I'm trying to concatenate two PySpark dataframes with some columns that are only on one of them:

from pyspark.sql.functions import randn, rand df_1 = sqlContext.range(0, 10) +--+ |id| +--+ | 0| | 1| | 2| | 3| | 4| | 5| | 6| | 7| | 8| | 9| +--+ df_2 = sqlContext.range(11, 20) +--+ |id| +--+ | 10| | 11| | 12| | 13| | 14| | 15| | 16| | 17| | 18| | 19| +--+ df_1 = df_1.select("id", rand(seed=10).alias("uniform"), randn(seed=27).alias("normal")) df_2 = df_2.select("id", rand(seed=10).alias("uniform"), randn(seed=27).alias("normal_2")) 

and now I want to generate a third dataframe. I would like something like pandas concat:

df_1.show() +---+--------------------+--------------------+ | id| uniform| normal| +---+--------------------+--------------------+ | 0| 0.8122802274304282| 1.2423430583597714| | 1| 0.8642043127063618| 0.3900018344856156| | 2| 0.8292577771850476| 1.8077401259195247| | 3| 0.198558705368724| -0.4270585782850261| | 4|0.012661361966674889| 0.702634599720141| | 5| 0.8535692890157796|-0.42355804115129153| | 6| 0.3723296190171911| 1.3789648582622995| | 7| 0.9529794127670571| 0.16238718777444605| | 8| 0.9746632635918108| 0.02448061333761742| | 9| 0.513622008243935| 0.7626741803250845| +---+--------------------+--------------------+ df_2.show() +---+--------------------+--------------------+ | id| uniform| normal_2| +---+--------------------+--------------------+ | 11| 0.3221262660507942| 1.0269298899109824| | 12| 0.4030672316912547| 1.285648175568798| | 13| 0.9690555459609131|-0.22986601831364423| | 14|0.011913836266515876| -0.678915153834693| | 15| 0.9359607054250594|-0.16557488664743034| | 16| 0.45680471157575453| -0.3885563551710555| | 17| 0.6411908952297819| 0.9161177183227823| | 18| 0.5669232696934479| 0.7270125277020573| | 19| 0.513622008243935| 0.7626741803250845| +---+--------------------+--------------------+ #do some concatenation here, how? df_concat.show() | id| uniform| normal| normal_2 | +---+--------------------+--------------------+------------+ | 0| 0.8122802274304282| 1.2423430583597714| None | | 1| 0.8642043127063618| 0.3900018344856156| None | | 2| 0.8292577771850476| 1.8077401259195247| None | | 3| 0.198558705368724| -0.4270585782850261| None | | 4|0.012661361966674889| 0.702634599720141| None | | 5| 0.8535692890157796|-0.42355804115129153| None | | 6| 0.3723296190171911| 1.3789648582622995| None | | 7| 0.9529794127670571| 0.16238718777444605| None | | 8| 0.9746632635918108| 0.02448061333761742| None | | 9| 0.513622008243935| 0.7626741803250845| None | | 11| 0.3221262660507942| None | 0.123 | | 12| 0.4030672316912547| None |0.12323 | | 13| 0.9690555459609131| None |0.123 | | 14|0.011913836266515876| None |0.18923 | | 15| 0.9359607054250594| None |0.99123 | | 16| 0.45680471157575453| None |0.123 | | 17| 0.6411908952297819| None |1.123 | | 18| 0.5669232696934479| None |0.10023 | | 19| 0.513622008243935| None |0.916332123 | +---+--------------------+--------------------+------------+ 

Is that possible?

12 Answers

Maybe you can try creating the unexisting columns and calling union (unionAll for Spark 1.6 or lower):

from pyspark.sql.functions import lit cols = ['id', 'uniform', 'normal', 'normal_2'] df_1_new = df_1.withColumn("normal_2", lit(None)).select(cols) df_2_new = df_2.withColumn("normal", lit(None)).select(cols) result = df_1_new.union(df_2_new) 

df_concat = df_1.union(df_2) 

The dataframes may need to have identical columns, in which case you can use withColumn() to create normal_1 and normal_2

You can use unionByName to make this:

df = df_1.unionByName(df_2) 

unionByName is available since Spark 2.3.0.

unionByName is a built-in option available in spark which is available from spark 2.3.0.

with spark version 3.1.0, there is allowMissingColumns option with the default value set to False to handle missing columns. Even if both dataframes don't have the same set of columns, this function will work, setting missing column values to null in the resulting dataframe.

