Thanks guys.
This seemed to be working after declaring all columns as Strings to start
and using filters below to avoid rogue characters. The second filter
ensures that there was trade volumes on that date.
val *rs = df2.filter($"Open" !== "-").filter($"Volume".cast("Integer")
> 0*).filter(changeToDate("TradeDate")
>=
monthsago).select((changeToDate("TradeDate").as("TradeDate")),(round(($"Close".cast("Float")+$"Open".cast("Float"))/2,2)).as("AverageDailyPrice"),
$"Low".cast("Float").as("Day's Low"), $"High".cast("Float")as("Day's
High")).orderBy(changeToDate("TradeDate"))
Cheers
Dr Mich Talebzadeh
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On 28 September 2016 at 14:45, Bedrytski Aliaksandr <sp...@bedryt.ski>
wrote:
> Hi Mich,
>
> if I understood you well, you may cast the value to float, it will yield
> null if the value is not a correct float:
>
> val df = Seq(("-", 5), ("1", 6), (",", 7), ("8.6", 7)).toDF("value",
> "id").createOrReplaceTempView("lines")
>
> spark.sql("SELECT cast(value as FLOAT) from lines").show()
>
> +-----+
> |value|
> +-----+
> | null|
> | 1.0 |
> | null|
> | 8.6 |
> +-----+
>
> After it you may filter the DataFrame for values containing null.
>
> Regards,
> --
> Bedrytski Aliaksandr
> sp...@bedryt.ski
>
>
>
> On Wed, Sep 28, 2016, at 10:11, Mich Talebzadeh wrote:
>
> Thanks all.
>
> This is the csv schema all columns mapped to String
>
> scala> df2.printSchema
> root
> |-- Stock: string (nullable = true)
> |-- Ticker: string (nullable = true)
> |-- TradeDate: string (nullable = true)
> |-- Open: string (nullable = true)
> |-- High: string (nullable = true)
> |-- Low: string (nullable = true)
> |-- Close: string (nullable = true)
> |-- Volume: string (nullable = true)
>
> The issue I have can be shown as below
>
> df2.filter( $"OPen" ===
> "-").select((changeToDate("TradeDate").as("TradeDate")),
> 'Open, 'High, 'Low, 'Close, 'Volume).show
>
> +----------+----+----+---+-----+------+
> | TradeDate|Open|High|Low|Close|Volume|
> +----------+----+----+---+-----+------+
> |2011-12-23| -| -| -|40.56| 0|
> |2011-04-21| -| -| -|45.85| 0|
> |2010-12-30| -| -| -|38.10| 0|
> |2010-12-23| -| -| -|38.36| 0|
> |2008-04-30| -| -| -|32.39| 0|
> |2008-04-29| -| -| -|33.05| 0|
> |2008-04-28| -| -| -|32.60| 0|
> +----------+----+----+---+-----+------+
> Now there are ways of dealing with this. However, the solution has to be
> generic! Checking for a column == "-" is not generic. How about if that
> column was "," etc.
>
> This is an issue in most databases. Specifically if a field is NaN.. --> (
> *NaN*, standing for not a number, is a numeric data type value
> representing an undefined or unrepresentable value, especially in
> floating-point calculations)
>
> Spark handles this
> <https://spark.apache.org/docs/1.5.1/api/java/org/apache/spark/sql/DataFrameNaFunctions.html>.
> I am on Spark 2.0.1 in Class DataFrameNaFunctions. The simplest one is to
> drop these rogue rows
>
> df2.filter( $"Open" === "-").drop()
>
> However, a better approach would be to use REPLACE method or testing any
> column for NaN
>
>
>
>
> There is a method called isnan(). However, it does not return correct
> values!
>
> df2.filter(isnan($"Open")).show
> +-----+------+---------+----+----+---+-----+------+
> |Stock|Ticker|TradeDate|Open|High|Low|Close|Volume|
> +-----+------+---------+----+----+---+-----+------+
> +-----+------+---------+----+----+---+-----+------+
>
>
> Any suggestions?
