[Python] Apply StringIndexer to several columns in a PySpark Dataframe

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.

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