# カスタムビジュアルスクリプトの例
<a name="custom-visual-transform-example-scripts"></a>

 以下の例で行われる変換はどれも等価です。ただし、2 番目の例 (SparkSQL) が最もクリーンで効率的です。その次が Pandas UDF で、最後は最初の例の低レベルのマッピングです。次の例は、2 つの列を合計する単純な変換の完全な例です。

```
from awsglue import DynamicFrame
 
# You can have other auxiliary variables, functions or classes on this file, it won't affect the runtime
def record_sum(rec, col1, col2, resultCol):
    rec[resultCol] = rec[col1] + rec[col2]
    return rec
 
 
# The number and name of arguments must match the definition on json config file
# (expect self which is the current DynamicFrame to transform
# If an argument is optional, you need to define a default value here
#  (resultCol in this example is an optional argument)
def custom_add_columns(self, col1, col2, resultCol="result"):
    # The mapping will alter the columns order, which could be important
    fields = [field.name for field in self.schema()]
    if resultCol not in fields:
        # If it's a new column put it at the end
        fields.append(resultCol)
    return self.map(lambda record: record_sum(record, col1, col2, resultCol)).select_fields(paths=fields)
 
 
# The name we assign on DynamicFrame must match the configured "functionName"
DynamicFrame.custom_add_columns = custom_add_columns
```

 次の例は、SparkSQL API を利用した同等の変換です。

```
from awsglue import DynamicFrame
 
# The number and name of arguments must match the definition on json config file
# (expect self which is the current DynamicFrame to transform
# If an argument is optional, you need to define a default value here
#  (resultCol in this example is an optional argument)
def custom_add_columns(self, col1, col2, resultCol="result"):
    df = self.toDF()
    return DynamicFrame.fromDF(
        df.withColumn(resultCol, df[col1] + df[col2]) # This is the conversion logic
        , self.glue_ctx, self.name) 
 
 
# The name we assign on DynamicFrame must match the configured "functionName"
DynamicFrame.custom_add_columns = custom_add_columns
```

 次の例では同じ変換を使用していますが、Pandas UDF を使用しているため、プレーン UDF を使用するよりも効率的です。Pandas UDF を書く方法の詳細については、[Apache Spark SQL のドキュメント](https://spark.apache.org/docs/3.1.1/api/python/reference/api/pyspark.sql.functions.pandas_udf.html)を参照してください。

```
from awsglue import DynamicFrame
import pandas as pd
from pyspark.sql.functions import pandas_udf
 
# The number and name of arguments must match the definition on json config file
# (expect self which is the current DynamicFrame to transform
# If an argument is optional, you need to define a default value here
#  (resultCol in this example is an optional argument)
def custom_add_columns(self, col1, col2, resultCol="result"):
    @pandas_udf("integer")  # We need to declare the type of the result column
    def add_columns(value1: pd.Series, value2: pd.Series) → pd.Series:
        return value1 + value2
 
    df = self.toDF()
    return DynamicFrame.fromDF(
        df.withColumn(resultCol, add_columns(col1, col2)) # This is the conversion logic
        , self.glue_ctx, self.name) 
 
# The name we assign on DynamicFrame must match the configured "functionName"
DynamicFrame.custom_add_columns = custom_add_columns
```