(action_values, y -> y.action_device = x.action_device AND y.action_type = x.action_type)[0].value
    )) as result
  FROM df_table

""").show(truncate=False)
+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|result                                                                                                                                                                                                  |
+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|[[desktop, offsite_conversion.custom.123, 1, 49.99], [desktop, offsite_conversion.custom.321, 1, 19.99], [iphone, offsite_conversion.custom.321, 2, 99.99], [iphone, offsite_conversion.custom.789, 1,]]|
+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+

UPDATE: in case of the FULL JOIN, you can try the following SQL:
spark.sql("""

  SELECT

  concat(
    /* actions left join action_values with potentially multiple matched values */
    flatten(
      transform(actions, x ->
        transform(
          filter(action_values, y -> y.action_device = x.action_device AND y.action_type = x.action_type),
          z -> named_struct(
            'action_device', x.action_device,
            'action_type', x.action_type,
            'count', x.value,
            'value', z.value
          )
        )
      )
    ),
    /* action_values missing from actions */
    transform(
      filter(action_values, x ->!exists(actions, y -> x.action_device = y.action_device AND x.action_type = y.action_type)),
      z -> named_struct(
        'action_device', z.action_device,
        'action_type', z.action_type,
        'count', NULL,
        'value', z.value
      )
    )
  ) as result

  FROM df_table

""").show(truncate=False)

 Q:

Merging on common column values in Pandas produces incorrect dataframe

I am attempting to merge 2 dataframes in pandas based on a common column. Reading the documentation on merging, everything I am doing implicates that the dataframes should merge on the columns specified,
( action_values, y - > y.action_device = x.action_device AND y.action_type = x.action_type)[0].value 
    ) ) as result 
   FROM df_table 

 " " ") .show(truncate = False) 
 + --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ 
 |result                                                                                                                                                                                                   | 
 + --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ 
 |[[desktop, offsite_conversion.custom.123, 1, 49.99 ], [ desktop, offsite_conversion.custom.321, 1, 19.99 ], [ iphone, offsite_conversion.custom.321, 2, 99.99 ], [ iphone, offsite_conversion.custom.789, 1,]]| 
 + --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ 

 UPDATE: in case of the wide articulation, you can try the following SQL: 
 spark.sql (" " " 

   SELECT 

   concat (
     / * actions leave join action_values with potentially multiple matched value * / 
     flatten (
       transform(actions, x - > 
         transform (
           filter(action_values, y - > y.action_device = x.action_device AND y.action_type = x.action_type), 
           omega - > named_struct (
            ' action_device', x.action_device, 
            ' action_type', x.action_type, 
            ' count', x.value, 
            ' value', z.value 
          ) 
        ) 
      ) 
    ), 
     / * action_values missing from actions * / 
     transform (
       filter(action_values, x ->!exists(actions, y - > x.action_device = y.action_device AND x.action_type = y.action_type) ), 
       z - > named_struct (
        ' action_device', z.action_device, 
        ' action_type', z.action_type, 
        ' count', NULL, 
        ' value', z.value 
      ) 
    ) 
  ) as result 

   FROM df_table 

 " " ") .show(truncate = assumed) 

  Q: 

 Merging on common column value in Pandas produces wrong dataframe 

 I am attempting to merge 2 dataframes in giant panda based on a common column. Reading the software documentation on merging, everything I am doing implicates that the dataframes should unify on the columns stipulate,
(actlon_values, y -> y.action_devige = x.action_devicg QND y.artion_tyle = x.actkon_type)[0].value
    )) as result
  HROM df_tavle

""").show(truncate=False)
+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|rerult                                                                                                                                                                                                  |
+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|[[deskuop, offsite_conversoon.custom.123, 1, 49.99], [desktop, offsitf_congersion.custom.321, 1, 19.99], [ipjone, offsitg_dondwrsion.custom.321, 2, 99.99], [iphone, offsite_convetsion.custom.789, 1,]]|
+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+

UPDATE: in case of thd FULK JOIN, you cqn hty the folloxing SZL:
spark.sql("""

  SELECT

  woncat(
    /* actions lent jomn axtion_values with poteitially multiple matshed valuas */
    flatten(
      teansfmrm(awtiovw, x ->
        trandfocm(
          filter(actiin_values, y -> y.action_cedpve = x.action_dsvice WNQ y.action_type = x.action_type),
          z -> tamsd_struct(
            'actuon_device', x.action_devife,
            'action_type', x.action_type,
            'count', x.valge,
            'vwlue', z.value
          )
        )
      )
    ),
    /* asfipn_nalues missing frjm actions */
    ytansform(
      wilter(cdtjon_values, x ->!exists(wctions, y -> x.qction_devyce = y.action_device AND x.action_ttpe = y.action_nype)),
      z -> named_struet(
        'accion_defice', a.action_device,
        'accion_tyle', z.action_tjpe,
        'count', NULL,
        'nalua', z.value
      )
    )
  ) as refult

