How to concatenate/append multiple Spark dataframes column wise in Pyspark?

user_3424990 0
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How to do pandas equivalent of pd.concat([df1,df2],axis='columns') using Pyspark dataframes? I googled and couldn't find a good solution.

DF1
var1        
     3      
     4      
     5      

DF2
var2    var3     
  23      31
  44      45
  52      53

Expected output dataframe
var1        var2    var3
     3        23      31
     4        44      45
     5        52      53

Edited to include expected output

pyspark apache-spark-sql apache-spark python
4 Answers

Equivalent of accepted answer using pyspark would be

from pyspark.sql.types import StructType

spark = SparkSession.builder().master("local").getOrCreate()
df1 = spark.sparkContext.parallelize([(1, "a"),(2, "b"),(3, "c")]).toDF(["id", "name"])
df2 = spark.sparkContext.parallelize([(7, "x"),(8, "y"),(9, "z")]).toDF(["age", "address"])

schema = StructType(df1.schema.fields + df2.schema.fields)
df1df2 = df1.rdd.zip(df2.rdd).map(lambda x: x[0]+x[1])
spark.createDataFrame(df1df2, schema).show()
user_5263650

I have spent hours to do this with PySpark and a working solution of mine is as follows; (quite in Python equivalent of @Shankar Koirala ' s answer by the way)

from pyspark.sql.functions import monotonically_increasing_id

DF1 = df2.withColumn("row_id", monotonically_increasing_id())
DF2 = df3.withColumn("row_id", monotonically_increasing_id())
result_df = DF1.join(DF2, ("row_id")).drop("row_id")

You are simply defining a common column for both of the dataframes and dropping that column right after merge. I hope this solution helps in cases like that dataframes do not include any common columns.

However, this method joins dataframes rows randomly, a detail to keep in mind.

user_111056630

Here What I did to merge 2 Dataframes column-wise in Pyspark (Without Joining) using @Shankar Koirala's Answer

    +---+-----+        +-----+----+       +---+-----+-----+----+
    | id| name|        |secNo|city|       | id| name|secNo|city|
    +---+-----+        +-----+----+       +---+-----+-----+----+
    |  1|sammy|    +   |  101|  LA|   =>  |  1|sammy|  101|  LA|
    |  2| jill|        |  102|  CA|       |  2| jill|  102|  CA|
    |  3| john|        |  103|  DC|       |  3| john|  103|  DC|
    +---+-----+        +-----+----+       +---+-----+-----+----+

Here's My Pyspark Code

    df1_schema = StructType([StructField("id",IntegerType()),StructField("name",StringType())])
    df1 = spark.sparkContext.parallelize([(1, "sammy"),(2, "jill"),(3, "john")])

    df1 = spark.createDataFrame(df1, schema=df1_schema)

    df2_schema = StructType([StructField("secNo",IntegerType()),StructField("city",StringType())])

    df2 = spark.sparkContext.parallelize([(101, "LA"),(102, "CA"),(103,"DC")])
    df2 = spark.createDataFrame(df2, schema=df2_schema)

    df3_schema = StructType(df1.schema.fields + df2.schema.fields)

    def myFunc(x):
      dt1 = x[0]
      dt2 = x[1]

      id = dt1[0]
      name = dt1[1]
      secNo = dt2[0]
      city = dt2[1]

      return [id,name,secNo,city]


    rdd_merged = df1.rdd.zip(df2.rdd).map(lambda x: myFunc(x))

    df3 = spark.createDataFrame(rdd_merged, schema=df3_schema)

Note that the 2 tables should have the same number of rows. Thank you "Shankar Koirala"

user_78221820
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