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Convert Rows to Columns in PYSPARK

Time:11-20

I am working a project that requires data to be transposed. In the past, I had done it using SAS and SQL which used to be super fast. I used the expr function with Stack as outlined below (code section).

The problem I am facing is 2 fold.

  1. The input data is about 200 GB (500 Million rows vs 70 columns) and stored as parquet files.
  2. The step that transposes (df2) runs for about 4-5 hours and terminates. I had changed the time out settings and played around with the Spark session settings but no luck so far.

What I did so far: The data is stored as parquet files in Azure Synapse Workspace. Firstly, I had assigned a ROWNUMBER to each row in the data frame. Then I have split the data into two data frames.

  1. df1 has ROWNUMBER and all the necessary columns (minus 25 diagnosis columns)
  2. df2 has ROWNUMBER as the 25 Diagnosis columns.
  3. I then tried to create df3 by joining df1 and df2 on ROWNUMBER.

Step 2 is a killer, I mean I was not able to get past this step as the session terminates after 4 hours.

I tried with SPARK SQL as well, but no luck there was well. Further, I was advised not to use SQL in SPARK as it will deteriorate the performance.

I am also thinking of doing the transpose outside of PYSPARK (not sure how and if it is even advisable to do so).

Code I wrote so far:

import sys
import pyspark.sql as t
import pyspark.sql.functions as f
from pyspark.sql.types import *

df_raw=spark.read.parquet("abfss:path/med_claims/*.parquet")
df_rn=df_raw.withColumn("ROWNUM", f.row_number().over(t.Window.orderBy(df_raw.MEMBER_ID, df_raw.SERVICE_FROM_DATE, df_raw.SERVICE_THRU_DATE)))

df1=df_rn.select(
                 df_rn.ROWNUM,
                 df_rn.MEMBER_ID,
                 df_rn.MEMBER_ID_DEPENDENT,
                 df_rn.SERVICE_FROM_DATE,
                 df_rn.SERVICE_THRU_DATE,
                 df_rn.SERVICE_PROCEDURE_CODE
                )

df2=df_rn.select(df_rn.ROWNUM,
             f.expr("stack(25, code1, code2, code3, code4, code5, \
                             code6, code7, code8, code9, code10, \
                             code11, code12, code13, code14, code15, \
                             code16, code17, code18, code19, code20, \
                             code21, code22, code23, code24, code25) as (TRANPOSED_DIAG)")) \
             .dropDuplicates() \
             .where(" (TRANPOSED_DIAG IS NOT NULL) OR (TRIM(TRANPOSED_DIAG) <> '') ")

df3=df1.join(df2, df1.ROWNUM == df2.ROWNUM, 'left') \
       .select(df1.ROWNUM,
             df1.MEMBER_ID,
             df1.MEMBER_ID_DEPENDENT,
             df1.SERVICE_FROM_DATE,
             df1.SERVICE_THRU_DATE,
             df1.SERVICE_PROCEDURE_CODE,
             df2.TRANPOSED_DIAG
            )

Input Data:

MEMBER_ID MEMBER_ID_DEPENDENT PROVIDER_KEY REVENUE_KEY PLACE_OF_SERVICE_KEY SERVICE_FROM_DATE SERVICE_THRU_DATE SERVICE_PROCEDURE_CODE CODE1 CODE2 CODE3 CODE4 CODE5 CODE6 CODE7 CODE8 CODE9 CODE10 CODE11 CODE12 CODE13 CODE14 CODE15 CODE16 CODE17 CODE18 CODE19 CODE20 CODE21 CODE22 CODE23 CODE24 CODE25
A1 A11 AB05547 4.85148E 12 7.96651E 11 9/23/2019 0:00 9/23/2019 0:00 89240 Z0000 M25852 M25851 Z0000 M25551 null null null null null null null null null null null null null null null null null null null null
A1 A11 AB92685 4.85148E 12 7.96651E 11 10/23/2020 0:00 10/23/2020 0:00 89240 Z524 Z524 null null null null null null null null null null null null null null null null null null null null null null null
A2 A12 AB64081 4.8515E 12 7.96651E 11 6/19/2020 0:00 6/19/2020 0:00 76499 Z9884 R109 K219 K449 Z9884 null null null null null null null null null null null null null null null null null null null
A3 A13 AB64081 4.8515E 12 7.96651E 11 9/13/2019 0:00 9/13/2019 0:00 76499 Z1231 Z1231 null null null null null null null null null null null null null null null null null null null null null null null
A4 A14 AB74417 4.8515E 12 7.96651E 11 9/30/2019 0:00 9/30/2019 0:00 76499 N210 N400 E782 E119 I10 Z87891 N210 null null null null null null null null null null null null null null null null null null