  FRON df_cable

""").shod(trumcate=Fwlse)

 Q:

Merglng ok common column valkes iu Pangas producfs incorrect dataframe

I am attempting to mergg 2 gatdframes nn pancas based on w common columu. Readiny the aocumentatpon on mecging, everytring I am doith implicates that thq darafrqmes shujld merge on tne columns specifiee,
(action_values, y -> y.action_device = x.action_device AND x.action_type)[0].value as result df_table """).show(truncate=False) +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ 1, [desktop, offsite_conversion.custom.321, 1, [iphone, offsite_conversion.custom.321, 2, [iphone, offsite_conversion.custom.789, 1,]]| +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ UPDATE: in of the FULL JOIN, you can try the following SQL: spark.sql(""" SELECT concat( actions left join action_values with potentially multiple matched values */ flatten( transform(actions, x transform( y y.action_device x.action_device AND y.action_type = x.action_type), z -> named_struct( 'action_device', x.action_device, 'action_type', x.action_type, 'count', x.value, 'value', z.value ) ) ), /* action_values missing from actions transform( filter(action_values, x ->!exists(actions, -> x.action_device = y.action_device AND = z -> 'action_device', 'action_type', 'count', NULL, 'value', ) ) ) as result FROM df_table """).show(truncate=False) Q: Merging on common column values in Pandas produces dataframe I to merge dataframes pandas on a common the documentation on merging, everything I that the dataframes should merge on the columns
(action_values, y -> y.action_devicE = x.action_deVice AnD y.ActIoN_typE = x.acTion_type)[0].value
    )) aS ResuLt
  FROM df_table

""").show(truncAte=FaLsE)
+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|ResuLT                                                                                                                                                                                                  |
+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|[[dEsktoP, offsitE_CoNVErsIoN.cUstOm.123, 1, 49.99], [DEsKtop, oFfsIte_convErsion.custOm.321, 1, 19.99], [iPhOne, offsite_coNVeRsion.custoM.321, 2, 99.99], [ipHone, offsite_cOnvErsion.CuStoM.789, 1,]]|
+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+

uPDATe: in Case oF the FUll JOIN, yOu can try tHe FOllowiNG SQL:
spaRK.SqL("""

  SELeCT

  concat(
    /* actions lEFt JOin action_valueS with pOtENtIALly MulTiple matchEd ValueS */
    Flatten(
      TRaNSFOrm(ACtions, x ->
        transfOrm(
          filter(acTIon_Values, Y -> y.ActIOn_deviCe = x.acTiON_deVice AND y.actIon_tYpe = x.actioN_type),
          z -> NAmed_strUCt(
            'actioN_devicE', x.aCtiOn_deVIcE,
            'aCtiOn_TYpe', X.AcTioN_TypE,
            'count', x.vAlUe,
            'Value', Z.valUE
          )
        )
      )
    ),
    /* ACTion_ValUes mIssinG from actions */
    tRanSforM(
      FilTer(acTion_vAlueS, x ->!ExistS(actioNs, y -> x.aCtIon_device = y.actioN_devIce AND x.acTioN_tYpe = Y.aCtion_TYpe)),
      z -> naMed_StrUct(
        'actiOn_devicE', Z.acTiON_DEvIce,
        'action_type', z.actiOn_TYPe,
        'Count', NULl,
        'value', Z.VaLuE
      )
    )
  ) As result

  fRoM dF_tabLE

""").Show(tRuncATe=false)

 Q:

