[python] Concatenate two PySpark dataframes

I'm trying to concatenate two PySpark dataframes with some columns that are only on each 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?

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

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|
+---+-----------+--------------------+--------------------+

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) 

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.

You can use unionByName to make this:

df = df_1.unionByName(df_2)

unionByName is available since Spark 2.3.0.

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))

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_))

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.

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()

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

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)