基于两个其他日期之间的日期合并两个数据框而没有公共列 [英] Merging two dataframes based on a date between two other dates without a common column

问题描述

我有两个数据框，需要根据日期值是否适合其他两个日期之间进行合并.基本上，我需要执行一个外部联接，其中B.event_date在A.start_date和A.end_date之间.似乎合并和联接总是假定有一个公共列，在这种情况下，我没有.

I have two dataframes that I need to merge based on whether or not a date value fits in between two other dates. Basically, I need to perform an outer join where B.event_date is between A.start_date and A.end_date. It seems that merge and join always assume a common column which in this case, I do not have.

    A                           B
    start_date  end_date        event_date  price
0   2017-03-27  2017-04-20  0   2017-01-20  100
1   2017-01-10  2017-02-01  1   2017-01-27  200

Result 
    start_date  end_date        event_date  price
0   2017-03-27  2017-04-20  
1   2017-01-10  2017-02-01      2017-01-20  100
2   2017-01-10  2017-02-01      2017-01-27  200

推荐答案

创建日期时间的数据和格式:

Create data and format to datetimes:

df_A = pd.DataFrame({'start_date':['2017-03-27','2017-01-10'],'end_date':['2017-04-20','2017-02-01']})
df_B = pd.DataFrame({'event_date':['2017-01-20','2017-01-27'],'price':[100,200]})

df_A['end_date'] = pd.to_datetime(df_A.end_date)
df_A['start_date'] = pd.to_datetime(df_A.start_date)
df_B['event_date'] = pd.to_datetime(df_B.event_date)

创建键以进行交叉联接:

Create keys to do a cross join:

df_A = df_A.assign(key=1)
df_B = df_B.assign(key=1)
df_merge = pd.merge(df_A, df_B, on='key').drop('key',axis=1)

过滤掉在开始日期和结束日期之间不符合事件日期标准的记录:

Filter out records that do not meet criteria of event dates between start and end dates:

df_merge = df_merge.query('event_date >= start_date and event_date <= end_date')

重新回到原始日期范围表和下拉键列

Join back to original date range table and drop key column

df_out = df_A.merge(df_merge, on=['start_date','end_date'], how='left').fillna('').drop('key', axis=1)

print(df_out)

输出:

              end_date           start_date           event_date price
0  2017-04-20 00:00:00  2017-03-27 00:00:00                           
1  2017-02-01 00:00:00  2017-01-10 00:00:00  2017-01-20 00:00:00   100
2  2017-02-01 00:00:00  2017-01-10 00:00:00  2017-01-27 00:00:00   200

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