>
> Thanks
>
>
>
>
>
>
> Dr Mich Talebzadeh
>
>
>
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>
> On 28 September 2016 at 04:07, Mike Metzger <m...@flexiblecreations.com>
> wrote:
>
> Hi Mich -
>
> Can you run a filter command on df1 prior to your map for any rows
> where p(3).toString != '-' then run your map command?
>
> Thanks
>
>
> Mike
>
>
> On Tue, Sep 27, 2016 at 5:06 PM, Mich Talebzadeh <
> mich.talebza...@gmail.com> wrote:
>
> Thanks guys
>
> Actually these are the 7 rogue rows. The column 0 is the Volume column
> which means there was no trades on those days
>
>
> *cat stock.csv|grep ",0"*SAP SE,SAP, 23-Dec-11,-,-,-,40.56,0
> SAP SE,SAP, 21-Apr-11,-,-,-,45.85,0
> SAP SE,SAP, 30-Dec-10,-,-,-,38.10,0
> SAP SE,SAP, 23-Dec-10,-,-,-,38.36,0
> SAP SE,SAP, 30-Apr-08,-,-,-,32.39,0
> SAP SE,SAP, 29-Apr-08,-,-,-,33.05,0
> SAP SE,SAP, 28-Apr-08,-,-,-,32.60,0
>
> So one way would be to exclude the rows that there was no volume of trade
> that day when cleaning up the csv file
>
> *cat stock.csv|grep -v ",0"*
>
> and that works. Bearing in mind that putting 0s in place of "-" will skew
> the price plot.
>
> BTW I am using Spark csv as well
>
> val df1 = spark.read.option("header", true).csv(location)
>
> This is the class and the mapping
>
>
> case class columns(Stock: String, Ticker: String, TradeDate: String, Open:
> Float, High: Float, Low: Float, Close: Float, Volume: Integer)
> val df2 = df1.map(p => columns(p(0).toString, p(1).toString,
> p(2).toString, p(3).toString.toFloat, p(4).toString.toFloat,
> p(5).toString.toFloat, p(6).toString.toFloat, p(7).toString.toInt))
>
>
>
> In here I have
>
> p(3).toString.toFloat
>
>
> How can one check for rogue data in p(3)?
>
>
> Thanks
>
>
>
>
>
>
>
>
> Dr Mich Talebzadeh
>
>
>
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>
> On 27 September 2016 at 21:49, Mich Talebzadeh <mich.talebza...@gmail.com>
> wrote:
>
>
> I have historical prices for various stocks.
>
> Each csv file has 10 years trade one row per each day.
>
> These are the columns defined in the class
>
> case class columns(Stock: String, Ticker: String, TradeDate: String, Open:
> Float, High: Float, Low: Float, Close: Float, Volume: Integer)
>
> The issue is with Open, High, Low, Close columns that all are defined as
> Float.
>
> Most rows are OK like below but the red one with "-" defined as Float
> causes issues
>
> Date Open High Low Close Volume
> 27-Sep-16 80.91 80.93 79.87 80.85 1873158
> 23-Dec-11 - - - 40.56 0
>
> Because the prices are defined as Float, these rows cause the application
> to crash
> scala> val rs = df2.filter(changeToDate("TradeDate") >=
> monthsago).select((changeToDate("TradeDate").as("TradeDate")
> ),(('Close+'Open)/2).as("AverageDailyPrice"), 'Low.as("Day's Low"),
> 'High.as("Day's High")).orderBy("TradeDate").collect
> 16/09/27 21:48:53 ERROR Executor: Exception in task 0.0 in stage 61.0 (TID
> 260)
> java.lang.NumberFormatException: For input string: "-"
>
>
>
>
> One way is to define the prices as Strings but that is not
> meaningful. Alternatively do the clean up before putting csv in HDFS but
> that becomes tedious and error prone.
>
> Any ideas will be appreciated.
>
>
>
> Dr Mich Talebzadeh
>
>
>
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