python - Pandas : Merge two dataframe where the keys are different -


i have 2 pandas dataframe, 1 store values , stores weight key of value dataframe : [symbol, date, hour] , weight dataframe [symbol, date].

 in [8]: value_df = pd.dataframe({'symbol':['s1','s1','s1','s1','s2','s2','s3'],              'date' : [20150101,20150101, 20150101, 20150102,20150101,20150102,20150103],              'hour' : [8,9,10,8,8,8,8],              'value' : [10,10.1,10.2,11,100,101,300]})  in [9]: value_df out[9]:         date  hour symbol  value 0  20150101     8     s1   10.0 1  20150101     9     s1   10.1 2  20150101    10     s1   10.2 3  20150102     8     s1   11.0 4  20150101     8     s2  100.0 5  20150102     8     s2  101.0 6  20150103     8     s3  300.0  in [10]: weight_df = pd.dataframe({'symbol': ['s1','s1','s1','s2','s2','s2','s3','s3','s3'], 'date':[20150101,20150102,20150103] * 3,'weight': [0.8,0.9,1,1,1,1,0.5,0.5,0.5]})  in [11]: weight_df out[11]:         date symbol  weight 0  20150101     s1     0.8 1  20150102     s1     0.9 2  20150103     s1     1.0 3  20150101     s2     1.0 4  20150102     s2     1.0 5  20150103     s2     1.0 6  20150101     s3     0.5 7  20150102     s3     0.5 8  20150103     s3     0.5

i merge these 2 tables, , add weight column value_df should cartesian product. instance:

         date  hour symbol  value weight 0  20150101     8     s1   10.0 0.8 1  20150101     9     s1   10.1 0.8 2  20150101    10     s1   10.2 0.8  3  20150102     8     s1   11.0 0.9 4  20150101     8     s2  100.0 1.0 5  20150102     8     s2  101.0 1.0  6  20150103     8     s3  300.0 0.5

challenge here because of additional 'hour' column.

i'm not sure understand "challenge". simple merge gives desired output:

>>> pandas.merge(value_df, weight_df, on=['date', 'symbol'])        date  hour symbol  value  weight 0  20150101     8     s1   10.0     0.8 1  20150101     9     s1   10.1     0.8 2  20150101    10     s1   10.2     0.8 3  20150102     8     s1   11.0     0.9 4  20150101     8     s2  100.0     1.0 5  20150102     8     s2  101.0     1.0 6  20150103     8     s3  300.0     0.5

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