Pyspark read and combine many parquet files efficiently

I have ~ 4000 parquet files that are each 3mb. I would like to read all of the files from an S3 bucket, do some aggregations, combine the files into one dataframe, and do some more aggregations. The parquet dataframes all have the same schema. The files are not all in the same folder in the S3 bucket but rather are spread across 5 different folders. I need some files in each folder but not all. I am working on my local machine which has 16gb RAM and 8 processors that I hope to leverage to make this faster. Here is my code:

ts_dfs = []
# For loop to read parquet files and append to empty list
for id in ids:
    # Make the path to the timeseries data
    timeseries_path = f'{dataset_path}/timeseries_individual_buildings/by_county/upgrade=0/county={id[0]}'
    ts_data_df = spark.read.parquet(timeseries_path).select('bldg_id', 
                                                            '`out.natural_gas.heating.energy_consumption_intensity`', 
                                                            '`out.electricity.heating.energy_consumption_intensity`',
                                                            'timestamp')
    # Aggregate
    ts_data_df = ts_data_df \
    .groupBy(f.month('timestamp').alias('month'),'bldg_id') \
    .agg(f.sum('`out.electricity.heating.energy_consumption_intensity`').alias('eui_elec'),
         f.sum('`out.natural_gas.heating.energy_consumption_intensity`').alias('eui_gas'))
    # Append
    ts_dfs.append(ts_data_df)

# Combine all of the dfs into one
ts_sdf = reduce(DataFrame.unionAll, ts_dfs)

# Merge with ids_df
ts = ts_sdf.join(ids_df, on = ['bldg_id'])

# Mean and Standard Deviation by month 
stats_df = ts_sdf.groupBy('month', '`in.hvac_heating_type_and_fuel`') \
.agg(f.mean('eui_elec').alias('mean_eui_elec'), 
     f.stddev('eui_elec').alias('std_eui_elec'), 
     f.mean('eui_gas'). alias('mean_eui_gas'), 
     f.stddev('eui_gas').alias('std_eui_gas'))

Reading all of the files through a forloop does not leverage the multiple cores, defeating the purpose of using Spark. As such this process takes 90 minutes on my own (though that may be more a function of my internet connection). Additionally, the next step: ts_sdf = reduce(DataFrame.unionAll, ts_dfs) which combines the dataframes using unionAll is taking even longer. I think my questions are:

1. How do I avoid having to read the files in a forloop?
2. Why is the unionAll taking so long? Does PySpark not parallelize this process somehow?

In general, how do I do this better? Perhaps PySpark is not the ideal tool for this?