# MonthName 类
`MonthName` 转换会使用表示日期的字符串创建一个包含月份名称的新列。
## 示例
```
from pyspark.context import SparkContext
from pyspark.sql import SparkSession
from awsgluedi.transforms import *
sc = SparkContext()
spark = SparkSession(sc)
spark.conf.set("spark.sql.legacy.timeParserPolicy", "LEGACY")
input_df = spark.createDataFrame(
[
("20-2018-12",),
("2018-20-12",),
("20182012",),
("12202018",),
("20122018",),
("20-12-2018",),
("12/20/2018",),
("02/02/02",),
("02 02 2009",),
("02/02/2009",),
("August/02/2009",),
("02/june/2009",),
("02/2020/june",),
("2013-02-21 06:35:45.658505",),
("August 02 2009",),
("2013/02/21",),
(None,),
],
["column_1"],
)
try:
df_output = datetime_functions.MonthName.apply(
data_frame=input_df,
spark_context=sc,
source_column="column_1",
target_column="target_column"
)
df_output.show()
except:
print("Unexpected Error happened ")
raise
```
## Output
输出是：
```
```
+------------+------------+
| column_1|target_column|
+------------+------------+
|20-2018-12 | December |
|2018-20-12 | null |
| 20182012| null |
| 12202018| null |
| 20122018| null |
|20-12-2018 | December |
|12/20/2018 | December |
| 02/02/02 | February |
|02 02 2009 | February |
|02/02/2009 | February |
|August/02/2009| August |
|02/june/2009| null |
|02/2020/june| null |
|2013-02-21 06:35:45.658505| February |
|August 02 2009| August |
| 2013/02/21| February |
| null | null |
+------------+------------+
```
```
`MonthName` 转换会将“source\_column”设为“column\_1”，将“target\_column”设为“target\_column”。它会尝试从“column\_1”列中的日期/时间字符串中提取月份名称，并将其放在“target\_column”列中。如果日期/时间字符串的格式无法识别或无法解析，则“target\_column”的值将设置为“null”。
该转换可成功地从各种日期/时间格式中提取月份名称，例如“20-12-2018”、“12/20/2018”、“02/02/2009”、“2013-02-21 06:35:45.658505”和“August 02 2009”。
## 方法
+ [\_\_call\_\_](#aws-glue-api-pyspark-transforms-MonthName-__call__)
+ [apply](#aws-glue-api-crawler-pyspark-transforms-MonthName-apply)
+ [name](#aws-glue-api-crawler-pyspark-transforms-MonthName-name)
+ [describeArgs](#aws-glue-api-crawler-pyspark-transforms-MonthName-describeArgs)
+ [describeReturn](#aws-glue-api-crawler-pyspark-transforms-MonthName-describeReturn)
+ [describeTransform](#aws-glue-api-crawler-pyspark-transforms-MonthName-describeTransform)
+ [describeErrors](#aws-glue-api-crawler-pyspark-transforms-MonthName-describeErrors)
+ [describe](#aws-glue-api-crawler-pyspark-transforms-MonthName-describe)
## \_\_call\_\_(spark\_context, data\_frame, target\_column, source\_column=None, value=None)
`MonthName` 转换会使用表示日期的字符串创建一个包含月份名称的新列。
+ `source_column`：现有列的名称。
+ `value` – 要评估的字符串。
+ `target_column` – 新创建的列的名称。
## apply(cls, \*args, \*\*kwargs)
继承自 `GlueTransform` [apply](aws-glue-api-crawler-pyspark-transforms-GlueTransform.md#aws-glue-api-crawler-pyspark-transforms-GlueTransform-apply)。
## name(cls)
继承自 `GlueTransform` [名称](aws-glue-api-crawler-pyspark-transforms-GlueTransform.md#aws-glue-api-crawler-pyspark-transforms-GlueTransform-name)。
## describeArgs(cls)
继承自 `GlueTransform` [describeArgs](aws-glue-api-crawler-pyspark-transforms-GlueTransform.md#aws-glue-api-crawler-pyspark-transforms-GlueTransform-describeArgs)。
## describeReturn(cls)
继承自 `GlueTransform` [describeReturn](aws-glue-api-crawler-pyspark-transforms-GlueTransform.md#aws-glue-api-crawler-pyspark-transforms-GlueTransform-describeReturn)。
## describeTransform(cls)
继承自 `GlueTransform` [describeTransform](aws-glue-api-crawler-pyspark-transforms-GlueTransform.md#aws-glue-api-crawler-pyspark-transforms-GlueTransform-describeTransform)。
## describeErrors(cls)
继承自 `GlueTransform` [describeErrors](aws-glue-api-crawler-pyspark-transforms-GlueTransform.md#aws-glue-api-crawler-pyspark-transforms-GlueTransform-describeErrors)。
## describe(cls)
继承自 `GlueTransform` [describe](aws-glue-api-crawler-pyspark-transforms-GlueTransform.md#aws-glue-api-crawler-pyspark-transforms-GlueTransform-describe)。