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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>>
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>> arise from relying on this email's technical content is explicitly
>> disclaimed. The author will in no case be liable for any monetary damages
>> arising from such loss, damage or destruction.
>>
>>
>>
>> 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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>>>
>>> *Disclaimer:* Use it at your own risk. Any and all responsibility for
>>> any loss, damage or destruction of data or any other property which may
>>> arise from relying on this email's technical content is explicitly
>>> disclaimed. The author will in no case be liable for any monetary damages
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>>>
>>>
>>
>>
>
