Join two data frames select all columns from one and some columns from the other

Question

Let s say I have a spark data frame df1  with several columns  among which the column id  and data frame df2 with two columns  id and other  Is there a way to replicate the following command sqlContext sql  quot SELECT df1    df2 other FROM df1 JOIN df2 ON df1 id   df2 id quot    by using only pyspark functions such as join    select   and the like  I have to implement this join in a function and I don t want to be forced to have sqlContext as a function parameter  Thanks

User · Answer

function to drop duplicate columns after joining   check it  def dropDupeDfCols df       newcols          dupcols       for i in range len df columns        if df columns i  not in newcols          newcols append df columns i       else          dupcols append i   df   df toDF   str i  for i in range len df columns     for dupcol in dupcols      df   df drop str dupcol    return df toDF  newcols

User · Answer

Asterisk     works with alias  Ex   from pyspark sql functions import    df1   df1 alias  df1   df2   df2 alias  df2    df1 join df2  df1 id    df2 id  select  df1

User · Answer

I got an error   a not found  using the suggested code   from pyspark sql functions import col df1 alias  a   join df2 alias  b   col  b id      col  a id    select  col  a   xx  for xx in a columns     col  b other1   col  b other2       I changed a columns to df1 columns and it worked out

User · Answer

Not sure if the most efficient way  but this worked for me   from pyspark sql functions import col  df1 alias  a   join df2 alias  b   col  b id      col  a id    select  col  a   xx  for xx in a columns     col  b other1   col  b other2       The trick is in    col  a   xx  for xx in a columns    all columns in a   col  b other1   col  b other2      some columns of b

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Here is the code snippet that does the inner join and select the columns from both dataframe and alias the same column to different column name  emp df    spark read csv  Employees csv   header  True   dept df   spark read csv  dept csv   header  True    emp dept df   emp df join dept df  DeptID   select emp df       dept df  Name   alias  DName    emp df show   dept df show   emp dept df show    Output  for  emp df show       --- --------- ------ ------    ID      Name Salary DeptID   --- --------- ------ ------     1      John  20000      1     2     Rohit  15000      2     3     Parth  14600      3     4   Rishabh  20500      1     5     Daisy  34000      2     6     Annie  23000      1     7  Sushmita  50000      3     8  Kaivalya  20000      1     9     Varun  70000      3    10 Shambhavi  21500      2    11   Johnson  25500      3    12      Riya  17000      2    13     Krish  17000      1    14  Akanksha  20000      2    15    Rutuja  21000      3   --- --------- ------ ------   Output  for  dept df show      ------ ----------   DeptID       Name   ------ ----------        1      Sales        2 Accounting        3  Marketing   ------ ----------   Join Output   --- --------- ------ ------ ----------    ID      Name Salary DeptID      DName   --- --------- ------ ------ ----------     1      John  20000      1      Sales     2     Rohit  15000      2 Accounting     3     Parth  14600      3  Marketing     4   Rishabh  20500      1      Sales     5     Daisy  34000      2 Accounting     6     Annie  23000      1      Sales     7  Sushmita  50000      3  Marketing     8  Kaivalya  20000      1      Sales     9     Varun  70000      3  Marketing    10 Shambhavi  21500      2 Accounting    11   Johnson  25500      3  Marketing    12      Riya  17000      2 Accounting    13     Krish  17000      1      Sales    14  Akanksha  20000      2 Accounting    15    Rutuja  21000      3  Marketing   --- --------- ------ ------ ----------

User · Answer

Here is a solution that does not require a SQL context  but maintains the metadata of a DataFrame   a   sc parallelize    a    foo      b    hem      c    haw     toDF   a id    extra    b   sc parallelize    p1    a      p2    b      p3    c     toDF   other    b id     c   a join b  a a id    b b id    Then  c show   yields    ---- ----- ----- ----   a id extra other b id   ---- ----- ----- ----      a   foo    p1    a      b   hem    p2    b      c   haw    p3    c   ---- ----- ----- ----

User · Answer

I believe that this would be the easiest and most intuitive way   final    df1 alias  df1   join df2 alias  df2                                   on   df1  id      df2  id                                   how    inner                             select  df1                                       df2 other

User · Answer

drop duplicate b id  c   a join b  a a id    b b id  drop b b id

User · Answer

Without using alias   df1 join df2  df1 id    df2 id  select df1      df2  other

User · Answer

You could just make the join and after that select the wanted columns https   spark apache org docs latest api python pyspark sql html highlight dataframe 20join pyspark sql DataFrame join

[pyspark] Join two data frames, select all columns from one and some columns from the other

function to drop duplicate columns after joining.

check it

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