将Dataframe中的日期从字符串格式转换为日期格式

a64a0gku  于 2021-07-09  发布在  Spark
关注(0)|答案(11)|浏览(416)

我正在尝试使用 to_date 函数,但返回空值。

df.createOrReplaceTempView("incidents")
spark.sql("select Date from incidents").show()

+----------+
|      Date|
+----------+
|08/26/2016|
|08/26/2016|
|08/26/2016|
|06/14/2016|

spark.sql("select to_date(Date) from incidents").show()

+---------------------------+
|to_date(CAST(Date AS DATE))|
 +---------------------------+
|                       null|
|                       null|
|                       null|
|                       null|

日期列采用字符串格式:

|-- Date: string (nullable = true)
mgdq6dx1

mgdq6dx11#

你也可以做这个查询。。。!

sqlContext.sql("""
select from_unixtime(unix_timestamp('08/26/2016', 'MM/dd/yyyy'), 'yyyy:MM:dd') as new_format
""").show()

wfveoks0

wfveoks02#

你可以这么做 df.withColumn("date", date_format(col("string"),"yyyy-MM-dd HH:mm:ss.ssssss")).show()

9rnv2umw

9rnv2umw3#

也可以传递日期格式

df.withColumn("Date",to_date(unix_timestamp(df.col("your_date_column"), "your_date_format").cast("timestamp")))

例如

import org.apache.spark.sql.functions._
val df = sc.parallelize(Seq("06 Jul 2018")).toDF("dateCol")
df.withColumn("Date",to_date(unix_timestamp(df.col("dateCol"), "dd MMM yyyy").cast("timestamp")))
x7rlezfr

x7rlezfr4#

因为您的主要目的是将Dataframe中的列类型从字符串转换为时间戳,所以我认为这种方法会更好。

import org.apache.spark.sql.functions.{to_date, to_timestamp}
val modifiedDF = DF.withColumn("Date", to_date($"Date", "MM/dd/yyyy"))

你也可以用 to_timestamp (我认为spark2.x提供了这一功能)如果您需要细粒度的时间戳。

jc3wubiy

jc3wubiy5#

sai kiriti badam提出的上述解决方案对我有效。
我正在使用azuredatabricks读取从eventhub捕获的数据。它包含一个名为enqueuedtimeutc的字符串列,格式如下。。。
2018年12月7日下午12:54:13
我用的是python笔记本,用的是以下内容。。。

import pyspark.sql.functions as func

sports_messages = sports_df.withColumn("EnqueuedTimestamp", func.to_timestamp("EnqueuedTimeUtc", "MM/dd/yyyy hh:mm:ss aaa"))

... 要使用以下格式的数据创建“timestamp”类型的新列enqueuedtimestamp。。。
2018-12-07 12:54:13

gab6jxml

gab6jxml6#

在pyspark中使用下面的函数将数据类型转换为所需的数据类型。在这里,我将所有日期数据类型转换为timestamp列。

def change_dtype(df):
    for name, dtype in df.dtypes:
        if dtype == "date":
            df = df.withColumn(name, col(name).cast('timestamp'))
    return df
pepwfjgg

pepwfjgg7#

我在没有temp表/视图和dataframe函数的情况下解决了同样的问题。
当然,我发现只有一种格式适用于这个解决方案,那就是 yyyy-MM-DD .
例如:

val df = sc.parallelize(Seq("2016-08-26")).toDF("Id")
val df2 = df.withColumn("Timestamp", (col("Id").cast("timestamp")))
val df3 = df2.withColumn("Date", (col("Id").cast("date")))

df3.printSchema

root
 |-- Id: string (nullable = true)
 |-- Timestamp: timestamp (nullable = true)
 |-- Date: date (nullable = true)

df3.show

+----------+--------------------+----------+
|        Id|           Timestamp|      Date|
+----------+--------------------+----------+
|2016-08-26|2016-08-26 00:00:...|2016-08-26|
+----------+--------------------+----------+

时间戳当然有 00:00:00.0 作为时间值。

hmae6n7t

hmae6n7t8#

我个人发现在使用spark 1.6时,使用从dd-mmm-yyyy格式到yyyy-mm-dd格式的基于unix时间戳的日期转换时出现了一些错误,但这可能会扩展到最新版本。下面我将介绍一种使用java.time解决问题的方法,该方法应适用于spark的所有版本:
在执行以下操作时,我看到了错误:

from_unixtime(unix_timestamp(StockMarketClosingDate, 'dd-MMM-yyyy'), 'yyyy-MM-dd') as FormattedDate