df_1.unionByName(df_2, allowMissingColumns=True).show() +---+--------------------+--------------------+--------------------+ | id| uniform| normal| normal_2| +---+--------------------+--------------------+--------------------+ | 0| 0.8122802274304282| 1.2423430583597714| null| | 1| 0.8642043127063618| 0.3900018344856156| null| | 2| 0.8292577771850476| 1.8077401259195247| null| | 3| 0.198558705368724| -0.4270585782850261| null| | 4|0.012661361966674889| 0.702634599720141| null| | 5| 0.8535692890157796|-0.42355804115129153| null| | 6| 0.3723296190171911| 1.3789648582622995| null| | 7| 0.9529794127670571| 0.16238718777444605| null| | 8| 0.9746632635918108| 0.02448061333761742| null| | 9| 0.513622008243935| 0.7626741803250845| null| | 11| 0.3221262660507942| null| 1.0269298899109824| | 12| 0.4030672316912547| null| 1.285648175568798| | 13| 0.9690555459609131| null|-0.22986601831364423| | 14|0.011913836266515876| null| -0.678915153834693| | 15| 0.9359607054250594| null|-0.16557488664743034| | 16| 0.45680471157575453| null| -0.3885563551710555| | 17| 0.6411908952297819| null| 0.9161177183227823| | 18| 0.5669232696934479| null| 0.7270125277020573| | 19| 0.513622008243935| null| 0.7626741803250845| +---+--------------------+--------------------+--------------------+ 

To make it more generic of keeping both columns in df1 and df2:

import pyspark.sql.functions as F # Keep all columns in either df1 or df2 def outter_union(df1, df2): # Add missing columns to df1 left_df = df1 for column in set(df2.columns) - set(df1.columns): left_df = left_df.withColumn(column, F.lit(None)) # Add missing columns to df2 right_df = df2 for column in set(df1.columns) - set(df2.columns): right_df = right_df.withColumn(column, F.lit(None)) # Make sure columns are ordered the same return left_df.union(right_df.select(left_df.columns)) 

Here is one way to do it, in case it is still useful: I ran this in pyspark shell, Python version 2.7.12 and my Spark install was version 2.0.1.

PS: I guess you meant to use different seeds for the df_1 df_2 and the code below reflects that.

from pyspark.sql.types import FloatType from pyspark.sql.functions import randn, rand import pyspark.sql.functions as F df_1 = sqlContext.range(0, 10) df_2 = sqlContext.range(11, 20) df_1 = df_1.select("id", rand(seed=10).alias("uniform"), randn(seed=27).alias("normal")) df_2 = df_2.select("id", rand(seed=11).alias("uniform"), randn(seed=28).alias("normal_2")) def get_uniform(df1_uniform, df2_uniform): if df1_uniform: return df1_uniform if df2_uniform: return df2_uniform u_get_uniform = F.udf(get_uniform, FloatType()) df_3 = df_1.join(df_2, on = "id", how = 'outer').select("id", u_get_uniform(df_1["uniform"], df_2["uniform"]).alias("uniform"), "normal", "normal_2").orderBy(F.col("id")) 

Here are the outputs I get:

df_1.show() +---+-------------------+--------------------+ | id| uniform| normal| +---+-------------------+--------------------+ | 0|0.41371264720975787| 0.5888539012978773| | 1| 0.7311719281896606| 0.8645537008427937| | 2| 0.1982919638208397| 0.06157382353970104| | 3|0.12714181165849525| 0.3623040918178586| | 4| 0.7604318153406678|-0.49575204523675975| | 5|0.12030715258495939| 1.0854146699817222| | 6|0.12131363910425985| -0.5284523629183004| | 7|0.44292918521277047| -0.4798519469521663| | 8| 0.8898784253886249| -0.8820294772950535| | 9|0.03650707717266999| -2.1591956435415334| +---+-------------------+--------------------+ df_2.show() +---+-------------------+--------------------+ | id| uniform| normal_2| +---+-------------------+--------------------+ | 11| 0.1982919638208397| 0.06157382353970104| | 12|0.12714181165849525| 0.3623040918178586| | 13|0.12030715258495939| 1.0854146699817222| | 14|0.12131363910425985| -0.5284523629183004| | 15|0.44292918521277047| -0.4798519469521663| | 16| 0.8898784253886249| -0.8820294772950535| | 17| 0.2731073068483362|-0.15116027592854422| | 18| 0.7784518091224375| -0.3785563841011868| | 19|0.43776394586845413| 0.47700719174464357| +---+-------------------+--------------------+ df_3.show() +---+-----------+--------------------+--------------------+ | id| uniform| normal| normal_2| +---+-----------+--------------------+--------------------+ | 0| 0.41371265| 0.5888539012978773| null| | 1| 0.7311719| 0.8645537008427937| null| | 2| 0.19829196| 0.06157382353970104| null| | 3| 0.12714182| 0.3623040918178586| null| | 4| 0.7604318|-0.49575204523675975| null| | 5|0.120307155| 1.0854146699817222| null| | 6| 0.12131364| -0.5284523629183004| null| | 7| 0.44292918| -0.4798519469521663| null| | 8| 0.88987845| -0.8820294772950535| null| | 9|0.036507078| -2.1591956435415334| null| | 11| 0.19829196| null| 0.06157382353970104| | 12| 0.12714182| null| 0.3623040918178586| | 13|0.120307155| null| 1.0854146699817222| | 14| 0.12131364| null| -0.5284523629183004| | 15| 0.44292918| null| -0.4798519469521663| | 16| 0.88987845| null| -0.8820294772950535| | 17| 0.27310732| null|-0.15116027592854422| | 18| 0.7784518| null| -0.3785563841011868| | 19| 0.43776396| null| 0.47700719174464357| +---+-----------+--------------------+--------------------+ 

