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MonthName class

The MonthName transform creates a new column containing the name of the month, from a string that represents a date.

Example

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

The output will be:

```
+------------+------------+
| 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 |
+------------+------------+
```

The MonthName transformation takes the `source_column` as `"column_1"` and the `target_column` as `"target_column"`. It attempts to extract the month name from the date/time strings in the `"column_1"` column and places it in the `"target_column"` column. If the date/time string is in an unrecognized format or cannot be parsed, the `"target_column"` value is set to `null`.

The transformation successfully extracts the month name from various date/time formats, such as "20-12-2018", "12/20/2018", "02/02/2009", "2013-02-21 06:35:45.658505", and "August 02 2009".

Methods

__call__(spark_context, data_frame, target_column, source_column=None, value=None)

The MonthName transform creates a new column containing the name of the month, from a string that represents a date.

apply(cls, *args, **kwargs)

Inherited from GlueTransform apply.

name(cls)

Inherited from GlueTransform name.

describeArgs(cls)

Inherited from GlueTransform describeArgs.

describeReturn(cls)

Inherited from GlueTransform describeReturn.

describeTransform(cls)

Inherited from GlueTransform describeTransform.

describeErrors(cls)

Inherited from GlueTransform describeErrors.

describe(cls)

Inherited from GlueTransform describe.