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I have a PySpark dataframe

+-------+--------------+----+----+
|address|          date|name|food|
+-------+--------------+----+----+
|1111111|20151122045510| Yin|gre |
|1111111|20151122045501| Yin|gre |
|1111111|20151122045500| Yln|gra |
|1111112|20151122065832| Yun|ddd |
|1111113|20160101003221| Yan|fdf |
|1111111|20160703045231| Yin|gre |
|1111114|20150419134543| Yin|fdf |
|1111115|20151123174302| Yen|ddd |
|2111115|      20123192| Yen|gre |
+-------+--------------+----+----+


that I want to transform to use with pyspark.ml. I can use a StringIndexer to convert the name column to a numeric category:

indexer = StringIndexer(inputCol="name", outputCol="name_index").fit(df)
df_ind = indexer.transform(df)
df_ind.show()


+-------+--------------+----+----------+----+
|address|          date|name|name_index|food|
+-------+--------------+----+----------+----+
|1111111|20151122045510| Yin|       0.0|gre |
|1111111|20151122045501| Yin|       0.0|gre |
|1111111|20151122045500| Yln|       2.0|gra |
|1111112|20151122065832| Yun|       4.0|ddd |
|1111113|20160101003221| Yan|       3.0|fdf |
|1111111|20160703045231| Yin|       0.0|gre |
|1111114|20150419134543| Yin|       0.0|fdf |
|1111115|20151123174302| Yen|       1.0|ddd |
|2111115|      20123192| Yen|       1.0|gre |
+-------+--------------+----+----------+----+


How can I transform several columns with StringIndexer (for example, name and food, each with its own StringIndexer) and then use VectorAssembler to generate a feature vector? Or do I have to create a StringIndexer for each column?

1 Answer

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by (32.3k points)

With PySpark 3.0+ this is now easier and you can use the inputCols and outputCols options: 

http://spark.apache.org/docs/latest/api/python/pyspark.ml.html#pyspark.ml.feature.StringIndexer

class pyspark.ml.feature.StringIndexer(inputCol=None, outputCol=None, inputCols=None, outputCols=None, handleInvalid='error', stringOrderType='frequencyDesc')

If you want to learn PySpark, you can check out the PySpark course by Intellipaat.

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