Spark:通过在两个数据帧上添加行索引/编号来合并 2 个数据帧 [英] Spark: Merge 2 dataframes by adding row index/number on both dataframes
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问题描述
问:在 PySpark 中,有没有办法合并两个数据帧或将数据帧的一列复制到另一列?
Q: Is there is any way to merge two dataframes or copy a column of a dataframe to another in PySpark?
例如,我有两个数据框:
For example, I have two Dataframes:
DF1
C1 C2
23397414 20875.7353
5213970 20497.5582
41323308 20935.7956
123276113 18884.0477
76456078 18389.9269
第二个数据框
DF2
C3 C4
2008-02-04 262.00
2008-02-05 257.25
2008-02-06 262.75
2008-02-07 237.00
2008-02-08 231.00
然后我想像这样将 DF2 的 C3 添加到 DF1:
Then i want to add C3 of DF2 to DF1 like this:
New DF
C1 C2 C3
23397414 20875.7353 2008-02-04
5213970 20497.5582 2008-02-05
41323308 20935.7956 2008-02-06
123276113 18884.0477 2008-02-07
76456078 18389.9269 2008-02-08
我希望这个例子很清楚.
I hope this example was clear.
推荐答案
rownum + window function 即解决方案 1 或 zipWithIndex.map
即解决方案 2 在这种情况下应该有所帮助.
rownum + window function i.e solution 1 or zipWithIndex.map
i.e solution 2 should help in this case.
那么我建议您将 rownumber 作为附加列名添加到 Dataframe
说 df1.
Then I would suggest you to add rownumber as additional column name to Dataframe
say df1.
DF1
C1 C2 columnindex
23397414 20875.7353 1
5213970 20497.5582 2
41323308 20935.7956 3
123276113 18884.0477 4
76456078 18389.9269 5
第二个数据框
DF2
C3 C4 columnindex
2008-02-04 262.00 1
2008-02-05 257.25 2
2008-02-06 262.75 3
2008-02-07 237.00 4
2008-02-08 231.00 5
现在..做 df1 和 df2 的内部连接,就是这样...你会得到低于输出
类似的东西
from pyspark.sql.window import Window
from pyspark.sql.functions import rowNumber
w = Window().orderBy()
df1 = .... // as showed above df1
df2 = .... // as shown above df2
df11 = df1.withColumn("columnindex", rowNumber().over(w))
df22 = df2.withColumn("columnindex", rowNumber().over(w))
newDF = df11.join(df22, df11.columnindex == df22.columnindex, 'inner').drop(df22.columnindex)
newDF.show()
New DF
C1 C2 C3
23397414 20875.7353 2008-02-04
5213970 20497.5582 2008-02-05
41323308 20935.7956 2008-02-06
123276113 18884.0477 2008-02-07
76456078 18389.9269 2008-02-08
解决方案 2:Scala 中的另一种好方法(可能这是最好的 :)),您可以将其转换为 pyspark :
/**
* Add Column Index to dataframe
*/
def addColumnIndex(df: DataFrame) = sqlContext.createDataFrame(
// Add Column index
df.rdd.zipWithIndex.map{case (row, columnindex) => Row.fromSeq(row.toSeq :+ columnindex)},
// Create schema
StructType(df.schema.fields :+ StructField("columnindex", LongType, false))
)
// Add index now...
val df1WithIndex = addColumnIndex(df1)
val df2WithIndex = addColumnIndex(df2)
// Now time to join ...
val newone = df1WithIndex
.join(df2WithIndex , Seq("columnindex"))
.drop("columnindex")
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