I try to learn some data science in Julia from Julia Academy’s Julia for Data Science and simple benchmark from it is quite surprising for me. I made its code below compact and probably use more packages that it need, just because I don’t know which are needed.
using BenchmarkTools
using DataFrames
using DelimitedFiles
using CSV
using XLSX
using Downloads
P = Downloads.download("https://raw.githubusercontent.com/nassarhuda/easy_data/master/programming_languages.csv",
"programming_languages.csv")
@btime P, H = readdlm("programming_languages.csv", ','; header=true);
@btime C = CSV.read("programming_languages.csv", DataFrame);
I get result like this.
125.375 μs (325 allocations: 51.19 KiB)
229.006 μs (428 allocations: 40.95 KiB)
Benchmarks show in the Jupyter notebook are below.
87.708 μs (325 allocations: 51.19 KiB)
35.417 μs (227 allocations: 22.02 KiB)
I have CSV.jl v0.9.11 and here is my machine info.
Julia Version 1.6.4
Commit 35f0c911f4 (2021-11-19 03:54 UTC)
Platform Info:
OS: Linux (x86_64-pc-linux-gnu)
CPU: Intel(R) Core(TM) i5-3210M CPU @ 2.50GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-11.0.1 (ORCJIT, ivybridge)