CODEWITHSUNDEEP
csvjulia
K. Claesson
K. Claesson

Reputation: 659

Importing a CSV file as a matrix

I would like to import a CSV file (file.csv) as a matrix in Julia to plot it as a heatmap using GR. My CSV file contains 255 rows and 255 entries on each row. Here are some entires from the CSV file to illustrate the format of the rows:

file.csv

-1.838713563526794E-8;-1.863045549663876E-8;-2.334704481052452E-8 ...
-1.7375447279939282E-8;-1.9194929690414267E-8;-2.0258124812468942E-8; ...
⋮
-1.1706980663321613E-8;-1.6244768693064608E-8;-5.443335580296977E-9; ...

Note: The elipsis (...) are not part of the CSV file, rather they indicate that entires have been omitted.

I have tried importing the file as a matrix using the following line m = CSV.read("./file.csv"), but this results in a 255 by 1 vector rather than the 255 by 255 matrix. Does anyone know of an effective way to import CSV files as matrices in Julia?

Upvotes: 2

Views: 3740

Answers (4)

branden_burger
branden_burger

Reputation: 339

While the readdlm answer by @user:1269567 is simple and clean, it may struggle with very large text files (probably a bad idea to store large numeric data as text anyway, but that's another story) -- which is how I came across this question. An efficient way using no other packages besides CSV.jl is the following:

using CSV
m = CSV.read("./file.csv", CSV.Tables.matrix; header=false)

as of CSV v0.10.9, assuming that there are no headers in the file.

Upvotes: 0

a2k42
a2k42

Reputation: 1275

2022 Answer

Not sure if there has been a change to CSV.jl, however, if I do CSV.read("file.csv") it will error

provide a valid sink argument, like 'using DataFrames; CSV.read(source, DataFrame)'

You can however use the fact that it wants any Tables.jl compatible type:

using CSV, Tables
M = CSV.read("file.csv", Tables.matrix, header=0)

Upvotes: 6

hckr
hckr

Reputation: 5583

m = CSV.read("./file.csv") returns a DataFrame.

If CSV.jl reads the file correctly so that all the columns of m are of type Float64 containing no missings, then you can convert m to a Float64 matrix with Matrix{Float64}(m), or obtain the matrix with one line:

m = Matrix{Float64}(CSV.read("./file.csv", header=0, delim=';'))
# or with piping syntax
m = CSV.read("./file.csv", header=0, delim=';') |> Matrix{Float64}

readdlm, though, should normally be enough and first solution to go for such simple CSV files like yours.

Upvotes: 3

Bogumił Kamiński
Bogumił Kamiński

Reputation: 69899

You can use

using DelimitedFiles
m = readdlm("./file.csv", ';', Float64)

(last argument specifying type can be omitted if you want Float64)

Upvotes: 6

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