Expected Output:

MEMBER_ID MEMBER_ID_DEPENDENT PROVIDER_KEY REVENUE_KEY PLACE_OF_SERVICE_KEY SERVICE_FROM_DATE SERVICE_THRU_DATE SERVICE_PROCEDURE_CODE TRANSPOSED_DIAGNOSIS
A1 A11 AB05547 4851484842551 796650504854 9/23/2019 0:00 9/23/2019 0:00 89240 Z0000
A1 A11 AB05548 4851484842551 796650504854 9/23/2019 0:00 9/23/2019 0:00 89241 M25852
A1 A11 AB05549 4851484842551 796650504854 9/23/2019 0:00 9/23/2019 0:00 89242 M25851
A1 A11 AB05550 4851484842551 796650504854 9/23/2019 0:00 9/23/2019 0:00 89243 M25551
A1 A11 AB92685 4851484842551 796650504854 10/23/2020 0:00 10/23/2020 0:00 89240 Z524
A2 A12 AB64081 4851504842551 796650504854 6/19/2020 0:00 6/19/2020 0:00 76499 Z9884
A2 A12 AB64082 4851504842551 796650504854 6/19/2020 0:00 6/19/2020 0:00 76500 R109
A2 A12 AB64083 4851504842551 796650504854 6/19/2020 0:00 6/19/2020 0:00 76501 K219
A2 A12 AB64084 4851504842551 796650504854 6/19/2020 0:00 6/19/2020 0:00 76502 K449
A3 A13 AB64081 4851504842551 796650504854 9/13/2019 0:00 9/13/2019 0:00 76499 Z1231
A4 A14 AB74417 4851504842551 796650504854 9/30/2019 0:00 9/30/2019 0:00 76499 N210
A4 A14 AB74418 4851504842551 796650504854 9/30/2019 0:00 9/30/2019 0:00 76500 N400
A4 A14 AB74419 4851504842551 796650504854 9/30/2019 0:00 9/30/2019 0:00 76501 E782
A4 A14 AB74420 4851504842551 796650504854 9/30/2019 0:00 9/30/2019 0:00 76502 E119
A4 A14 AB74421 4851504842551 796650504854 9/30/2019 0:00 9/30/2019 0:00 76503 I10
A4 A14 AB74422 4851504842551 796650504854 9/30/2019 0:00 9/30/2019 0:00 76504 Z87891

CodePudding user response:

Merge columns, explode it after filtering the null values.

codes = list(filter(lambda c: c.startswith('CODE'), df.columns))

df.withColumn('TRANSPOSED_DIAGNOSIS', f.array(*map(lambda c: f.col(c), codes))) \
  .drop(*codes) \
  .withColumn('TRANSPOSED_DIAGNOSIS', f.expr('filter(TRANSPOSED_DIAGNOSIS, x -> x is not null)')) \
  .withColumn('TRANSPOSED_DIAGNOSIS', f.explode('TRANSPOSED_DIAGNOSIS')) \
  .show(30, truncate=False)

