["python","apache-spark","dataframe","pyspark","apache-spark-sql"]
Spark Dataframe distinguish columns with duplicated name

So as I know in Spark Dataframe, that for multiple columns can have the same name as shown in below dataframe snapshot: [ Row(a=107831, f=SparseVector(5, {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0}), a=107831, f=SparseVector(5, {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0})), Row(a=107831, f=SparseVector(5, {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0}), a=125231, f=SparseVector(5, {0: 0.0, 1: 0.0, 2: 0.0047, 3: 0.0, 4: 0.0043})), Row(a=107831, f=SparseVector(5, {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0}), a=145831, f=SparseVector(5, {0: 0.0, 1: 0.2356, 2: 0.0036, 3: 0.0, 4: 0.4132})), Row(a=107831, f=SparseVector(5, {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0}), a=147031, f=SparseVector(5, {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0})), Row(a=107831, f=SparseVector(5, {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0}), a=149231, f=SparseVector(5, {0: 0.0, 1: 0.0032, 2: 0.2451, 3: 0.0, 4: 0.0042})) ] Above result is created by join with a dataframe to itself, you can see there are 4 columns with both two a and f. The problem is is there when I try to do more calculation with the a column, I cant find a way to select the a, I have try df[0] and df.select('a'), both returned me below error mesaage: AnalysisException: Reference 'a' is ambiguous, could be: a#1333L, a#1335L. Is there anyway in Spark API that I can distinguish the columns from the duplicated names again? or maybe some way to let me change the column names?
I would recommend that you change the column names for your join. df1.select(col("a") as "df1_a", col("f") as "df1_f") .join(df2.select(col("a") as "df2_a", col("f") as "df2_f"), col("df1_a" === col("df2_a")) The resulting DataFrame will have schema (df1_a, df1_f, df2_a, df2_f)