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Data

Being able to easily load and process data is a crucial task that can make any data science more pleasant. In this notebook, we will cover most common types often encountered in data science tasks, and we will be using this data throughout the rest of this tutorial.

using BenchmarkTools
using DataFrames
using DelimitedFiles
using CSV
using XLSX
┌ Info: Precompiling DataFrames [a93c6f00-e57d-5684-b7b6-d8193f3e46c0] └ @ Base loading.jl:1260 ┌ Info: Precompiling CSV [336ed68f-0bac-5ca0-87d4-7b16caf5d00b] └ @ Base loading.jl:1260 ┌ Info: Precompiling XLSX [fdbf4ff8-1666-58a4-91e7-1b58723a45e0] └ @ Base loading.jl:1260

🗃️ Get some data

In Julia, it's pretty easy to dowload a file from the web using the download function. But also, you can use your favorite command line commad to download files by easily switching from Julia via the ; key. Let's try both.

Note: download depends on external tools such as curl, wget or fetch. So you must have one of these.

?download
search: download
P = download("https://raw.githubusercontent.com/nassarhuda/easy_data/master/programming_languages.csv",
    "programminglanguages.csv")
"programminglanguages.csv"