Find mean of pyspark array<double>

In your case, your options are use explode or a udf. As you've noted, explode is unnecessarily expensive. Thus, a udf is the way to go.

You can write your own function to take the mean of a list of numbers, or just piggy back off of numpy.mean. If you use numpy.mean, you'll have to cast the result to a float (because spark doesn't know how to handle numpy.float64s).

import numpy as np
from pyspark.sql.functions import udf
from pyspark.sql.types import FloatType

array_mean = udf(lambda x: float(np.mean(x)), FloatType())
df.select(array_mean("longitude").alias("avg")).show()
#+---------+
#|      avg|
#+---------+
#|    -81.9|
#|-82.93166|
#|   -82.93|
#+---------+

In the recent Spark versions (2.4 or later) the most efficient solution is to use aggregate higher order function:

from pyspark.sql.functions import expr

query = """aggregate(
    `{col}`,
    CAST(0.0 AS double),
    (acc, x) -> acc + x,
    acc -> acc / size(`{col}`)
) AS  `avg_{col}`""".format(col="longitude")

df.selectExpr("*", query).show()

+--------------------+------------------+
|           longitude|     avg_longitude|
+--------------------+------------------+
|      [-80.9, -82.9]|             -81.9|
|[-82.92, -82.93, ...|-82.93166666666667|
|    [-82.93, -82.93]|            -82.93|
+--------------------+------------------+

See also Spark Scala row-wise average by handling null