Reputation: 1445
I have an ArrayType column where every element is also an array, of exactly 2 elements.
from pyspark.sql import SparkSession
data = [
{"u": ["apple", 23]},
{"u": ["banana", 12]}
]
spark = SparkSession.builder.getOrCreate()
df = spark.createDataFrame(data)
df.show()
+------------+
| u|
+------------+
| [apple, 23]|
|[banana, 12]|
+------------+
I want to replace each inner array with its first element. Had I been writing vanilla Python, it would be:
result = [ar[0] for ar in array_of_arrays]
With Spark, I can use a UDF:
from pyspark.sql import functions as f, types as t
fn = f.udf(lambda u: u[0], t.StringType())
new_df = df.select(fn(f.col("u")))
new_df.show()
+-----------+
|<lambda>(u)|
+-----------+
| apple|
| banana|
+-----------+
Which is the output I want. But how can I do this with PySpark, without:
Upvotes: 0
Views: 114
Reputation: 26676
df.withColumn('u_1', col('u')[0]).show()
+------------+------+
| u| u_1|
+------------+------+
| [apple, 23]| apple|
|[banana, 12]|banana|
+------------+------+
Upvotes: 1