 默认 direction='backward'：

df1['Date'] = pd.to_datetime(df1['Date'])
df22['Date'] = pd.to_datetime(df22['Date'])

df = pd.merge_asof(df1, df22, on='Date', by='Pf')
print (df)
        Date Pf Status
0 2021-01-01  A    NaN
1 2021-01-01  B    NaN
2 2021-02-01  A    New
3 2021-02-01  B    NaN
4 2021-03-01  A    New
5 2021-03-01  B    NaN

Use merge_asof with default direction='backward':

df1['Date'] = pd.to_datetime(df1['Date'])
df22['Date'] = pd.to_datetime(df22['Date'])

df = pd.merge_asof(df1, df22, on='Date', by='Pf')
print (df)
        Date Pf Status
0 2021-01-01  A    NaN
1 2021-01-01  B    NaN
2 2021-02-01  A    New
3 2021-02-01  B    NaN
4 2021-03-01  A    New
5 2021-03-01  B    NaN

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