To concatenate multiple pyspark dataframes into one:

from functools import reduce reduce(lambda x,y:x.union(y), [df_1,df_2]) 

And you can replace the list of [df_1, df_2] to a list of any length.

Above answers are very elegant. I have written this function long back where i was also struggling to concatenate two dataframe with distinct columns.

Suppose you have dataframe sdf1 and sdf2

from pyspark.sql import functions as F from pyspark.sql.types import * def unequal_union_sdf(sdf1, sdf2): s_df1_schema = set((x.name, x.dataType) for x in sdf1.schema) s_df2_schema = set((x.name, x.dataType) for x in sdf2.schema) for i,j in s_df2_schema.difference(s_df1_schema): sdf1 = sdf1.withColumn(i,F.lit(None).cast(j)) for i,j in s_df1_schema.difference(s_df2_schema): sdf2 = sdf2.withColumn(i,F.lit(None).cast(j)) common_schema_colnames = sdf1.columns sdk = \ sdf1.select(common_schema_colnames).union(sdf2.select(common_schema_colnames)) return sdk sdf_concat = unequal_union_sdf(sdf1, sdf2) 

This should do it for you ...

from pyspark.sql.types import FloatType from pyspark.sql.functions import randn, rand, lit, coalesce, col import pyspark.sql.functions as F df_1 = sqlContext.range(0, 6) df_2 = sqlContext.range(3, 10) df_1 = df_1.select("id", lit("old").alias("source")) df_2 = df_2.select("id") df_1.show() df_2.show() df_3 = df_1.alias("df_1").join(df_2.alias("df_2"), df_1.id == df_2.id, "outer")\ .select(\ [coalesce(df_1.id, df_2.id).alias("id")] +\ [col("df_1." + c) for c in df_1.columns if c != "id"])\ .sort("id") df_3.show() 

I was trying to implement pandas append functionality in pyspark and what I created a custom function where we can concat 2 or more data frame even they are having different no. of columns only condition is if dataframes have identical name then their datatype should be same/match.

I have written a custom function to merge 2 dataframes.

def append_dfs(df1,df2): list1 = df1.columns list2 = df2.columns for col in list2: if(col not in list1): df1 = df1.withColumn(col, F.lit(None)) for col in list1: if(col not in list2): df2 = df2.withColumn(col, F.lit(None)) return df1.unionByName(df2) 

usage:

1. concate 2 dataframes

   final_df = append_dfs(df1,df2)

2. 1. concate more than 2(say3) dataframes

   final_df = append_dfs(append_dfs(df1,df2),df3)

example:

df1:

df2:

result=append_dfs(df1,df2)

result :

Hope this will useful.

I would solve this in this way:

from pyspark.sql import SparkSession df_1.createOrReplaceTempView("tab_1") df_2.createOrReplaceTempView("tab_2") df_concat=spark.sql("select tab_1.id,tab_1.uniform,tab_1.normal,tab_2.normal_2 from tab_1 tab_1 left join tab_2 tab_2 on tab_1.uniform=tab_2.uniform\ union\ select tab_2.id,tab_2.uniform,tab_1.normal,tab_2.normal_2 from tab_2 tab_2 left join tab_1 tab_1 on tab_1.uniform=tab_2.uniform") df_concat.show() 

Maybe, you want to concatenate more of two Dataframes. I found a issue which use pandas Dataframe conversion.

Suppose you have 3 spark Dataframe who want to concatenate.

The code is the following:

list_dfs = [] list_dfs_ = [] df = spark.read.json('path_to_your_jsonfile.json',multiLine = True) df2 = spark.read.json('path_to_your_jsonfile2.json',multiLine = True) df3 = spark.read.json('path_to_your_jsonfile3.json',multiLine = True) list_dfs.extend([df,df2,df3]) for df in list_dfs : df = df.select([column for column in df.columns]).toPandas() list_dfs_.append(df) list_dfs.clear() df_ = sqlContext.createDataFrame(pd.concat(list_dfs_))