Personally, after loading a data set with DataFrame, i run a loop to
transform it to what I like.
On Tuesday, August 4, 2015 at 3:50:01 PM UTC+2, Danny Zuko wrote:
>
> Le jeudi 23 juillet 2015 15:16:49 UTC+2, Andreas Noack a écrit :
>>
>> For the example you describe, you can simply use the tools in base, but
>> unfortunately I don't think our reader can handle continental style decimal
>> comma yet. However, that is easy to search/replace with a dot. Something
>> like
>>
>> cov(diff(log(readdlm("prices.csv", ';'))))
>>
>> should then do the job.
>>
>
> Let's assume we have a csv file of the following form:
>
> "Dates","ES1 Index","VG1 Index","TY1 Comdty","RX1 Comdty","GC1 Comdty"
> "1999-01-04",1391.12,3034.53,66.515625,86.2,441.39
> "1999-01-05",1404.86,3072.41,66.3125,86.17,440.63
> "1999-01-06",1435.12,3156.59,66.4375,86.32,441.7
>
>
> readcsv("prices.csv", header=true) does its job.
>
> However, I won't be able to pass it to any function that performs any
> numerical computation as all values in the first column are strings. Hence,
> log(readcsv("prices.csv", header=true)) will return an error.
>
> The read.csv function in R has a handy row.names arguments which - when
> you set it equal to 1 - takes the first column of your csv file as a vector
> of row-names. Hence, if you evaluate log(read.csv("prices.csv", sep = ";",
> row.names=1)) you do not get any error as all values are now numerical +
> each row keeps its own row name.
>
> As of now, the only place where I found this handy feature is the
> readtimearray function in the TimeSeries.jl package. Is there any other way
> in Julia to benefit from this little but handy comfort that I am not yet
> aware of?
>
