Category : merge

I have 2 dataframes: df1 and df2. I would like to merge the 2 dataframes on the column link in df2. Link column in df2 contains a list of column and values which match in df1: df1 = pd.DataFrame({‘p’:[1,2,3,4], ‘a’:[1,2,2,2],’b’:[‘z’,’z’,’z’,’z’],’c’:[3,3,4,4],’d’:[5,5,5,6]}) df2 = pd.DataFrame({‘e’:[11,22,33,44], ‘link’:[‘a=1,c=3′,’a=2,c=3′,’a=2,c=4,d=5′,’a=2,c=4’]}) The result should end with dataframe like this where column e ..

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Here is my dataframe following the merging of two separate dataframes. In the [order] column, orders 2, 4 and 5, have multiple corresponding amounts in the [value] column. This is replicated elsewhere in the dataframe at random points. What can I do to merge these amounts so that I am left with one row in ..

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I have two tables: reference_id reference_date price 112 2012-01-01 14.35 205 2012-03-02 10.02 325 2012-04-02 5.40 14 2012-06-20 7.68 ticket-reference_id quantity 112 2 205 4 325 7 14 5 I need to join the values of the reference_id and ticket_reference_id preserving the column names. So that, I need to have the next result: reference_id reference_date ..

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I want to merge two dataframes, with completely different indexes and mostly same columns without losing any information in the result. For example, my dataframes have sizes of (32564, 38855) and (32319, 37879). The indexes are completely different, but 37000 of the columns are shared. I need to get a result dataframe with (64883, 39734) ..

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I want to merge the following 2 data frames in Pandas but the result isn’t containing all the relevant columns: L1aIn[0:5] Filename OrbitNumber OrbitMode OrbitModeCounter Year Month Day L1aIn 0 oco2_L1aInDP_35863a_210329_B10206_210330111927.h5 35863 DP a 2021 3 29 1 1 oco2_L1aInDP_35862a_210329_B10206_210330111935.h5 35862 DP a 2021 3 29 1 2 oco2_L1aInDP_35861b_210329_B10206_210330111934.h5 35861 DP b 2021 3 29 ..

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I don’t know if this could be consider a join/merge/concatenate or something else but this is what is happening: I have two pandas dataframes (df1, df2) and I am trying to put them together so I could have something similar to the result below: df1 = pd.DataFrame({‘index’: [0, 1], ‘column A’: [‘item_a’, ‘item_b’]}) index column ..

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