从Spark中的数据框列值中删除空格 [英] Remove blank space from data frame column values in Spark

问题描述

我有一个架构的数据框(business_df):

I have a data frame (business_df) of schema:

|-- business_id: string (nullable = true)
|-- categories: array (nullable = true)
|    |-- element: string (containsNull = true)
|-- city: string (nullable = true)
|-- full_address: string (nullable = true)
|-- hours: struct (nullable = true)
|-- name: string (nullable = true)

我想创建一个新的数据帧(new_df)，以便'name'列中的值不包含任何空格.

I want to make a new data frame (new_df) so that the values in the 'name' column do not contain any blank spaces.

我的代码是:

from pyspark import SparkContext
from pyspark.sql import SQLContext
from pyspark.sql import HiveContext
from pyspark.sql.functions import UserDefinedFunction
from pyspark.sql.types import StringType

udf = UserDefinedFunction(lambda x: x.replace(' ', ''), StringType())
new_df = business_df.select(*[udf(column).alias(name) if column == name else column for column in business_df.columns])
new_df.registerTempTable("vegas")
new_df.printSchema()
vegas_business = sqlContext.sql("SELECT stars, name from vegas limit 10").collect()

我一直收到此错误:

NameError: global name 'replace' is not defined

此代码有什么问题?

推荐答案

虽然所描述的问题无法通过提供的代码重现，但使用Python UDFs来处理此类简单任务却效率低下.如果您只想从文本中删除空格，请使用regexp_replace:

While the problem you've described is not reproducible with provided code, using Python UDFs to handle simple tasks like this, is rather inefficient. If you want to simply remove spaces from the text use regexp_replace:

from pyspark.sql.functions import regexp_replace, col

df = sc.parallelize([
    (1, "foo bar"), (2, "foobar "), (3, "   ")
]).toDF(["k", "v"])

df.select(regexp_replace(col("v"), " ", ""))

如果要规范空行，请使用trim:

If you want to normalize empty lines use trim:

from pyspark.sql.functions import trim

df.select(trim(col("v")))

如果要保留前导/尾随空格，可以调整regexp_replace:

If you want to keep leading / trailing spaces you can adjust regexp_replace:

df.select(regexp_replace(col("v"), "^\s+$", ""))

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