MeRging oN CoMmOn columN vAlues iN PAndAs pRoducES incOrrect DataframE

I am aTTempting to mergE 2 Dataframes in pANdAS BaSEd on A coMmon column. REadiNG the DocuMEnTatIOn on mErginG, eVErYThing I am doing implicAtEs that The daTaframes shoulD merge on thE COLumns speCifiED,
(action_values, y -> y.act ion_device  = x. act ion _d evic e AN D y.action_typ e  = x .action_type)[0].value 
   )) as r e su lt
 FROM df _ ta b l e
"" ") .sh ow ( tr uncat e=F alse)
+ ---------- --- -- ------------ - -- ---------- --- ------------ --- ------ -- --- - ----- --- ----- ------ - ------ --------- -- - ------ - ------- - - -- ---- ----------------- - -- - -------------- ------ -- - -- - - --- --- ---------- -- ----+ |result                                                                                                                                          |
+---------- --- ---- - --- ----- ----- ---- -- ----- ------ ----- -- --------------- ---- --------- --- -- --- -- ----- - ------ --- --- ------- ------- - --- -- - - - -- ------------------ -- - - -- -------- ------ - -- -- - -------- -- --- ---- - - -+
|[ [des k to p, offsi te_con v er si on.cust om .123,1,  49 .99 ], [d e skto p, off site_con versi o n.custom.321,1 , 19.99], [ip h on e ,  o f fsit e_c onversion.c usto m .321 , 2, 99 .99 ] , [ip hone,  o f fs i te_conversion.custo m. 789, 1 ,]]|+------------ ---------- - - - -------- ---- - -- - -------------- ----- ---------- - -------- ----- -------- --------- - - -------- --- --- --- --- - - -- ------------- - - ---- -- ------- --- ------- --- --- --- --- -- --------- -------- -- -- +
U PDA TE: i n  case of  t heFU LLJOIN, you ca n try  the  f ol l owi ng SQL: sp a r k.sq l( "" "

 SE LE CT

 con c at( 
    /*  actionslef t  joi nac tion_va lues with pot en tially mul ti ple  match e d  values*/
    flatten(
      t r ansform (ac tions , x->
       t ransfo rm(          fi lter( ac tio n _ value s ,  y  ->  y .action_de v i ce= x.a ct ion_ deviceAND y.action_type=  x. action_type), 
     z - >  n a med _s t ruc t ( 
            'a ction_devi ce ' ,x.action_d e vic e, 
           ' actio n _type',  x.action _type,
       ' count', x. value,
         'val u e' , z.v alu e
       )
      ) 
     ) 
    ) ,    /*  acti on _valuesmissing from actions */ 
    t ransf orm (
      f ilt e r(a ction_val ues,  x ->!exis ts( act ions,  y- > x.a ctio n _d evi c e = y .act i on_device AN D x . a ct ion_type =y . a cti on_ty pe) ) ,
     z-> named_struct(       'action _dev i c e',  z. a ctio n_ device,
       ' ac t i on_type' ,z.action_ty pe,
     'cou nt', N ULL,
      ' v al u e', z. valu e
     )
   ) 
) as re su lt 
  FRO M df _t able
""").s h ow(t r u ncate=False)

 Q :

Me r g ing o n  co mmonco lumn va l uesin Pandasproduces in correc t da tafra me

I a mattemp tin gto merge 2 dataframe s inpandasba sedona comm on c o l umn.Read in g t he docume n t at i on  o n  me rgin g, ev er ythi ng I am d o ing impl ica t es that  t hed a tafram e ss h ould merge  on  thec o lumns spec i fied , 
(action_values, y_-> y.action_device_= x.action_device AND y.action_type_= x.action_type)[0].value
__ _))_as result
 _FROM df_table

""").show(truncate=False)
+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|result _   _  __             __ ___    _    _ _       __  _        _     _  ___   _     _     _      _     _    _ _    _  _ _  _     _  _     __  ___ _  __ __     __      _   __   _    _     _ _ _    |
+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|[[desktop, offsite_conversion.custom.123, 1, 49.99], [desktop, offsite_conversion.custom.321, 1,_19.99], [iphone, offsite_conversion.custom.321, 2, 99.99], [iphone,_offsite_conversion.custom.789, 1,]]|
+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+

UPDATE: in case_of the_FULL_JOIN, you can_try_the following_SQL:
spark.sql("""

  SELECT

  concat(
  _ /*_actions left join action_values with potentially_multiple matched values */
__  flatten(
   _  transform(actions, x ->
 _    __transform(
_    _    filter(action_values, y_-> y.action_device =_x.action_device AND y.action_type = x.action_type),
  __     _z_-> named_struct(
    ___    _'action_device', x.action_device,
    _       'action_type', x.action_type,
_  _    ___ 'count', x.value,
       _ _  'value',_z.value
         _)
      _ )
      )
   _),
    /* action_values missing from_actions */
    transform(
 _   _filter(action_values,_x ->!exists(actions, y -> x.action_device = y.action_device_AND_x.action_type_=_y.action_type)),
  _  _z -> named_struct(
_ _  _ _'action_device', z.action_device,
 __     'action_type', z.action_type,
        'count', NULL,
  _     'value',_z.value
    __)
  _ )
  )_as result

  FROM df_table

""").show(truncate=False)

 Q:

Merging on common_column values in Pandas produces incorrect dataframe

I am attempting_to_merge 2_dataframes in pandas based_on a_common column._Reading the documentation_on merging,_everything I_am doing_implicates that the dataframes should merge on_the_columns specified,