Merge many dataframes into one in Pyspark [non pandas df] - pyspark

Question

I will be getting dataframes generated one by one through a process. I have to merge them into one.
+--------+----------+
| Name|Age |
+--------+----------+
|Alex | 30|
+--------+----------+
+--------+----------+
| Name|Age |
+--------+----------+
|Earl | 32|
+--------+----------+
+--------+----------+
| Name|Age |
+--------+----------+
|Jane | 15|
+--------+----------+
Finally:
+--------+----------+
| Name|Age |
+--------+----------+
|Alex | 30|
+--------+----------+
|Earl | 32|
+--------+----------+
|Jane | 15|
+--------+----------+
Tried many options like concat, merge, append but all are I guess pandas libraries. I am not using pandas. Using version python 2.7 and Spark 2.2
Edited to cover final scenario with foreachpartition:
l = [('Alex', 30)]
k = [('Earl', 32)]
ldf = spark.createDataFrame(l, ('Name', 'Age'))
ldf = spark.createDataFrame(k, ('Name', 'Age'))
# option 1:
union_df(ldf).show()
#option 2:
uxdf = union_df(ldf)
uxdf.show()
output in both cases:
+-------+---+
| Name|Age|
+-------+---+
|Earl | 32|
+-------+---+

Answer 1

You can use unionAll() for dataframes:
from functools import reduce # For Python 3.x
from pyspark.sql import DataFrame
def unionAll(*dfs):
return reduce(DataFrame.union, dfs)
df1 = sqlContext.createDataFrame([(1, "foo1"), (2, "bar1")], ("k", "v"))
df2 = sqlContext.createDataFrame([(3, "foo2"), (4, "bar2")], ("k", "v"))
df3 = sqlContext.createDataFrame([(5, "foo3"), (6, "bar3")], ("k", "v"))
unionAll(df1, df2, df3).show()
## +---+----+
## | k| v|
## +---+----+
## | 1|foo1|
## | 2|bar1|
## | 3|foo2|
## | 4|bar2|
## | 5|foo3|
## | 6|bar3|
## +---+----+
EDIT:
You can create an empty dataframe, and keep doing a union to it:
# Create first dataframe
ldf = spark.createDataFrame(l, ["Name", "Age"])
ldf.show()
# Save it's schema
schema = ldf.schema
# Create an empty DF with the same schema, (you need to provide schema to create empty dataframe)
empty_df = spark.createDataFrame(spark.sparkContext.emptyRDD(), schema)
empty_df.show()
# Union the first DF with the empty df
empty_df = empty_df.union(ldf)
empty_df.show()
# New dataframe after some operations
ldf = spark.createDataFrame(k, schema)
# Union with the empty_df again
empty_df = empty_df.union(ldf)
empty_df.show()
# First DF ldf
+----+---+
|Name|Age|
+----+---+
|Alex| 30|
+----+---+
# Empty dataframe empty_df
+----+---+
|Name|Age|
+----+---+
+----+---+
# After first union empty_df.union(ldf)
+----+---+
|Name|Age|
+----+---+
|Alex| 30|
+----+---+
# After second union with new ldf
+----+---+
|Name|Age|
+----+---+
|Alex| 30|
|Earl| 32|
+----+---+