在Pyspark[non-df]中将多个数据帧合并为一个
我将通过一个过程一个接一个地生成数据帧。我必须把它们合并成一个在Pyspark[non-df]中将多个数据帧合并为一个,pyspark,pyspark-dataframes,Pyspark,Pyspark Dataframes,我将通过一个过程一个接一个地生成数据帧。我必须把它们合并成一个 +--------+----------+ | Name|Age | +--------+----------+ |Alex | 30| +--------+----------+ +--------+----------+ | Name|Age | +--------+----------+ |Earl | 32| +--------+--------
+--------+----------+
| Name|Age |
+--------+----------+
|Alex | 30|
+--------+----------+
+--------+----------+
| Name|Age |
+--------+----------+
|Earl | 32|
+--------+----------+
+--------+----------+
| Name|Age |
+--------+----------+
|Jane | 15|
+--------+----------+
最后:
+--------+----------+
| Name|Age |
+--------+----------+
|Alex | 30|
+--------+----------+
|Earl | 32|
+--------+----------+
|Jane | 15|
+--------+----------+
尝试了许多选项,如concat、merge、append,但我想它们都是pandas库。我没有使用熊猫。使用Python2.7版和Spark 2.2版
编辑以涵盖foreachpartition的最终场景:
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()
两种情况下的输出:
+-------+---+
| Name|Age|
+-------+---+
|Earl | 32|
+-------+---+
您可以对数据帧使用unionAll()
:
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|
## +---+----+
编辑:
您可以创建一个空数据帧,并继续对其进行联合:
# 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|
+----+---+
事实上,在您键入此答案的同时,我实现了相同的代码(可能是:)一个变化是,Python2.7中不推荐使用unionAll,并给出了警告。然而,工会也在这里工作。创建一个新的dfTried。它只保留最终df的值,而丢弃其他分区…分区也是数据帧,尝试使用上面的函数来泛化到分区,而不是dataframesUnable@pissall。你能给我一点建议吗