 --------- ------------------- ------------ ----------- -------------------- ----------------- ----------------- ---------------------- -------------------- 
|MEMBER_ID|MEMBER_ID_DEPENDENT|PROVIDER_KEY|REVENUE_KEY|PLACE_OF_SERVICE_KEY|SERVICE_FROM_DATE|SERVICE_THRU_DATE|SERVICE_PROCEDURE_CODE|TRANSPOSED_DIAGNOSIS|
 --------- ------------------- ------------ ----------- -------------------- ----------------- ----------------- ---------------------- -------------------- 
|A1       |A11                |AB05547     |4.85148E 12|7.96651E 11         |9/23/2019 0:00   |9/23/2019 0:00   |89240                 |Z0000               |
|A1       |A11                |AB05547     |4.85148E 12|7.96651E 11         |9/23/2019 0:00   |9/23/2019 0:00   |89240                 |M25852              |
|A1       |A11                |AB05547     |4.85148E 12|7.96651E 11         |9/23/2019 0:00   |9/23/2019 0:00   |89240                 |M25851              |
|A1       |A11                |AB05547     |4.85148E 12|7.96651E 11         |9/23/2019 0:00   |9/23/2019 0:00   |89240                 |Z0000               |
|A1       |A11                |AB05547     |4.85148E 12|7.96651E 11         |9/23/2019 0:00   |9/23/2019 0:00   |89240                 |M25551              |
|A1       |A11                |AB92685     |4.85148E 12|7.96651E 11         |10/23/2020 0:00  |10/23/2020 0:00  |89240                 |Z524                |
|A1       |A11                |AB92685     |4.85148E 12|7.96651E 11         |10/23/2020 0:00  |10/23/2020 0:00  |89240                 |Z524                |
|A2       |A12                |AB64081     |4.8515E 12 |7.96651E 11         |6/19/2020 0:00   |6/19/2020 0:00   |76499                 |Z9884               |
|A2       |A12                |AB64081     |4.8515E 12 |7.96651E 11         |6/19/2020 0:00   |6/19/2020 0:00   |76499                 |R109                |
|A2       |A12                |AB64081     |4.8515E 12 |7.96651E 11         |6/19/2020 0:00   |6/19/2020 0:00   |76499                 |K219                |
|A2       |A12                |AB64081     |4.8515E 12 |7.96651E 11         |6/19/2020 0:00   |6/19/2020 0:00   |76499                 |K449                |
|A2       |A12                |AB64081     |4.8515E 12 |7.96651E 11         |6/19/2020 0:00   |6/19/2020 0:00   |76499                 |Z9884               |
|A3       |A13                |AB64081     |4.8515E 12 |7.96651E 11         |9/13/2019 0:00   |9/13/2019 0:00   |76499                 |Z1231               |
|A3       |A13                |AB64081     |4.8515E 12 |7.96651E 11         |9/13/2019 0:00   |9/13/2019 0:00   |76499                 |Z1231               |
|A4       |A14                |AB74417     |4.8515E 12 |7.96651E 11         |9/30/2019 0:00   |9/30/2019 0:00   |76499                 |N210                |
|A4       |A14                |AB74417     |4.8515E 12 |7.96651E 11         |9/30/2019 0:00   |9/30/2019 0:00   |76499                 |N400                |
|A4       |A14                |AB74417     |4.8515E 12 |7.96651E 11         |9/30/2019 0:00   |9/30/2019 0:00   |76499                 |E782                |
|A4       |A14                |AB74417     |4.8515E 12 |7.96651E 11         |9/30/2019 0:00   |9/30/2019 0:00   |76499                 |E119                |
|A4       |A14                |AB74417     |4.8515E 12 |7.96651E 11         |9/30/2019 0:00   |9/30/2019 0:00   |76499                 |I10                 |
|A4       |A14                |AB74417     |4.8515E 12 |7.96651E 11         |9/30/2019 0:00   |9/30/2019 0:00   |76499                 |Z87891              |
|A4       |A14                |AB74417     |4.8515E 12 |7.96651E 11         |9/30/2019 0:00   |9/30/2019 0:00   |76499                 |N210                |
 --------- ------------------- ------------ ----------- -------------------- ----------------- ----------------- ---------------------- -------------------- 

CodePudding user response:

This will be an expensive operation in any approach, however you may consider the following approaches which avoids using another expensive join.

For simplification and code re-use I've filtered out the desired and code related columns into different variables instead of hardcoding them.

Approach 1 : Recommended

Continuing from df_raw's first load, you may try the following:

from pyspark.sql import functions as F
from pyspark.sql import Window

# extract service procedure code columns from `df_raw` by looking for the simple pattern 'CODE'. 
# This filter can be easily modified for more complex code columns names
service_procedure_cols = [col for col in df_raw.columns if 'CODE' in col and 'SERVICE' not in col]

# extract the desired column names in the dataframe
desired_cols = [col for col in df_raw.columns if 'CODE' not in col or 'SERVICE' in col]
#build the stack expresssion by counting the number of columns with `len` and concatenating the column names
code_column_stack_expression = "stack(" str(len(service_procedure_cols)) ", " ",".join(service_procedure_cols) ") as (TRANSPOSED_DIAGNOSIS)"

df_step_1 = (
    # select the desired column names and unpivot the data
    df_raw.select(desired_cols   [ F.expr(code_column_stack_expression)])
    # filter or remove null and empty columns
          .where(F.col("TRANSPOSED_DIAGNOSIS").isNotNull() & (F.trim("TRANSPOSED_DIAGNOSIS") != '' ))
    # remove duplicates
          .dropDuplicates()
)

df_step_1.show(truncate=False)

