The following code
col_ind = 1
insertcols!(df, col_ind, :Rata_Die => zeros(Int64, record_count); makeunique = true )
Unix time date format is used in many applications, including Yahoo finance.
using Dates, Printf
unix_date = @sprintf("%.0f", Dates.datetime2unix(Dates.now()))
println( unix_date ) # String
Output:
1603013077
> jupyter notebook --notebook-dir 'R:'
Sidenote:
In case you wonder why I am putting up with Windows 10 and not working on my MacOS, I wanted to try the NVidia GPU performance with Julia in the Jupyter notebook.
Syntax explained:
dataA = data[isequal.(data.Treatment, "A"), : ]
first(dataA, 6)
6 rows × 5 columns
| Age | WCC | CRP | Treatment | Result | |
|---|---|---|---|---|---|
| Int64 | Float64 | Int64 | String | String | |
| 1 | 37 | 10.2 | 30 | A | Worse |
| 2 | 67 | 10.4 | 70 | A | Worse |
| 3 | 42 | 10.4 | 100 | A | Static |
| 4 | 56 | 12.5 | 80 | A | Worse |
| 5 | 60 | 9.5 | 0 | A | Static |
| 6 | 62 | 13.7 | 10 | A | Worse |
# dataA = data[data[:Treatment] .== "A", :]
Warning: `getindex(df::DataFrame, col_ind::ColumnIndex)` is deprecated, use `df[!, col_ind]` instead.
Here are the Julia packages I have been using so far, I keep this list in case I need to re-install Julia.
Un-comment, install and pre-compile the package you need:
import Pkg
# Pkg.add("Atom")
# using Atom
# Pkg.add("BinDeps")
# using BinDeps
# Pkg.add("CSV")
# using CSV
# Pkg.add("CUDAapi")
# using CUDAapi
# Pkg.add("Calculus")
# using Calculus
# Pkg.add("CodecZlib")
# using CodecZlib
# Pkg.add("Codecs")
# using Codecs
# Pkg.add("ColorTypes")
# using ColorTypes
# Pkg.add("Colors")
# using Colors
# Pkg.add("Combinatorics")
# using Combinatorics
# Pkg.add("Compat")
# using Compat
# Pkg.add("Compose")
# using Compose
# Pkg.add("Contour")
# using Contour
# Pkg.add("DataArrays")
# using DataArrays
# Pkg.add("DataFrames")
# using DataFrames
# Pkg.add("DataStructures")
# using DataStructures
# Pkg.add("Distances")
# using Distances
# Pkg.add("Distributions")
# using Distributions
# Pkg.add("FileIO")
# using FileIO
# Pkg.add("FixedPointNumbers")
# using FixedPointNumbers
# Pkg.add("Flux")
# using Flux
# Pkg.add("ForwardDiff")
# using ForwardDiff
# Pkg.add("GLM")
# using GLM
# Pkg.add("GR")
# using GR
# Pkg.add("GZip")
# using GZip
# Pkg.add("GeometryBasics")
# using GeometryBasics
# Pkg.add("Hexagons")
# using Hexagons
# Pkg.add("HypothesisTests")
# using HypothesisTests
# Pkg.add("IJulia")
# using IJulia
# Pkg.add("JSON")
# using JSON
# Pkg.add("Juno")
# using Juno
# Pkg.add("KernelDensity")
# using KernelDensity
# Pkg.add("Knet")
# using Knet
# Pkg.add("Lathe")
# using Lathe
# Pkg.add("Loess")
# using Loess
# Pkg.add("Measures")
# using Measures
# Pkg.add("Metalhead")
# using Metalhead
# Pkg.add("NaNMath")
# using NaNMath
# Pkg.add("OffsetArrays")
# using OffsetArrays
# Pkg.add("Optim")
# using Optim
# Pkg.add("PDMats")
# using PDMats
# Pkg.add("Parameters")
# using Parameters
# Pkg.add("Plots")
# using Plots
# Pkg.add("Polynomials")
# using Polynomials
# Pkg.add("Primes")
# using Primes
# Pkg.add("PyCall")
# using PyCall
# Pkg.add("PyPlot")
# using PyPlot
# Pkg.add("QuartzImageIO")
# using QuartzImageIO
# Pkg.add("RDatasets")
# using RDatasets
# Pkg.add("Reexport")
# using Reexport
# Pkg.add("Rmath")
# using Rmath
# Pkg.add("Roots")
# using Roots
# Pkg.add("Showoff")
# using Showoff
# Pkg.add("SortingAlgorithms")
# using SortingAlgorithms
# Pkg.add("StaticArrays")
# using StaticArrays
# Pkg.add("StatsBase")
# using StatsBase
# Pkg.add("StatsFuns")
# using StatsFuns
# Pkg.add("StatsPlots")
# using StatsPlots
# Pkg.add("TensorFlow")
# using TensorFlow
# Pkg.add("TextAnalysis")
# using TextAnalysis
# Pkg.add("TinySegmenter")
# using TinySegmenter
# Pkg.add("URIParser")
# using URIParser
# Pkg.add("UnicodePlots")
# using UnicodePlots
# Pkg.add("WordTokenizers")
# using WordTokenizers
# Pkg.add("Dates")
# using Dates
# Pkg.add("DelimitedFiles")
# using DelimitedFiles
# Pkg.add("SHA")
# using SHA
# Pkg.add("Statistics")
# using Statistics
Counting Days since...
