Acessing nested columns in pyspark dataframe

Question

I have an xml document that looks like this:

<?xml version="1.0" encoding="UTF-8" standalone="yes"?>
<Position>
    <Search>
        <Location>
            <Region>OH</Region>
            <Country>us</Country>
            <Longitude>-816071</Longitude>
            <Latitude>415051</Latitude>
        </Location>
    </Search>
</Position>

I read it into a dataframe:

df = sqlContext.read.format('com.databricks.spark.xml').options(rowTag='Position').load('1.xml')

I can see 1 column:

df.columns
['Search']

print df.select("Search")
DataFrame[Search: struct<Location:struct<Country:string,Latitude:bigint,Longitude:bigint,Region:string>>]

How do I access the nested columns. ex Location.Region?

Answer 1

you can do something like below:

df.select("Search.Location.*").show()

output:

+-------+--------+---------+------+
|Country|Latitude|Longitude|Region|
+-------+--------+---------+------+
|     us|  415051|  -816071|    OH|
+-------+--------+---------+------+