Pyspark 按另一个数据帧的列过滤数据帧 [英] Pyspark filter dataframe by columns of another dataframe

问题描述

不知道为什么我在这方面遇到困难，考虑到在 R 或 Pandas 中很容易做到，这似乎很简单.我想避免使用 Pandas，因为我要处理大量数据，而且我相信 toPandas() 会将所有数据加载到 pyspark 中的驱动程序内存中.

Not sure why I'm having a difficult time with this, it seems so simple considering it's fairly easy to do in R or pandas. I wanted to avoid using pandas though since I'm dealing with a lot of data, and I believe toPandas() loads all the data into the driver’s memory in pyspark.

我有 2 个数据帧:df1 和 df2.我想过滤 df1(删除所有行)，其中 df1.userid = df2.userid AND df1.group = df2.group.我不确定是否应该使用 filter()、join() 或 sql 例如:

I have 2 dataframes: df1 and df2. I want to filter df1 (remove all rows) where df1.userid = df2.userid AND df1.group = df2.group. I wasn't sure if I should use filter(), join(), or sql For example:

df1:
+------+----------+--------------------+
|userid|   group  |      all_picks     |
+------+----------+--------------------+
|   348|         2|[225, 2235, 2225]   |
|   567|         1|[1110, 1150]        |
|   595|         1|[1150, 1150, 1150]  |
|   580|         2|[2240, 2225]        |
|   448|         1|[1130]              |
+------+----------+--------------------+

df2:
+------+----------+---------+
|userid|   group  |   pick  |
+------+----------+---------+
|   348|         2|     2270|
|   595|         1|     2125|
+------+----------+---------+

Result I want:
+------+----------+--------------------+
|userid|   group  |      all_picks     |
+------+----------+--------------------+
|   567|         1|[1110, 1150]        |
|   580|         2|[2240, 2225]        |
|   448|         1|[1130]              |
+------+----------+--------------------+

我尝试了很多 join() 和 filter() 函数，我相信我得到的最接近的是:

I've tried many join() and filter() functions, I believe the closest I got was:

cond = [df1.userid == df2.userid, df2.group == df2.group]
df1.join(df2, cond, 'left_outer').select(df1.userid, df1.group, df1.all_picks) # Result has 7 rows

我尝试了很多不同的连接类型，也尝试了不同的

I tried a bunch of different join types, and I also tried different

cond values:
    cond = ((df1.userid == df2.userid) & (df2.group == df2.group)) # result has 7 rows
    cond = ((df1.userid != df2.userid) & (df2.group != df2.group)) # result has 2 rows

但是，连接似乎是在添加额外的行，而不是删除.

However, it seems like the joins are adding additional rows, rather than deleting.

我正在使用 python 2.7 和 spark 2.1.0

推荐答案

Left anti join 正是您要找的:

Left anti join is what you're looking for:

df1.join(df2, ["userid", "group"], "leftanti")

但是同样的事情可以用左外连接来完成:

but the same thing can be done with left outer join:

(df1
    .join(df2, ["userid", "group"], "leftouter")
    .where(df2["pick"].isNull())
    .drop(df2["pick"]))

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