Wide dataframe operation in Pyspark too slow

I'm a Spark newbie and am trying to use pyspark (Spark 2.2) to perform filter and aggregation operations on a very wide feature set (~13 million rows, 15,000 columns). The feature set is stored as parquet files on an S3 drive. I am running a test script to load the feature set in a dataframe, select a few thousand records, groupby a particular region code and average each of the 15k feature cols. The problem is that the job either errors out or is taking way too long (approx 8 hours for a sample of 5% records).

Are there any ways of speeding up these kinds of operations on a wide dataframe in Pyspark? I'm using Jupyter notebooks and would like these queries to complete in minutes instead of hours.

Here's my code

df_feature_store = spark.read.parquet(PATH_FEATURE_STORE).sample(False, 0.05, seed=0).cache()
    logger.info("Initial data set loaded and sampled")

    df_selected_rors = spark.read.csv(PATH_DATA_SOURCE+"ROR Sample.csv", header=True)
    agg_cols = [x for x in df_feature_store.columns if re.search("^G\d{2}",x)]
    agg_cols = agg_cols[:10]  # just testing with fewer columns
    expr = {x:"mean" for x in agg_cols}
    joineddf = df_feature_store.join(df_selected_rors, df_feature_store.ROLLOUTREGION_IDENTIFIER == df_selected_rors.ROR, "inner")
    aggdf = joineddf.groupby("ROLLOUT_REGION_IDENTIFIER").agg(expr)
    # replace groupby
    # loop for a 1000 column aggregations 
    # transpose columns into rows as arrays
    aggdf.write.mode("overwrite").csv(PATH_FEATURE_STORE + "aggregated", header=True)
    logger.info("Done")`