在pyspark中包含两个日期之间获取开始和结束

Question

我一直在尝试获取 2 个给定日期的月份范围，但它没有按预期工作。

例如

start_date (dd-mm-yyyy) = 12-01-2022
end_date (dd-mm-yyyy) = 03-06-2022

预期输出：

Valid_From	Valid_To
2022-01-12	2022-01-31
2022-02-01	2022-02-28
2022-03-01	2022-03-31
2022-04-01	2022-04-30
2022-05-01	2022-05-31
2022-06-01	2022-06-03

我的代码：

var_forecast_start_date = datetime.datetime(2022, 1, 12)
var_forecast_end_date = datetime.datetime(2022, 6, 2)

df_datetime = pandas_to_spark(
    df_datetime(start=var_forecast_start_date, end=var_forecast_end_date)
)


df_datetime = df_datetime.withColumn(
    "DateID", date_format(df_datetime.Date, "yyyyMMdd").cast(IntegerType())
).withColumn("FiscalDate", date_format(df_datetime.Date, "yyyy-MM-dd"))

df_datetime = df_datetime.selectExpr(
    "add_months(date_add(last_day(Date),1),-1) AS Valid_From",
    "last_day(Date) AS Valid_To",
).distinct()

Answer 1

尝试以下方法：

import findspark
from pyspark.sql import SparkSession, Window
from pyspark.sql import functions as F

findspark.init()
spark = SparkSession.builder.appName("local").getOrCreate()
columns = ["start_date", "end_date"]
data = [("12-01-2022", "03-06-2022")]

df = spark.createDataFrame(data).toDF(*columns)
df = (
    df.withColumn(
        "start_date", F.to_date(F.col("start_date"), "dd-MM-yyyy").cast("DATE")
    )
    .withColumn(
        "end_date", F.to_date(F.col("end_date"), "dd-MM-yyyy").cast("DATE")
    )
    .withColumn(
        "months_between",
        F.round(
            F.months_between(F.col("end_date"), F.col("start_date"), True)
        ).cast("Integer"),
    )
    .withColumn(
        "months_between_seq", F.sequence(F.lit(1), F.col("months_between"))
    )
    .withColumn("months_between_seq", F.explode(F.col("months_between_seq")))
    .withColumn(
        "end_of_month",
        F.expr(
            """
                LAST_DAY(ADD_MONTHS(start_date, months_between_seq - 1))
            """
        ),
    )
    .withColumn(
        "begin_of_month",
        F.expr(
            """
                LAST_DAY(ADD_MONTHS(start_date, months_between_seq - 1)) + 1
            """
        ),
    )
)

start_window_agg = Window.partitionBy().orderBy("Valid_From")
start_union_sdf = (
    df.select(
        F.col("start_date").alias("Valid_From")
    )
    .unionByName(
        df.select(
            F.col("begin_of_month").alias("Valid_From")
        )
    )
    .drop_duplicates()
    .withColumn(
        "row_number",
        F.row_number().over(start_window_agg)
    )
)
end_window_agg = Window.partitionBy().orderBy("Valid_To")
end_union_sdf = (
    df.select(
        F.col("end_date").alias("Valid_To")
    )
    .unionByName(
        df.select(
            F.col("end_of_month").alias("Valid_To")
        )
    )
    .drop_duplicates()
    .withColumn(
        "row_number",
        F.row_number().over(end_window_agg)
    )
)
join_sdf = (
    end_union_sdf
    .join(
        start_union_sdf,
        how="inner",
        on=["row_number"]
    )
    .drop("row_number")
    .withColumn("Valid_To", F.col("Valid_To").cast("DATE"))
    .withColumn("Valid_From", F.col("Valid_From").cast("DATE"))
    .select("Valid_From", "Valid_To")
    .orderBy("Valid_From")
)
join_sdf.show()

它returns:

+----------+----------+
|Valid_From|  Valid_To|
+----------+----------+
|2022-01-12|2022-01-31|
|2022-02-01|2022-02-28|
|2022-03-01|2022-03-31|
|2022-04-01|2022-04-30|
|2022-05-01|2022-05-31|
|2022-06-01|2022-06-03|
+----------+----------+

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