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How to convert from double to double[][] and add multiple columns to dataframe?

 
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I'm trying to use this method from the Smile library:

but I'd like to add one double integer as the value in the Dataframe,

so I'm wondering how I can convert a double to double[][] or if there is a better way to add the value to the dataframe?

Additionally I'd like to add multiple columns with one double integer, for ex. I'd have 5 columns with 1 row:

so I'm wondering if I have to make 5 separate dataframes and then merge them all together or if I can do that in less steps?

Thanks so much!
 
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hi Glenda,

this works:

or this:

and y is a 1x1 double[][].

If you have one row (as in your example, header not included), then you can turn that into a double[][] as well, like in:

You get a 5 x 1 double[][], assuming that a row in your 2D array is a column in your daraframe. Then uae the constructor.
 
Glenda Karen
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Thanks so much!

I converted the double to double[][]

Is there any way to make the dataframe with header names as well? Something like:
 
Glenda Karen
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What I did so far is make multiple dataframes and then combine them in one merge:

This works but makes a lot of extra dataframes.
 
Piet Souris
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Glenda Karen wrote:Is there any way to make the dataframe with header names as well? Something like:


If you use a double[][] (or int[][]) in your constructor, then that 2D array contains all of the data already. So you only need one gouble[][], and in the constructor you used in your opening post, you then specified all the column names. Isn't that exactly what you wish?

If you cannot use a double[][] in your constructor (for instance when some of the columns are not doubles), then using the constructor with BaseVectors (see thr API and a previous topic) also seems very handy.

And last but least: so you are creating a few extra dataframes. If that does not take hours, or many GigaBytes of memory: so what?  
 
Glenda Karen
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Thanks so much! That's great!
And yes true point, not such a big deal to make a few dataframes, I actually ended up going with that and worked well!
 
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