[英]How to add multiple columns to pyspark DF using pandas_udf with multiple source columns?
And I need to extract from utc_timestamp
its date and its hour into two different columns depending on time zone.而且我需要根据时区从utc_timestamp
中提取其日期和时间到两个不同的列中。 Time zone name is defined by id
from configuration const variable.时区名称由配置 const 变量中的id
定义。
Input DF Output DF
+-------------+--+ +-------------+--+----------+----+
|utc_timestamp|id| |utc_timestamp|id|date |hour|
+-------------+--+ +-------------+--+----------+----|
|1608000000782|1 | |1608000000782|1 |2020-12-14|20 |
+-------------+--+ +-------------+--+----------+----+
|1608000240782|2 | |1608000240782|2 |2020-12-15|11 |
+-------------+--+ +-------------+--+----------+----+
I have pandas_udf that allows me to extract one column at a time and I have to create it twice:我有 pandas_udf 允许我一次提取一列,我必须创建它两次:
from pyspark.sql.functions import pandas_udf, PandasUDFType
from pyspark.sql.types import DateType, IntegerType
import pandas as pd
import pytz
TIMEZONE_LIST = {1: 'America/Chicago', 2: 'Asia/Tokyo'}
class TimezoneUdfProvider(object):
def __init__(self):
self.extract_date_udf = pandas_udf(self._extract_date, DateType(), PandasUDFType.SCALAR)
self.extract_hour_udf = pandas_udf(self._extract_hour, IntegerType(), PandasUDFType.SCALAR)
def _extract_date(self, utc_timestamps: pd.Series, ids: pd.Series) -> pd.Series:
return pd.Series([extract_date(c1, c2) for c1, c2 in zip(utc_timestamps, ids)])
def _extract_hour(self, utc_timestamps: pd.Series, ids: pd.Series) -> pd.Series:
return pd.Series([extract_hour(c1, c2) for c1, c2 in zip(utc_timestamps, ids)])
def extract_date(utc_timestamp: int, id: str):
timezone_name = TIMEZONE_LIST[id]
timezone_nw = pytz.timezone(timezone_name)
return pd.datetime.fromtimestamp(utc_timestamp / 1000e00, tz=timezone_nw).date()
def extract_hour(utc_timestamp: int, id: str) -> int:
timezone_name = TIMEZONE_LIST[id]
timezone_nw = pytz.timezone(timezone_name)
return pd.datetime.fromtimestamp(utc_timestamp / 1000e00, tz=timezone_nw).hour
def extract_from_utc(df: DataFrame) -> DataFrame:
timezone_udf1 = TimezoneUdfProvider()
df_with_date = df.withColumn('date', timezone_udf1.extract_date_udf(f.col(utc_timestamp), f.col(id)))
timezone_udf2 = TimezoneUdfProvider()
df_with_hour = df_with_date.withColumn('hour', timezone_udf2.extract_hour_udf(f.col(utc_timestamp), f.col(id)))
return df_with_hour
Is there a better way to do it?有更好的方法吗? Without a need to use the same udf provider twice?不需要两次使用相同的 udf 提供程序?
you can do this without using udf using spark inbuilt functions.您可以在不使用 udf 的情况下使用 spark 内置函数来执行此操作。
We can use create_map
to map the dictionary and create new timezone column, then convert using from_unixtime
and from_utc_timestamp
using the timezone as the newly mapped column.我们可以使用create_map
到 map 字典并创建新的时区列,然后使用from_unixtime
和from_utc_timestamp
将时区作为新映射的列进行转换。 Once we have the timestamp as per the timezones, we can then fetch Hour and date feilds.一旦我们根据时区获得时间戳,我们就可以获取小时和日期字段。
TIMEZONE_LIST = {1: 'America/Chicago', 2: 'Asia/Tokyo'}
import pyspark.sql.functions as F
from itertools import chain
map_exp = F.create_map([F.lit(i) for i in chain(*TIMEZONE_LIST.items())])
final = (df.withColumn("TimeZone", map_exp.getItem(col("id")))
.withColumn("Timestamp",
F.from_utc_timestamp(F.from_unixtime(F.col("utc_timestamp")/1000),F.col("TimeZone")))
.withColumn("date",F.to_date("Timestamp")).withColumn("Hour",F.hour("Timestamp"))
.drop("Timestamp"))
final.show()
(3) Spark Jobs
final:pyspark.sql.dataframe.DataFrame = [utc_timestamp: long, id: long ... 3 more fields]
+-------------+---+---------------+----------+----+
|utc_timestamp| id| TimeZone| date|Hour|
+-------------+---+---------------+----------+----+
|1608000000782| 1|America/Chicago|2020-12-14| 20|
|1608000240782| 2| Asia/Tokyo|2020-12-15| 11|
+-------------+---+---------------+----------+----+
EDIT : replacing create_map
with a udf
:编辑:用udf
替换create_map
:
import pyspark.sql.functions as F
from pyspark.sql.functions import StringType
TIMEZONE_LIST = {1: 'America/Chicago', 2: 'Asia/Tokyo'}
def fun(x):
return TIMEZONE_LIST.get(x,None)
map_udf = F.udf(fun,StringType())
final = (df.withColumn("TimeZone", map_udf("id")).withColumn("Timestamp",
F.from_utc_timestamp(F.from_unixtime(F.col("utc_timestamp")/1000),F.col("TimeZone")))
.withColumn("date",F.to_date("Timestamp")).withColumn("Hour",F.hour("Timestamp"))
.drop("Timestamp"))
final.show()
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