Removing duplicate columns after a DF join in Spark

Question

When you join two DFs with similar column names:

df = df1.join(df2, df1[\'id\'] == df2[\'id\'])

Join works fine but you can\'t call the

Answer 1

After I've joined multiple tables together, I run them through a simple function to drop columns in the DF if it encounters duplicates while walking from left to right. Alternatively, you could rename these columns too.

Where Names is a table with columns ['Id', 'Name', 'DateId', 'Description'] and Dates is a table with columns ['Id', 'Date', 'Description'], the columns Id and Description will be duplicated after being joined.

Names = sparkSession.sql("SELECT * FROM Names")
Dates = sparkSession.sql("SELECT * FROM Dates")
NamesAndDates = Names.join(Dates, Names.DateId == Dates.Id, "inner")
NamesAndDates = dropDupeDfCols(NamesAndDates)
NamesAndDates.saveAsTable("...", format="parquet", mode="overwrite", path="...")

Where dropDupeDfCols is defined as:

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)

The resulting data frame will contain columns ['Id', 'Name', 'DateId', 'Description', 'Date'].