Question

我正在尝试连接两个数据帧，看起来像这样：

df1:

+---+---+
|  a|  b|
+---+---+
|  a|  b|
|  1|  2|
+---+---+
only showing top 2 rows

df2:

+---+---+
|  c|  d|
+---+---+
|  c|  d|
|  7|  8|
+---+---+
only showing top 2 rows

它们都有相同的行数，我想做类似的事情：

+---+---+---+---+                
|  a|  b|  c|  d|            
+---+---+---+---+           
|  a|  b|  c|  d|          
|  1|  2|  7|  8|    
+---+---+---+---+

我试过了：

df1=df1.withColumn('c', df2.c).collect()

df1=df1.withColumn('d', df2.d).collect()

但没有成功，给我这个错误：

Traceback (most recent call last):
  File "/usr/hdp/current/spark-client/python/pyspark/sql/utils.py", line 45, in deco
    return f(*a, **kw)
  File "/usr/hdp/current/spark-client/python/lib/py4j-0.9-src.zip/py4j/protocol.py", line 308, in get_return_value
    format(target_id, ".", name), value)
py4j.protocol.Py4JJavaError: An error occurred while calling o2804.withColumn.

有办法吗？

由于

Answer 1

以下是@Suresh提案的示例，添加列rownumber

from pyspark.sql import functions as F
df1 = sqlctx.createDataFrame([('a','b'),('1','2')],['a','b']).withColumn("row_number", F.row_number().over(Window.partitionBy().orderBy("a")))
df2 = sqlctx.createDataFrame([('c','d'),('7','8')],['c','d']).withColumn("row_number", F.row_number().over(Window.partitionBy().orderBy("c")))

 df3=df1.join(df2,df1.row_number==df2.row_number,'inner')\
                       .select(df1.a,df1.b,df2.c,df2.d)

 df3=df1.join(df2,df1.row_number==df2.row_number,'inner').select(df1.a,df1.b,df2.c,df2.d)
 df3.show()

连接两个数据帧pyspark

1 个答案: