# Code example: Data preparation using ResolveChoice, Lambda, and ApplyMapping
The dataset that is used in this example consists of Medicare Provider payment data that was downloaded from two [Data.CMS.gov](https://data.cms.gov) data sets: "Inpatient Prospective Payment System Provider Summary for the Top 100 Diagnosis-Related Groups - FY2011" and "Inpatient Charge Data FY 2011". After downloading the data, we modified the dataset to introduce a couple of erroneous records at the end of the file. This modified file is located in a public Amazon S3 bucket at `s3://awsglue-datasets/examples/medicare/Medicare_Hospital_Provider.csv`.
You can find the source code for this example in the `data_cleaning_and_lambda.py` file in the [AWS Glue examples](https://github.com/awslabs/aws-glue-samples) GitHub repository.
The preferred way to debug Python or PySpark scripts while running on AWS is to use [Notebooks on AWS Glue Studio](https://docs.aws.amazon.com/glue/latest/ug/notebooks-chapter.html).
## Step 1: Crawl the data in the Amazon S3 bucket
1. Sign in to the AWS Management Console and open the AWS Glue console at [https://console.aws.amazon.com/glue/](https://console.aws.amazon.com/glue/).
1. Following the process described in [Configuring a crawler](define-crawler.md), create a new crawler that can crawl the `s3://awsglue-datasets/examples/medicare/Medicare_Hospital_Provider.csv` file, and can place the resulting metadata into a database named `payments` in the AWS Glue Data Catalog.
1. Run the new crawler, and then check the `payments` database. You should find that the crawler has created a metadata table named `medicare` in the database after reading the beginning of the file to determine its format and delimiter.
The schema of the new `medicare` table is as follows:
```
Column name Data type
==================================================
drg definition string
provider id bigint
provider name string
provider street address string
provider city string
provider state string
provider zip code bigint
hospital referral region description string
total discharges bigint
average covered charges string
average total payments string
average medicare payments string
```
## Step 2: Add boilerplate script to the development endpoint notebook
Paste the following boilerplate script into the development endpoint notebook to import the AWS Glue libraries that you need, and set up a single `GlueContext`:
```
import sys
from awsglue.transforms import *
from awsglue.utils import getResolvedOptions
from pyspark.context import SparkContext
from awsglue.context import GlueContext
from awsglue.job import Job
glueContext = GlueContext(SparkContext.getOrCreate())
```
## Step 3: Compare different schema parsings
Next, you can see if the schema that was recognized by an Apache Spark `DataFrame` is the same as the one that your AWS Glue crawler recorded. Run this code:
```
medicare = spark.read.format(
"com.databricks.spark.csv").option(
"header", "true").option(
"inferSchema", "true").load(
's3://awsglue-datasets/examples/medicare/Medicare_Hospital_Provider.csv')
medicare.printSchema()
```
Here's the output from the `printSchema` call:
```
root
|-- DRG Definition: string (nullable = true)
|-- Provider Id: string (nullable = true)
|-- Provider Name: string (nullable = true)
|-- Provider Street Address: string (nullable = true)
|-- Provider City: string (nullable = true)
|-- Provider State: string (nullable = true)
|-- Provider Zip Code: integer (nullable = true)
|-- Hospital Referral Region Description: string (nullable = true)
|-- Total Discharges : integer (nullable = true)
|-- Average Covered Charges : string (nullable = true)
|-- Average Total Payments : string (nullable = true)
|-- Average Medicare Payments: string (nullable = true)
```
Next, look at the schema that an AWS Glue `DynamicFrame` generates:
```
medicare_dynamicframe = glueContext.create_dynamic_frame.from_catalog(
database = "payments",
table_name = "medicare")
medicare_dynamicframe.printSchema()
```
The output from `printSchema` is as follows:
```
root
|-- drg definition: string
|-- provider id: choice
| |-- long
| |-- string
|-- provider name: string
|-- provider street address: string
|-- provider city: string
|-- provider state: string
|-- provider zip code: long
|-- hospital referral region description: string
|-- total discharges: long
|-- average covered charges: string
|-- average total payments: string
|-- average medicare payments: string
```
The `DynamicFrame` generates a schema in which `provider id` could be either a `long` or a `string` type. The `DataFrame` schema lists `Provider Id` as being a `string` type, and the Data Catalog lists `provider id` as being a `bigint` type.
Which one is correct? There are two records at the end of the file (out of 160,000 records) with `string` values in that column. These are the erroneous records that were introduced to illustrate a problem.
To address this kind of problem, the AWS Glue `DynamicFrame` introduces the concept of a *choice* type. In this case, the `DynamicFrame` shows that both `long` and `string` values can appear in that column. The AWS Glue crawler missed the `string` values because it considered only a 2 MB prefix of the data. The Apache Spark `DataFrame` considered the whole dataset, but it was forced to assign the most general type to the column, namely `string`. In fact, Spark often resorts to the most general case when there are complex types or variations with which it is unfamiliar.
To query the `provider id` column, resolve the choice type first. You can use the `resolveChoice` transform method in your `DynamicFrame` to convert those `string` values to `long` values with a `cast:long` option:
```
medicare_res = medicare_dynamicframe.resolveChoice(specs = [('provider id','cast:long')])
medicare_res.printSchema()
```
The `printSchema` output is now:
```
root
|-- drg definition: string
|-- provider id: long
|-- provider name: string
|-- provider street address: string
|-- provider city: string
|-- provider state: string
|-- provider zip code: long
|-- hospital referral region description: string
|-- total discharges: long
|-- average covered charges: string
|-- average total payments: string
|-- average medicare payments: string
```
Where the value was a `string` that could not be cast, AWS Glue inserted a `null`.
Another option is to convert the choice type to a `struct`, which keeps values of both types.
Next, look at the rows that were anomalous:
```
medicare_res.toDF().where("'provider id' is NULL").show()
```
You see the following:
```
+--------------------+-----------+---------------+-----------------------+-------------+--------------+-----------------+------------------------------------+----------------+-----------------------+----------------------+-------------------------+
| drg definition|provider id| provider name|provider street address|provider city|provider state|provider zip code|hospital referral region description|total discharges|average covered charges|average total payments|average medicare payments|
+--------------------+-----------+---------------+-----------------------+-------------+--------------+-----------------+------------------------------------+----------------+-----------------------+----------------------+-------------------------+
|948 - SIGNS & SYM...| null| INC| 1050 DIVISION ST| MAUSTON| WI| 53948| WI - Madison| 12| $11961.41| $4619.00| $3775.33|
|948 - SIGNS & SYM...| null| INC- ST JOSEPH| 5000 W CHAMBERS ST| MILWAUKEE| WI| 53210| WI - Milwaukee| 14| $10514.28| $5562.50| $4522.78|
+--------------------+-----------+---------------+-----------------------+-------------+--------------+-----------------+------------------------------------+----------------+-----------------------+----------------------+-------------------------+
```
Now remove the two malformed records, as follows:
```
medicare_dataframe = medicare_res.toDF()
medicare_dataframe = medicare_dataframe.where("'provider id' is NOT NULL")
```
## Step 4: Map the data and use Apache Spark Lambda functions
AWS Glue does not yet directly support Lambda functions, also known as user-defined functions. But you can always convert a `DynamicFrame` to and from an Apache Spark `DataFrame` to take advantage of Spark functionality in addition to the special features of `DynamicFrames`.
Next, turn the payment information into numbers, so analytic engines like Amazon Redshift or Amazon Athena can do their number crunching faster:
```
from pyspark.sql.functions import udf
from pyspark.sql.types import StringType
chop_f = udf(lambda x: x[1:], StringType())
medicare_dataframe = medicare_dataframe.withColumn(
"ACC", chop_f(
medicare_dataframe["average covered charges"])).withColumn(
"ATP", chop_f(
medicare_dataframe["average total payments"])).withColumn(
"AMP", chop_f(
medicare_dataframe["average medicare payments"]))
medicare_dataframe.select(['ACC', 'ATP', 'AMP']).show()
```
The output from the `show` call is as follows:
```
+--------+-------+-------+
| ACC| ATP| AMP|
+--------+-------+-------+
|32963.07|5777.24|4763.73|
|15131.85|5787.57|4976.71|
|37560.37|5434.95|4453.79|
|13998.28|5417.56|4129.16|
|31633.27|5658.33|4851.44|
|16920.79|6653.80|5374.14|
|11977.13|5834.74|4761.41|
|35841.09|8031.12|5858.50|
|28523.39|6113.38|5228.40|
|75233.38|5541.05|4386.94|
|67327.92|5461.57|4493.57|
|39607.28|5356.28|4408.20|
|22862.23|5374.65|4186.02|
|31110.85|5366.23|4376.23|
|25411.33|5282.93|4383.73|
| 9234.51|5676.55|4509.11|
|15895.85|5930.11|3972.85|
|19721.16|6192.54|5179.38|
|10710.88|4968.00|3898.88|
|51343.75|5996.00|4962.45|
+--------+-------+-------+
only showing top 20 rows
```
These are all still strings in the data. We can use the powerful `apply_mapping` transform method to drop, rename, cast, and nest the data so that other data programming languages and systems can easily access it:
```
from awsglue.dynamicframe import DynamicFrame
medicare_tmp_dyf = DynamicFrame.fromDF(medicare_dataframe, glueContext, "nested")
medicare_nest_dyf = medicare_tmp_dyf.apply_mapping([('drg definition', 'string', 'drg', 'string'),
('provider id', 'long', 'provider.id', 'long'),
('provider name', 'string', 'provider.name', 'string'),
('provider city', 'string', 'provider.city', 'string'),
('provider state', 'string', 'provider.state', 'string'),
('provider zip code', 'long', 'provider.zip', 'long'),
('hospital referral region description', 'string','rr', 'string'),
('ACC', 'string', 'charges.covered', 'double'),
('ATP', 'string', 'charges.total_pay', 'double'),
('AMP', 'string', 'charges.medicare_pay', 'double')])
medicare_nest_dyf.printSchema()
```
The `printSchema` output is as follows:
```
root
|-- drg: string
|-- provider: struct
| |-- id: long
| |-- name: string
| |-- city: string
| |-- state: string
| |-- zip: long
|-- rr: string
|-- charges: struct
| |-- covered: double
| |-- total_pay: double
| |-- medicare_pay: double
```
Turning the data back into a Spark `DataFrame`, you can show what it looks like now:
```
medicare_nest_dyf.toDF().show()
```
The output is as follows:
```
+--------------------+--------------------+---------------+--------------------+
| drg| provider| rr| charges|
+--------------------+--------------------+---------------+--------------------+
|039 - EXTRACRANIA...|[10001,SOUTHEAST ...| AL - Dothan|[32963.07,5777.24...|
|039 - EXTRACRANIA...|[10005,MARSHALL M...|AL - Birmingham|[15131.85,5787.57...|
|039 - EXTRACRANIA...|[10006,ELIZA COFF...|AL - Birmingham|[37560.37,5434.95...|
|039 - EXTRACRANIA...|[10011,ST VINCENT...|AL - Birmingham|[13998.28,5417.56...|
|039 - EXTRACRANIA...|[10016,SHELBY BAP...|AL - Birmingham|[31633.27,5658.33...|
|039 - EXTRACRANIA...|[10023,BAPTIST ME...|AL - Montgomery|[16920.79,6653.8,...|
|039 - EXTRACRANIA...|[10029,EAST ALABA...|AL - Birmingham|[11977.13,5834.74...|
|039 - EXTRACRANIA...|[10033,UNIVERSITY...|AL - Birmingham|[35841.09,8031.12...|
|039 - EXTRACRANIA...|[10039,HUNTSVILLE...|AL - Huntsville|[28523.39,6113.38...|
|039 - EXTRACRANIA...|[10040,GADSDEN RE...|AL - Birmingham|[75233.38,5541.05...|
|039 - EXTRACRANIA...|[10046,RIVERVIEW ...|AL - Birmingham|[67327.92,5461.57...|
|039 - EXTRACRANIA...|[10055,FLOWERS HO...| AL - Dothan|[39607.28,5356.28...|
|039 - EXTRACRANIA...|[10056,ST VINCENT...|AL - Birmingham|[22862.23,5374.65...|
|039 - EXTRACRANIA...|[10078,NORTHEAST ...|AL - Birmingham|[31110.85,5366.23...|
|039 - EXTRACRANIA...|[10083,SOUTH BALD...| AL - Mobile|[25411.33,5282.93...|
|039 - EXTRACRANIA...|[10085,DECATUR GE...|AL - Huntsville|[9234.51,5676.55,...|
|039 - EXTRACRANIA...|[10090,PROVIDENCE...| AL - Mobile|[15895.85,5930.11...|
|039 - EXTRACRANIA...|[10092,D C H REGI...|AL - Tuscaloosa|[19721.16,6192.54...|
|039 - EXTRACRANIA...|[10100,THOMAS HOS...| AL - Mobile|[10710.88,4968.0,...|
|039 - EXTRACRANIA...|[10103,BAPTIST ME...|AL - Birmingham|[51343.75,5996.0,...|
+--------------------+--------------------+---------------+--------------------+
only showing top 20 rows
```
## Step 5: Write the data to Apache Parquet
AWS Glue makes it easy to write the data in a format such as Apache Parquet that relational databases can effectively consume:
```
glueContext.write_dynamic_frame.from_options(
frame = medicare_nest_dyf,
connection_type = "s3",
connection_options = {"path": "s3://glue-sample-target/output-dir/medicare_parquet"},
format = "parquet")
```