remove last pipe-delimited value from dataframe column in pyspark


I am using spark 2.1 and have a dataframe column contain value like AB|12|XY|4. I want to create a new column by removing the last element, so it should show like AB|12|XY.

I tried to split, rsplit did not work, so need some suggestion to get the desired output.


Use the Spark SQL <a href="https://spark.apache.org/docs/2.1.0/api/python/pyspark.sql.html#pyspark.sql.functions.split" rel="nofollow">split function</a> as follows:

<pre class="lang-python prettyprint-override">>>> from pyspark.sql.functions import split >>> json_data = ['{"c1":"AB|12|XY|4"}','{"c1":"11|22|33|44|remove"}'] >>> df = spark.read.json(sc.parallelize(json_data)) >>> df.show() +------------------+ | c1| +------------------+ | AB|12|XY|4| |11|22|33|44|remove| +------------------+ >>> df2 = df.withColumn("c2", split(df.c1, '\|\w+$')[0]) # split takes a regex pattern >>> df2.show() +------------------+-----------+ | c1| c2| +------------------+-----------+ | AB|12|XY|4| AB|12|XY| |11|22|33|44|remove|11|22|33|44| +------------------+-----------+

If you need to do something more complicated that can't be implemented using the built-in functions, you can define your own user-defined function (UDF):

<pre class="lang-python prettyprint-override">>>> from pyspark.sql.functions import udf >>> from pyspark.sql.types import * >>> def my_func(str): ... return str.rsplit('|',1)[0] ... >>> my_udf = udf(my_func, StringType()) >>> json_data = ['{"c1":"AB|12|XY|4"}','{"c1":"11|22|33|44|remove"}'] >>> df = spark.read.json(sc.parallelize(json_data)) >>> df2 = df.withColumn("c2", my_udf(df.c1)) >>> df2.show() +------------------+-----------+ | c1| c2| +------------------+-----------+ | AB|12|XY|4| AB|12|XY| |11|22|33|44|remove|11|22|33|44| +------------------+-----------+

Built-in <a href="http://www.cs.sfu.ca/CourseCentral/732/ggbaker/content/spark-sql.html#udf" rel="nofollow">SQL functions are preferred</a> (also <a href="http://www.cs.sfu.ca/CourseCentral/732/ggbaker/content/spark-sql.html#python-jvm" rel="nofollow">here</a>) because your data does not get passed back and forth between the JVM process and the Python process, which is what happens when you use a UDF.


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