Pyspark:使用字符串格式通过正则表达式过滤数据帧? [英] Pyspark: filter dataframe by regex with string formatting?

问题描述

我已经阅读了几篇关于使用"like"运算符通过包含字符串/表达式的条件来过滤spark数据帧的文章，但是我想知道以下内容是否是在％s中使用％s的最佳实践"所需条件如下:

I've read several posts on using the "like" operator to filter a spark dataframe by the condition of containing a string/expression, but was wondering if the following is a "best-practice" on using %s in the desired condition as follows:

input_path = <s3_location_str>
my_expr = "Arizona.*hot"  # a regex expression
dx = sqlContext.read.parquet(input_path)  # "keyword" is a field in dx

# is the following correct?
substr = "'%%%s%%'" %my_keyword  # escape % via %% to get "%"
dk = dx.filter("keyword like %s" %substr)

# dk should contain rows with keyword values such as "Arizona is hot."

注意

我正在尝试在dx中获取所有包含表达式my_keyword的行.否则，对于完全匹配，我们不需要在周围的百分号'％'.

I'm trying to get all rows in dx that contain the expression my_keyword. Otherwise, for exact matches we wouldn't need surrounding percent signs '%'.

推荐答案

从neeraj的暗示来看，在pyspark中执行此操作的正确方法似乎是:

From neeraj's hint, it seems like the correct way to do this in pyspark is:

expr = "Arizona.*hot"
dk = dx.filter(dx["keyword"].rlike(expr))

请注意，dx.filter($"keyword" ...)不起作用，因为pyspark(我的版本)似乎不支持现成的$命名法.

Note that dx.filter($"keyword" ...) did not work since (my version) of pyspark didn't seem to support the $ nomenclature out of the box.

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