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?
** EDIT **: This is not a dupe because I need to to this programatically for several data frames with different column names. I can't use VectorIndexer or VectorAssembler because the columns are not numerical.
** EDIT 2**: A tentative solution is
indexers = [StringIndexer(inputCol=column, outputCol=column+"_index").fit(df).transform(df) for column in df.columns ]
where I create a list now with three dataframes, each identical to the original plus the transformed column. Now I need to join then to form the final dataframe, but that's very inefficient.
The best way that I've found to do it is to combine several StringIndex on a list and use a Pipeline to execute them all:
from pyspark.ml import Pipeline
from pyspark.ml.feature import StringIndexer
indexers = [StringIndexer(inputCol=column, outputCol=column+"_index").fit(df) for column in list(set(df.columns)-set(['date'])) ]
pipeline = Pipeline(stages=indexers)
df_r = pipeline.fit(df).transform(df)
df_r.show()
+-------+--------------+----+----+----------+----------+-------------+
|address| date|food|name|food_index|name_index|address_index|
+-------+--------------+----+----+----------+----------+-------------+
|1111111|20151122045510| gre| Yin| 0.0| 0.0| 0.0|
|1111111|20151122045501| gra| Yin| 2.0| 0.0| 0.0|
|1111111|20151122045500| gre| Yln| 0.0| 2.0| 0.0|
|1111112|20151122065832| gre| Yun| 0.0| 4.0| 3.0|
|1111113|20160101003221| gre| Yan| 0.0| 3.0| 1.0|
|1111111|20160703045231| gre| Yin| 0.0| 0.0| 0.0|
|1111114|20150419134543| gre| Yin| 0.0| 0.0| 5.0|
|1111115|20151123174302| ddd| Yen| 1.0| 1.0| 2.0|
|2111115| 20123192| ddd| Yen| 1.0| 1.0| 4.0|
+-------+--------------+----+----+----------+----------+-------------+