Outputs:

 --------- ------------------- ------------ ----------- -------------------- ----------------- ----------------- ---------------------- -------------------- 
|MEMBER_ID|MEMBER_ID_DEPENDENT|PROVIDER_KEY|REVENUE_KEY|PLACE_OF_SERVICE_KEY|SERVICE_FROM_DATE|SERVICE_THRU_DATE|SERVICE_PROCEDURE_CODE|TRANSPOSED_DIAGNOSIS|
 --------- ------------------- ------------ ----------- -------------------- ----------------- ----------------- ---------------------- -------------------- 
|A1       |A11                |AB05547     |4.85148E 12|7.96651E 11         |9/23/2019 0:00   |9/23/2019 0:00   |89240                 |Z0000               |
|A1       |A11                |AB05547     |4.85148E 12|7.96651E 11         |9/23/2019 0:00   |9/23/2019 0:00   |89240                 |M25852              |
|A1       |A11                |AB05547     |4.85148E 12|7.96651E 11         |9/23/2019 0:00   |9/23/2019 0:00   |89240                 |M25851              |
|A1       |A11                |AB05547     |4.85148E 12|7.96651E 11         |9/23/2019 0:00   |9/23/2019 0:00   |89240                 |Z0000               |
|A1       |A11                |AB05547     |4.85148E 12|7.96651E 11         |9/23/2019 0:00   |9/23/2019 0:00   |89240                 |M25551              |
|A1       |A11                |AB92685     |4.85148E 12|7.96651E 11         |10/23/2020 0:00  |10/23/2020 0:00  |89240                 |Z524                |
|A1       |A11                |AB92685     |4.85148E 12|7.96651E 11         |10/23/2020 0:00  |10/23/2020 0:00  |89240                 |Z524                |
|A2       |A12                |AB64081     |4.8515E 12 |7.96651E 11         |6/19/2020 0:00   |6/19/2020 0:00   |76499                 |Z9884               |
|A2       |A12                |AB64081     |4.8515E 12 |7.96651E 11         |6/19/2020 0:00   |6/19/2020 0:00   |76499                 |R109                |
|A2       |A12                |AB64081     |4.8515E 12 |7.96651E 11         |6/19/2020 0:00   |6/19/2020 0:00   |76499                 |K219                |
|A2       |A12                |AB64081     |4.8515E 12 |7.96651E 11         |6/19/2020 0:00   |6/19/2020 0:00   |76499                 |K449      
df_step_2 = (
    # Replace the existing `SERVICE_PROCEDURE_CODE` column with the new service procedure column by casting it as an integer and adding the generated row number partitioned by your desired columns and ordered by the columns you specified in your example
    df_step_1.withColumn(
        "SERVICE_PROCEDURE_CODE",
        F.col("SERVICE_PROCEDURE_CODE").cast("INT") F.row_number().over(
            Window.partitionBy(desired_cols).orderBy(["MEMBER_ID", "SERVICE_FROM_DATE", "SERVICE_THRU_DATE"]) -1
        )
    )
)
df_step_2.show(truncate=False)

Outputs:

