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?
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)
# To remove the duplicates:
result = result.dropDuplicates()