python - Pandas DataFrame - Adding rows to df based on data in df -

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apologies not specified title. i've been, unsuccesfully far, trying come way add new 'rows' pandas dataframe based on contents of of columns. hope make clear example. data mock-up data suffices in painting bigger picture.

so, lets car dealer has, among others, following 7 customers. in dataframe can see customer-id, gender (because why not), , country live in. in addition, can see whether they've bought of 4 car brands (and type of car) or not (na) (all values in dataframe strings btw). example, customer 4 female russia, , has bought porsche 911 dealer.

        cust-id sex country audi ferrari porsche jaguar     0   cu1      f    fr     r8    ff      na     na     1   cu2      m        na    na      na     xf     2   cu3      m    uk     rs7   na      na     na     3   cu4      f    ru     na    na      911    na     4   cu5      m        na    na      918    ford     5   cu6      f        s6    na      na     f-type     6   cu7      m    uk     a8    na      macans xe

what i'd able create new rows cases customer has bought more 1 car, each row specifying 1 car, , other car brand columns saying 'na' in specific row. above example result in following dataframe.

            cust-id sex country audi ferrari porsche jaguar     0         cu1    f    fr     r8    na      na     na     1         cu1    f    fr     na    ff      na     na     2         cu2    m        na    na      na     xf     3         cu3    m    uk     rs7   na      na     na     4         cu4    f    ru     na    na      911    na     5         cu5    m        na    na      918    na     6         cu5    m        na    na      na     ford     7         cu6    f        s6    na      na     f-type     8         cu7    m    uk     a8    na      na     na     9         cu7    m    uk     na    na      macans na     10        cu7    m    uk     na    na      na     xe

this means original row 3 cars specified lead 3 new rows each specifying 1 of cars (with original row gone). cust-id, sex, , country values not change. first time using website ask question myself formatting not bad. appreciate help/guidance. python pandas dataframe

the way approach following:

  1. iterate on every car column , keep records have non-null values

    df_dict = {}  car in ['audi', 'ferrari', 'porsche' ,'jaguar']:        non_nulls = df[ df.apply(lambda x: not pd.isnull(x[car] ), axis=1)]      df_dict[car] = non_nulls[[cust-id,sex,country, car]]

  2. concatenate dataframes pd.concat, create nulls in right places

    final_df = pd.concat( df_dict.values() )

something along lines should work. did not test code though, use own judgement!


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