 --------- ------------------- ------------ ----------- -------------------- ----------------- ----------------- ---------------------- -------------------- 
|MEMBER_ID|MEMBER_ID_DEPENDENT|PROVIDER_KEY|REVENUE_KEY|PLACE_OF_SERVICE_KEY|SERVICE_FROM_DATE|SERVICE_THRU_DATE|SERVICE_PROCEDURE_CODE|TRANSPOSED_DIAGNOSIS|
 --------- ------------------- ------------ ----------- -------------------- ----------------- ----------------- ---------------------- -------------------- 
|A1       |A11                |AB05547     |4.85148E 12|7.96651E 11         |9/23/2019 0:00   |9/23/2019 0:00   |89240                 |Z0000               |
|A1       |A11                |AB05547     |4.85148E 12|7.96651E 11         |9/23/2019 0:00   |9/23/2019 0:00   |89241                 |M25852              |
|A1       |A11                |AB05547     |4.85148E 12|7.96651E 11         |9/23/2019 0:00   |9/23/2019 0:00   |89242                 |M25851              |
|A1       |A11                |AB05547     |4.85148E 12|7.96651E 11         |9/23/2019 0:00   |9/23/2019 0:00   |89243                 |Z0000               |
|A1       |A11                |AB05547     |4.85148E 12|7.96651E 11         |9/23/2019 0:00   |9/23/2019 0:00   |89244                 |M25551              |
|A1       |A11                |AB92685     |4.85148E 12|7.96651E 11         |10/23/2020 0:00  |10/23/2020 0:00  |89240                 |Z524                |
|A1       |A11                |AB92685     |4.85148E 12|7.96651E 11         |10/23/2020 0:00  |10/23/2020 0:00  |89241                 |Z524                |
|A2       |A12                |AB64081     |4.8515E 12 |7.96651E 11         |6/19/2020 0:00   |6/19/2020 0:00   |76499                 |Z9884               |
|A2       |A12                |AB64081     |4.8515E 12 |7.96651E 11         |6/19/2020 0:00   |6/19/2020 0:00   |76500                 |R109                |
|A2       |A12                |AB64081     |4.8515E 12 |7.96651E 11         |6/19/2020 0:00   |6/19/2020 0:00   |76501                 |K219                |
|A2       |A12                |AB64081     |4.8515E 12 |7.96651E 11         |6/19/2020 0:00   |6/19/2020 0:00   |76502                 |K449                |
|A2       |A12                |AB64081     |4.8515E 12 |7.96651E 11         |6/19/2020 0:00   |6/19/2020 0:00   |76503                 |Z9884               |
|A3       |A13                |AB64081     |4.8515E 12 |7.96651E 11         |9/13/2019 0:00   |9/13/2019 0:00   |76499                 |Z1231               |
|A3       |A13                |AB64081     |4.8515E 12 |7.96651E 11         |9/13/2019 0:00   |9/13/2019 0:00   |76500                 |Z1231               |
|A4       |A14                |AB74417     |4.8515E 12 |7.96651E 11         |9/30/2019 0:00   |9/30/2019 0:00   |76499                 |N210                |
|A4       |A14                |AB74417     |4.8515E 12 |7.96651E 11         |9/30/2019 0:00   |9/30/2019 0:00   |76500                 |N400                |
|A4       |A14                |AB74417     |4.8515E 12 |7.96651E 11         |9/30/2019 0:00   |9/30/2019 0:00   |76501                 |E782                |
|A4       |A14                |AB74417     |4.8515E 12 |7.96651E 11         |9/30/2019 0:00   |9/30/2019 0:00   |76502                 |E119                |
|A4       |A14                |AB74417     |4.8515E 12 |7.96651E 11         |9/30/2019 0:00   |9/30/2019 0:00   |76503                 |I10                 |
|A4       |A14                |AB74417     |4.8515E 12 |7.96651E 11         |9/30/2019 0:00   |9/30/2019 0:00   |76504                 |Z87891              |
 --------- ------------------- ------------ ----------- -------------------- ----------------- ----------------- ---------------------- -------------------- 
only showing top 20 rows

Approach 2: Uses original code number to update service code

This approach may also be simpler to read for some as it uses a loop to build a union of the desired dataset.

NB. This may cause overlaps in your service procedure code

Continuing from df_raw's first load, you may try the following:

from pyspark.sql import functions as F
from pyspark.sql import Window

# cache the original df
df_raw.cache()

# extract service procedure code columns from `df_raw` by looking for the simple pattern 'CODE'. 
# This filter can be easily modified for more complex code columns names
service_procedure_cols = [col for col in df_raw.columns if 'CODE' in col and 'SERVICE' not in col]

# extract the desired column names in the dataframe
desired_cols = [col for col in df_raw.columns if 'CODE' not in col or 'SERVICE' in col]

# use a temp variable `df_combined` to store the final dataframe
df_combined = None
# for each of the service procedure columns
for col in service_procedure_cols:
    # extract the code number
    col_num = int(col.replace("CODE",""))
    # combined the desired columns with this code column to get all desired columns for the diagnosis
    diagnosis_desired_columns = desired_cols   [col]
    # creating a temporary df
    interim_df = (
    # select all desired columns
        df_raw.select(*diagnosis_desired_columns)
    # update the service procedure code with the extracted code number
              .withColumn(
                  "SERVICE_PROCEDURE_CODE",
                  F.col("SERVICE_PROCEDURE_CODE").cast("INT") col_num
              )
     # rename the code column
              .withColumnRenamed(col,"TRANSPOSED_DIAGNOSIS")
     # filter null and empty columns
              .where(F.col("TRANSPOSED_DIAGNOSIS").isNotNull() & (F.trim("TRANSPOSED_DIAGNOSIS") !=''))
              .dropDuplicates()
    )
    # if the initial combined df variable is empty assign it `interim_df`
    # otherwise perform a union and store the result
    if df_combined is None:
        df_combined = interim_df 
    else:
        df_combined = df_combined.union(interim_df)

