Question

我尝试搜索此内容，但最接近的是this。但这并没有给我我想要的东西。我想将重复项的所有实例都放在数据框中。例如，如果我有一个数据框

   Col1   Col2   Col3
   Alice  Girl   April
   Jean   Boy    Aug
   Jean   Boy    Sept

我想删除基于Col1和Col2的所有重复项，以便获得

  Col1   Col2  Col3
  Alice  Girl  April

有什么办法吗？

如果我有很多这样的列：

   Col1   Col2   Col3  .... Col n
   Alice  Girl   April .... Apple
   Jean   Boy    Aug   .... Orange
   Jean   Boy    Sept  .... Banana

我如何仅按Col1和Col2分组，但仍保留其余列？

谢谢

Answer 1

from pyspark.sql import functions as F
# Sample Dataframe
df = sqlContext.createDataFrame([
    ["Alice", "Girl","April"],
   ["Jean","Boy", "Aug"],
   ["Jean","Boy","Sept"]
], 
    ["Col1","Col2", "Col3"])

# Group by on required column and select rows where count is 1.
df2 = (df
       .groupBy(["col1", "col2"])
       .agg(
           F.count(F.lit(1)).alias('count'), 
           F.max("col3").alias("col3")).where("count = 1")).drop("count")

df2.show(10, False)

输出：

+-----+----+-----+
|col1 |col2|col3 |
+-----+----+-----+
|Alice|Girl|April|
+-----+----+-----+

回复编辑后的版本

df = sqlContext.createDataFrame([
    ["Alice", "Girl","April", "April"],
    ["Jean","Boy", "Aug", "XYZ"],
    ["Jean","Boy","Sept", "IamBatman"]
], 
    ["col1","col2", "col3", "newcol"])

groupingcols = ["col1", "col2"]
othercolumns = [F.max(col).alias(col) for col in df.columns if col not in groupingcols]

df2 = (df
       .groupBy(groupingcols)
       .agg(F.count(F.lit(1)).alias('count'), *othercolumns)
       .where("count = 1")
       .drop("count"))

df2.show(10, False)

输出：

+-----+----+-----+------+
|col1 |col2|col3 |newcol|
+-----+----+-----+------+
|Alice|Girl|April|April |
+-----+----+-----+------+

在pyspark中删除所有重复的实例

1 个答案:

回复编辑后的版本