使用 None 值过滤 Pyspark 数据框列 [英] Filter Pyspark dataframe column with None value

问题描述

我正在尝试过滤具有 None 作为行值的 PySpark 数据帧:

I'm trying to filter a PySpark dataframe that has None as a row value:

df.select('dt_mvmt').distinct().collect()

[Row(dt_mvmt=u'2016-03-27'),
 Row(dt_mvmt=u'2016-03-28'),
 Row(dt_mvmt=u'2016-03-29'),
 Row(dt_mvmt=None),
 Row(dt_mvmt=u'2016-03-30'),
 Row(dt_mvmt=u'2016-03-31')]

并且我可以使用字符串值正确过滤:

and I can filter correctly with an string value:

df[df.dt_mvmt == '2016-03-31']
# some results here

但这失败了:

df[df.dt_mvmt == None].count()
0
df[df.dt_mvmt != None].count()
0

但每个类别肯定都有价值.怎么回事?

But there are definitely values on each category. What's going on?

推荐答案

您可以使用 Column.isNull/Column.isNotNull:

df.where(col("dt_mvmt").isNull())

df.where(col("dt_mvmt").isNotNull())

如果你想简单地删除 NULL 值，你可以使用 na.drop 和 subset 参数:

If you want to simply drop NULL values you can use na.drop with subset argument:

df.na.drop(subset=["dt_mvmt"])

与 NULL 的基于相等的比较将不起作用，因为在 SQL 中 NULL 是未定义的，因此任何将其与另一个值进行比较的尝试都会返回 NULL:

Equality based comparisons with NULL won't work because in SQL NULL is undefined so any attempt to compare it with another value returns NULL:

sqlContext.sql("SELECT NULL = NULL").show()
## +-------------+
## |(NULL = NULL)|
## +-------------+
## |         null|
## +-------------+


sqlContext.sql("SELECT NULL != NULL").show()
## +-------------------+
## |(NOT (NULL = NULL))|
## +-------------------+
## |               null|
## +-------------------+

将值与 NULL 进行比较的唯一有效方法是 IS/IS NOT，它们等价于 isNull>/isNotNull 方法调用.

The only valid method to compare value with NULL is IS / IS NOT which are equivalent to the isNull / isNotNull method calls.

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