I have a df_movies and col of geners that look like json format.
|genres |
[{'id': 28, 'name': 'Action'}, {'id': 12, 'name': 'Adventure'}, {'id': 37, 'name': 'Western'}]
How can I extract the first field of 'name': val?
way #1
df_movies.withColumn
("genres_extract",regexp_extract(col("genres"),
""" 'name': (\w+)""",1)).show(false)
way #2
df_movies.withColumn
("genres_extract",regexp_extract(col("genres"),
"""[{'id':\s\d,\s 'name':\s(\w+)""",1))
Excepted: Action
You can use get_json_object function:
Seq("""[{"id": 28, "name": "Action"}, {"id": 12, "name": "Adventure"}, {"id": 37, "name": "Western"}]""")
.toDF("genres")
.withColumn("genres_extract", get_json_object(col("genres"), "$[0].name" ))
.show()
+--------------------+--------------+
| genres|genres_extract|
+--------------------+--------------+
|[{"id": 28, "name...| Action|
+--------------------+--------------+
Another possibility is using the from_json function together with a self defined schema. This allows you to "unwrap" the json structure into a dataframe with all of the data in there, so that you can use it however you want!
Something like the following:
import org.apache.spark.sql.types._
Seq("""[{"id": 28, "name": "Action"}, {"id": 12, "name": "Adventure"}, {"id": 37, "name": "Western"}]""")
.toDF("genres")
// Creating the necessary schema for the from_json function
val moviesSchema = ArrayType(
new StructType()
.add("id", StringType)
.add("name", StringType)
)
// Parsing the json string into our schema, exploding the column to make one row
// per json object in the array and then selecting the wanted columns,
// unwrapping the parsedActions column into separate columns
val parsedDf = df
.withColumn("parsedMovies", explode(from_json(col("genres"), moviesSchema)))
.select("parsedMovies.*")
parsedDf.show(false)
+---+---------+
| id| name|
+---+---------+
| 28| Action|
| 12|Adventure|
| 37| Western|
+---+---------+
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