下面是说明错误的代码,以及我的解决方案。首先,我以一种通用的标准文件格式读入股市数据:

import sys.process._
    import org.apache.spark.sql.SQLContext
    import org.apache.spark.sql.functions.udf
    import org.apache.spark.sql.types.{StructType, StructField, StringType, IntegerType, DateType}
    import sqlContext.implicits._

    val EODSchema = StructType(Array(
        StructField("Symbol"                , StringType, true),     //$1       
        StructField("Date"                  , StringType, true),     //$2       
        StructField("Open"                  , StringType, true),     //$3       
        StructField("High"                  , StringType, true),     //$4
        StructField("Low"                   , StringType, true),     //$5
        StructField("Close"                 , StringType, true),     //$6
        StructField("Volume"                , StringType, true)      //$7
        ))

    val textFileName = "/user/feeds/eoddata/INDEX/INDEX_19*.csv"

    // below is code to read using later versions of spark
    //val eoddata = spark.read.format("csv").option("sep", ",").schema(EODSchema).option("header", "true").load(textFileName)

    // here is code to read using 1.6, via, "com.databricks:spark-csv_2.10:1.2.0"

    val eoddata = sqlContext.read
                               .format("com.databricks.spark.csv")
                               .option("header", "true")                               // Use first line of all files as header
                               .option("delimiter", ",")                               //.option("dateFormat", "dd-MMM-yyyy") failed to work
                               .schema(EODSchema)
                               .load(textFileName)

    eoddata.registerTempTable("eoddata")

以下是有问题的日期转换:

%sql 
-- notice there are errors around the turn of the year
Select 
    e.Date as StringDate
,   cast(from_unixtime(unix_timestamp(e.Date, "dd-MMM-yyyy"), 'YYYY-MM-dd') as Date)  as ProperDate
,   e.Close
from eoddata e
where e.Symbol = 'SPX.IDX'
order by cast(from_unixtime(unix_timestamp(e.Date, "dd-MMM-yyyy"), 'YYYY-MM-dd') as Date)
limit 1000

齐柏林飞艇制造的图表显示了尖峰,这是错误。

下面是显示日期转换错误的检查:

// shows the unix_timestamp conversion approach can create errors
val result =  sqlContext.sql("""
Select errors.* from
(
    Select 
    t.*
    , substring(t.OriginalStringDate, 8, 11) as String_Year_yyyy 
    , substring(t.ConvertedCloseDate, 0, 4)  as Converted_Date_Year_yyyy
    from
    (        Select
                Symbol
            ,   cast(from_unixtime(unix_timestamp(e.Date, "dd-MMM-yyyy"), 'YYYY-MM-dd') as Date)  as ConvertedCloseDate
            ,   e.Date as OriginalStringDate
            ,   Close
            from eoddata e
            where e.Symbol = 'SPX.IDX'
    ) t 
) errors
where String_Year_yyyy <> Converted_Date_Year_yyyy
""")

//df.withColumn("tx_date", to_date(unix_timestamp($"date", "M/dd/yyyy").cast("timestamp")))

result.registerTempTable("SPX")
result.cache()
result.show(100)
result: org.apache.spark.sql.DataFrame = [Symbol: string, ConvertedCloseDate: date, OriginalStringDate: string, Close: string, String_Year_yyyy: string, Converted_Date_Year_yyyy: string]
res53: result.type = [Symbol: string, ConvertedCloseDate: date, OriginalStringDate: string, Close: string, String_Year_yyyy: string, Converted_Date_Year_yyyy: string]
+-------+------------------+------------------+-------+----------------+------------------------+
| Symbol|ConvertedCloseDate|OriginalStringDate|  Close|String_Year_yyyy|Converted_Date_Year_yyyy|
+-------+------------------+------------------+-------+----------------+------------------------+
|SPX.IDX|        1997-12-30|       30-Dec-1996| 753.85|            1996|                    1997|
|SPX.IDX|        1997-12-31|       31-Dec-1996| 740.74|            1996|                    1997|
|SPX.IDX|        1998-12-29|       29-Dec-1997| 953.36|            1997|                    1998|
|SPX.IDX|        1998-12-30|       30-Dec-1997| 970.84|            1997|                    1998|
|SPX.IDX|        1998-12-31|       31-Dec-1997| 970.43|            1997|                    1998|
|SPX.IDX|        1998-01-01|       01-Jan-1999|1229.23|            1999|                    1998|
+-------+------------------+------------------+-------+----------------+------------------------+
FINISHED

在这个结果之后,我用这样的自定义项切换到java.time转换,这对我很有用:

// now we will create a UDF that uses the very nice java.time library to properly convert the silly stockmarket dates
// start by importing the specific java.time libraries that superceded the joda.time ones
import java.time.LocalDate
import java.time.format.DateTimeFormatter