# only here for debugging purposes to show the results
df_combined.orderBy(desired_cols).show(truncate=False)

Outputs:

 --------- ------------------- ------------ ----------- -------------------- ----------------- ----------------- ---------------------- -------------------- 
|MEMBER_ID|MEMBER_ID_DEPENDENT|PROVIDER_KEY|REVENUE_KEY|PLACE_OF_SERVICE_KEY|SERVICE_FROM_DATE|SERVICE_THRU_DATE|SERVICE_PROCEDURE_CODE|TRANSPOSED_DIAGNOSIS|
 --------- ------------------- ------------ ----------- -------------------- ----------------- ----------------- ---------------------- -------------------- 
|A1       |A11                |AB05547     |4.85148E 12|7.96651E 11         |9/23/2019 0:00   |9/23/2019 0:00   |89241                 |Z0000               |
|A1       |A11                |AB05547     |4.85148E 12|7.96651E 11         |9/23/2019 0:00   |9/23/2019 0:00   |89242                 |M25852              |
|A1       |A11                |AB05547     |4.85148E 12|7.96651E 11         |9/23/2019 0:00   |9/23/2019 0:00   |89243                 |M25851              |
|A1       |A11                |AB05547     |4.85148E 12|7.96651E 11         |9/23/2019 0:00   |9/23/2019 0:00   |89244                 |Z0000               |
|A1       |A11                |AB05547     |4.85148E 12|7.96651E 11         |9/23/2019 0:00   |9/23/2019 0:00   |89245                 |M25551              |
|A1       |A11                |AB92685     |4.85148E 12|7.96651E 11         |10/23/2020 0:00  |10/23/2020 0:00  |89241                 |Z524                |
|A1       |A11                |AB92685     |4.85148E 12|7.96651E 11         |10/23/2020 0:00  |10/23/2020 0:00  |89242                 |Z524                |
|A2       |A12                |AB64081     |4.8515E 12 |7.96651E 11         |6/19/2020 0:00   |6/19/2020 0:00   |76500                 |Z9884               |
|A2       |A12                |AB64081     |4.8515E 12 |7.96651E 11         |6/19/2020 0:00   |6/19/2020 0:00   |76501                 |R109                |
|A2       |A12                |AB64081     |4.8515E 12 |7.96651E 11         |6/19/2020 0:00   |6/19/2020 0:00   |76502                 |K219                |
|A2       |A12                |AB64081     |4.8515E 12 |7.96651E 11         |6/19/2020 0:00   |6/19/2020 0:00   |76503                 |K449                |
|A2       |A12                |AB64081     |4.8515E 12 |7.96651E 11         |6/19/2020 0:00   |6/19/2020 0:00   |76504                 |Z9884               |
|A3       |A13                |AB64081     |4.8515E 12 |7.96651E 11         |9/13/2019 0:00   |9/13/2019 0:00   |76500                 |Z1231               |
|A3       |A13                |AB64081     |4.8515E 12 |7.96651E 11         |9/13/2019 0:00   |9/13/2019 0:00   |76501                 |Z1231               |
|A4       |A14                |AB74417     |4.8515E 12 |7.96651E 11         |9/30/2019 0:00   |9/30/2019 0:00   |76500                 |N210                |
|A4       |A14                |AB74417     |4.8515E 12 |7.96651E 11         |9/30/2019 0:00   |9/30/2019 0:00   |76501                 |N400                |
|A4       |A14                |AB74417     |4.8515E 12 |7.96651E 11         |9/30/2019 0:00   |9/30/2019 0:00   |76502                 |E782                |
|A4       |A14                |AB74417     |4.8515E 12 |7.96651E 11         |9/30/2019 0:00   |9/30/2019 0:00   |76503                 |E119                |
|A4       |A14                |AB74417     |4.8515E 12 |7.96651E 11         |9/30/2019 0:00   |9/30/2019 0:00   |76504                 |I10                 |
|A4       |A14                |AB74417     |4.8515E 12 |7.96651E 11         |9/30/2019 0:00   |9/30/2019 0:00   |76505                 |Z87891              |
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