你可以用 to_date 要将月份转换为日期，请使用 date_format 要获取月份名称：

from pyspark.sql import functions as F
df = spark.createDataFrame([("1",), ("2",), ("3",), ("4",), ("5",)], ["month_number"])
df1 = df.withColumn("month_name", F.date_format(F.to_date("month_number", "MM"), "MMMM")) \
    .withColumn("month_abbr", F.date_format(F.to_date("month_number", "MM"), "MMM"))
df1.show()
# +------------+----------+----------+
# |month_number|month_name|month_abbr|
# +------------+----------+----------+
# |           1|   January|       Jan|
# |           2|  February|       Feb|
# |           3|     March|       Mar|
# |           4|     April|       Apr|
# |           5|       May|       May|
# +------------+----------+----------+

请注意，对于spark 3，需要设置 spark.conf.set("spark.sql.legacy.timeParserPolicy", "LEGACY") 将月数转换为日期。
也可以使用保存Map的Map列 month_number -> month_abbr :

import calendar
import itertools
from pyspark.sql import functions as F
months = F.create_map(*[
    F.lit(m) for m in itertools.chain(*[(x, calendar.month_abbr[x]) for x in range(1, 12, 1)])
])
df1 = df.withColumn("month_abbr", months[F.col("month_number")])

使用自定义项的另一种方法：

import calendar
from pyspark.sql import functions as F
month_name = F.udf(lambda x: calendar.month_name[int(x)])
month_abbr = F.udf(lambda x: calendar.month_abbr[int(x)])
df1 = df.withColumn("month_name", month_name(F.col("month_number"))) \
    .withColumn("month_abbr", month_abbr(F.col("month_number")))

如果有人想在scala中执行此操作，可以按以下方式执行：

//Sample Data
val df = Seq(("1"),("2"),("3"),("4"),("5"),("6"),("7"),("8"),("9"),("10"),("11"),("12")).toDF("month_number")
import org.apache.spark.sql.functions._
val df1 = df.withColumn("Month_Abbr",date_format(to_date($"month_number","MM"),"MMM"))
display(df1)

您可以看到如下输出：