// now define a specific data conversion function we want

def fromEODDate (YourStringDate: String): String = {

    val formatter = DateTimeFormatter.ofPattern("dd-MMM-yyyy")
    var   retDate = LocalDate.parse(YourStringDate, formatter)

    // this should return a proper yyyy-MM-dd date from the silly dd-MMM-yyyy formats
    // now we format this true local date with a formatter to the desired yyyy-MM-dd format

    val retStringDate = retDate.format(DateTimeFormatter.ISO_LOCAL_DATE)
    return(retStringDate)
}

现在我将其注册为一个函数,以便在sql中使用:

sqlContext.udf.register("fromEODDate", fromEODDate(_:String))

并检查结果,然后重新运行测试:

val results = sqlContext.sql("""
    Select
        e.Symbol    as Symbol
    ,   e.Date      as OrigStringDate
    ,   Cast(fromEODDate(e.Date) as Date) as ConvertedDate
    ,   e.Open
    ,   e.High
    ,   e.Low
    ,   e.Close
    from eoddata e
    order by Cast(fromEODDate(e.Date) as Date)
""")

results.printSchema()
results.cache()
results.registerTempTable("results")
results.show(10)
results: org.apache.spark.sql.DataFrame = [Symbol: string, OrigStringDate: string, ConvertedDate: date, Open: string, High: string, Low: string, Close: string]
root
 |-- Symbol: string (nullable = true)
 |-- OrigStringDate: string (nullable = true)
 |-- ConvertedDate: date (nullable = true)
 |-- Open: string (nullable = true)
 |-- High: string (nullable = true)
 |-- Low: string (nullable = true)
 |-- Close: string (nullable = true)
res79: results.type = [Symbol: string, OrigStringDate: string, ConvertedDate: date, Open: string, High: string, Low: string, Close: string]
+--------+--------------+-------------+-------+-------+-------+-------+
|  Symbol|OrigStringDate|ConvertedDate|   Open|   High|    Low|  Close|
+--------+--------------+-------------+-------+-------+-------+-------+
|ADVA.IDX|   01-Jan-1996|   1996-01-01|    364|    364|    364|    364|
|ADVN.IDX|   01-Jan-1996|   1996-01-01|   1527|   1527|   1527|   1527|
|ADVQ.IDX|   01-Jan-1996|   1996-01-01|   1283|   1283|   1283|   1283|
|BANK.IDX|   01-Jan-1996|   1996-01-01|1009.41|1009.41|1009.41|1009.41|
| BKX.IDX|   01-Jan-1996|   1996-01-01|  39.39|  39.39|  39.39|  39.39|
|COMP.IDX|   01-Jan-1996|   1996-01-01|1052.13|1052.13|1052.13|1052.13|
| CPR.IDX|   01-Jan-1996|   1996-01-01|  1.261|  1.261|  1.261|  1.261|
|DECA.IDX|   01-Jan-1996|   1996-01-01|    205|    205|    205|    205|
|DECN.IDX|   01-Jan-1996|   1996-01-01|    825|    825|    825|    825|
|DECQ.IDX|   01-Jan-1996|   1996-01-01|    754|    754|    754|    754|
+--------+--------------+-------------+-------+-------+-------+-------+
only showing top 10 rows

我重新运行图表,看看是否有错误/尖峰:

如您所见,没有更多的尖峰或错误。我现在使用自定义项,正如我所展示的那样,将我的日期格式转换应用到标准的yyyy-mm-dd格式,并且从那以后就没有任何错误了。:-)

nimxete2

nimxete29#

dateid为int列包含int格式的日期

spark.sql("SELECT from_unixtime(unix_timestamp(cast(dateid as varchar(10)), 'yyyymmdd'), 'yyyy-mm-dd') from XYZ").show(50, false)
6jjcrrmo

6jjcrrmo10#

使用 to_date 使用java SimpleDateFormat .

TO_DATE(CAST(UNIX_TIMESTAMP(date, 'MM/dd/yyyy') AS TIMESTAMP))

例子:

spark.sql("""
  SELECT TO_DATE(CAST(UNIX_TIMESTAMP('08/26/2016', 'MM/dd/yyyy') AS TIMESTAMP)) AS newdate"""
).show()

+----------+
|        dt|
+----------+
|2016-08-26|
+----------+
iibxawm4

iibxawm411#

找到下面提到的代码,可能会对您有所帮助。

val stringDate = spark.sparkContext.parallelize(Seq("12/16/2019")).toDF("StringDate")
                    val dateCoversion = stringDate.withColumn("dateColumn", to_date(unix_timestamp($"StringDate", "dd/mm/yyyy").cast("Timestamp")))
                    dateCoversion.show(false)
+----------+----------+
|StringDate|dateColumn|
+----------+----------+
|12/16/2019|2019-01-12|
+----------+----------+

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