Date 熊猫：如何在偏移日期合并两个数据帧？

我想根据df2行是否在df1行之后的3-6个月的日期范围内合并两个数据帧df1和df2。例如：

df1（我有每家公司的季度数据）：

df2（对于每个公司，我都有可以在任何一天发生的活动日期）：

公司事件日期
0 012345 2005-07-28这实际上是一个罕见的问题，不同的解决方案的算法复杂性可能会显著不同。您可能想考虑一下1-LIN片段的细微之处。

算法上：


根据日期对较大的数据帧进行排序

对于较小数据框中的每个日期，使用模块查找较大数据框中的相关行


对于长度分别为m和n（m这是我的解决方案，与Ami Tavory建议的算法不同：

#find the date offsets to define date ranges
start_time = df1.DATADATE.apply(pd.offsets.MonthEnd(3))
end_time = df1.DATADATE.apply(pd.offsets.MonthEnd(6))

#make these extra columns
df1['start_time'] = start_time
df1['end_time'] = end_time

#find unique company names in both dfs
unique_companies_df1 = df1.company.unique()
unique_companies_df2 = df2.company.unique()

#sort df1 by company and DATADATE, so we can iterate in a sensible order
sorted_df1=df1.sort(['company','DATADATE']).reset_index(drop=True)

#define empty df to append data
df3 = pd.DataFrame()

#iterate through each company in df1, find 
#that company in sorted df2, then for each 
#DATADATE quarter of df1, bisect df2 in the 
#correct locations (i.e. start_time to end_time)

for cmpny in unique_companies_df1:

    if cmpny in unique_companies_df2: #if this company is in both dfs, take the relevant rows that are associated with this company 
        selected_df2 = df2[df2.company==cmpny].sort('EventDate').reset_index(drop=True)
        selected_df1 = sorted_df1[sorted_df1.company==cmpny].reset_index(drop=True)

        for quarter in xrange(len(selected_df1.DATADATE)): #iterate through each DATADATE quarter in df1
            lo=bisect.bisect_right(selected_df2.EventDate,selected_CS.start_time[quarter]) #bisect_right to ensure that we do not include dates before our date range
            hi=bisect.bisect_left(selected_IT.EventDate,selected_CS.end_time[quarter]) #bisect_left here to not include dates after our desired date range            
            df_right = selected_df2.loc[lo:hi].copy()  #grab all rows with EventDates that fall within our date range
            df_left = pd.DataFrame(selected_df1.loc[quarter]).transpose()

            if len(df_right)==0: # if no EventDates fall within range, create a row with cmpny in the 'company' column, and a NaT in the EventDate column to merge
                df_right.loc[0,'company']=cmpny

            temp = pd.merge(df_left,df_right,how='inner',on='company') #merge the df1 company quarter with all df2's rows that fell within date range
            df3=df3.append(temp)

我按照您提供的步骤实现了它，并在上面发布了我的代码。虽然在我的大数据集上花费了很长时间，但它是有效的。我原本希望能够将pandas groupby合并到df1中，并按['company'、'DATADATE']和groupby.apply（）对其进行分组。这是一个函数，可以获取df2中的相关行，其事件日期介于df1中每行的开始时间和结束时间之间（即DATADATE之后的3-6个月）。这很有趣。当我有时间的时候，我真的很乐意深入地看看你的答案。
    company EventDate
0   012345  2005-07-28 <-- won't get merged b/c not within date range
1   012345  2005-10-12
2   123456  2005-05-15
3   123456  2005-05-17
4   123456  2005-05-25
5   123456  2005-05-30
6   123456  2005-08-08
7   123456  2005-11-29
8   abcxyz  2005-12-31 <-- won't be merged because company not in df1

    company DATADATE    EventDate
0   012345  2005-06-30  2005-10-12
1   012345  2005-09-30  NaN   <-- nan because no EventDates fell in this range
2   012345  2005-12-31  NaN
3   012345  2006-03-31  NaN
4   123456  2005-01-31  2005-05-15
5   123456  2005-01-31  2005-05-17
5   123456  2005-01-31  2005-05-25
5   123456  2005-01-31  2005-05-30
6   123456  2005-03-31  2005-08-08
7   123456  2005-06-30  2005-11-19
8   123456  2005-09-30  NaN

#find the date offsets to define date ranges
start_time = df1.DATADATE.apply(pd.offsets.MonthEnd(3))
end_time = df1.DATADATE.apply(pd.offsets.MonthEnd(6))

#make these extra columns
df1['start_time'] = start_time
df1['end_time'] = end_time

#find unique company names in both dfs
unique_companies_df1 = df1.company.unique()
unique_companies_df2 = df2.company.unique()

#sort df1 by company and DATADATE, so we can iterate in a sensible order
sorted_df1=df1.sort(['company','DATADATE']).reset_index(drop=True)

#define empty df to append data
df3 = pd.DataFrame()

#iterate through each company in df1, find 
#that company in sorted df2, then for each 
#DATADATE quarter of df1, bisect df2 in the 
#correct locations (i.e. start_time to end_time)

for cmpny in unique_companies_df1:

    if cmpny in unique_companies_df2: #if this company is in both dfs, take the relevant rows that are associated with this company 
        selected_df2 = df2[df2.company==cmpny].sort('EventDate').reset_index(drop=True)
        selected_df1 = sorted_df1[sorted_df1.company==cmpny].reset_index(drop=True)

        for quarter in xrange(len(selected_df1.DATADATE)): #iterate through each DATADATE quarter in df1
            lo=bisect.bisect_right(selected_df2.EventDate,selected_CS.start_time[quarter]) #bisect_right to ensure that we do not include dates before our date range
            hi=bisect.bisect_left(selected_IT.EventDate,selected_CS.end_time[quarter]) #bisect_left here to not include dates after our desired date range            
            df_right = selected_df2.loc[lo:hi].copy()  #grab all rows with EventDates that fall within our date range
            df_left = pd.DataFrame(selected_df1.loc[quarter]).transpose()

            if len(df_right)==0: # if no EventDates fall within range, create a row with cmpny in the 'company' column, and a NaT in the EventDate column to merge
                df_right.loc[0,'company']=cmpny

            temp = pd.merge(df_left,df_right,how='inner',on='company') #merge the df1 company quarter with all df2's rows that fell within date range
            df3=df3.append(temp)