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2

Try Import["yourfilenamehere", "CSV", CharacterEncoding -> "UTF-8"]


3

if0 = Interpolation[data0, "ExtrapolationHandler" -> {Automatic, "WarningMessage" -> False}]; difference = {#, #2 - if0 @ #} & @@@ data9k; ListLinePlot[{data0, data9k, difference}, ImageSize -> Large, PlotLegends -> {"data0", "data9k", "difference"}] An alternative approach is to use TemporalData: td0 = TemporalData[#2, {#}, ...


2

Currently, BlockchainTransactionData doesn't return that information about the Wolfram blockchain. Note that blockchain functionalities are marked as experimental and that means they can change between releases. Blockchain functions introduced in v12 are more focused on Bitcoin and Ethereum. You can see some examples here: https://www.wolfram.com/language/...


2

Some of those transactions contain expressions added to the Wolfram blockchain using BlockchainPut and you see that as noise. Be aware that blockchain functions are tagged as experimental and may change. V12 introduced new blockchain functionalities focused on Bitcoin and Ethereum. You can see use cases here: https://www.wolfram.com/language/12/blockchain/?...


2

To get a different perspective ListLogLinearPlot[data, PlotRange -> {{0, 60}, {0, 4}}]


4

Perhaps: ListPlot[DeleteDuplicates[data, EuclideanDistance[##] < 1.2 &]] Connecting the dots might be appropriate in some cases: ListLinePlot@data


5

Others might have a much better way, but wanted to look into the residuals and found that fitting two sines one after the other does actually a pretty good job. Also, I scaled your values up by 10^8 just to be sure there are no numerical issues. I'll just leave the code here and see for yourself how many of the parameters you need. pts = {2.29398, 16.4984, ...


5

One can't expect much with estimating 5 coefficients (a, b, c, d, and error variance) with just 7 points. That also makes it difficult for getting convergence to the maximum likelihood or least-squares estimates as good starting values become more critical. (The default starting values are all 1.0 and most of the final estimates are far away from 1.0.) ...


2

ClearAll[aXes, ePilog, parallelCoordsPlot] aXes[ts_: Left, nticks_: {6, 6}, off_: 6, axisstyle_: Directive[Thin, Black], lblstyle_: Directive[14, Black]] := Module[{x = #2, majorminor = {#, Complement[Join @@ #2, #]} & @@ FindDivisions[MinMax@#, nticks]}, {axisstyle, Line[{{x, 0}, {x, 1}}], MapThread[Text[Style[N@#, lblstyle], ...


7

I implemented the package "ParallelCoordinatesPlot.m" for doing this kind of plots and put it in GitHub. I plan to improve it some more. It is especially interesting to have automatic selection of the axes order that produces most discernible results. Import["https://raw.githubusercontent.com/antononcube/MathematicaForPrediction/master/Misc/...


2

Maybe you find this useful... Data = RandomReal[{-1, 1}, {1000, 2}]; m = FindClusters[Data, 5, Method -> "KMeans"]; Show[ VoronoiMesh[Mean /@ m, Transpose[List @@ BoundingRegion[Data]]], ListPlot[m, PlotRange -> List @@ BoundingRegion[Data]], ListPlot[Partition[Mean /@ m, 1]] ]


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