https://en.wikipedia.org/wiki/Rata_Die
# col1 is an exported column of dates
lastDay = 54 # last row in the column sorted by the date
daysSince(x) = Dates.datetime2rata(x) - Dates.datetime2rata(col1[lastDay])
arrayDays = Array{Int64}(undef, lastDay) # array size is 54
for i = 1:lastDay
arrayDays[i] = daysSince(col1[i])
end
arrayDays
Output:
54-element Array{Int64,1}:
613
606
599
592
582
575
568
554
547
540
533
526
512
⋮
Flux is a machine learning library writer in Julia language.
(v1.3) pkg> add Flux
When trying to execute:
wikiEVDraw = readdlm("wikipediaEVDraw.csv", ',') # getting quotes right is important!
I get the following error:
UndefVarError: readdlm not defined.
help?> readdlm search: readdir Couldn't find readdlm Perhaps you meant readdir, read, read!, real or readchomp No documentation found. Binding readdlm does not exist.
In this case use:
using DelimitedFileswikiEVDraw = readdlm("wikipediaEVDraw.csv", ',') # getting quotes right is important!
Here is a Julia language library to operate with AWS Lamda functions:
https://github.com/samoconnor/AWSLambda.jl#deploy-a-julia-lambda-function
Way to create an Array in Julia language:
array18 = fill(18, 4) # values of 18, 4 rows
4-element Array{Int64,1}:
18
18
18
18A = Array{Float64,2}(undef, 4,3)
# Array of floats, 2-dimentional, populate with undefiled data, 4-rows, 3-columns
4×3 Array{Float64,2}:
2.40692e-314 5.0e-324 1.5e-323
2.39671e-314 2.40692e-314 1.5e-323
2.40692e-314 2.40692e-314 1.0e-323 2.40692e-314 2.40692e-314 2.38725e-314
A[1,1] = 123
A
4×3 Array{Float64,2}:
123.0 5.0e-324 2.5e-323
2.38296e-314 2.38338e-314 2.5e-323
2.38338e-314 2.38338e-314 1.0e-323
2.38338e-314 2.38338e-314 2.64889e-32A = Array{Integer}(undef,2,3)
2×3 Array{Integer,2}:
#undef #undef #undef
#undef #undef #undefA = Array{Int64}(undef,2,3)
2×3 Array{Int64,2}:
4826207760 4826207824 4826207888
4826207792 4826207856 4825801360Reference:
## Uncomment the lines below if you have not run it previously
# import Pkg; Pkg.add("Metalhead")
# import Pkg; Pkg.add("QuartzImageIO")
using Metalhead
using Metalhead: classify
vgg = VGG19()
## local file in this lab's folder
image = load("skunk001.jpeg")
classify(vgg, image)
import Pkg
Pkg.add("CUDAapi")
using CUDAapi
has_CUDA = "false"
if has_cuda()
@info "CUDA is on"
has_CUDA = "true"
end
println("it is ", has_CUDA, " that I have CUDA.")
