Julia Pais Anal May 2026

# ---- 2️⃣ Build the CountryInfo struct --------------------------- info = CountryInfo( get(data["name"], "common", "unknown"), get(data["name"], "official", "unknown"), get(data, "cca2", "??"), get(data, "cca3", "???"), get(data, "population", 0), get(data, "area", 0.0), get(data, "capital", String[]), get(data, "region", "unknown"), get(data, "subregion", "unknown"), languages_from_dict(get(data, "languages", Dict())), currencies_from_dict(get(data, "currencies", Dict())), get(data["flags"], "png", "") )

You can extend any of the steps (e.g., add more fields, plug in a different data source, or compute extra statistics). # -------------------------------------------------------------- # Country analysis feature for Julia # -------------------------------------------------------------- using HTTP using JSON3 using DataFrames # optional, only needed if you want tabular output using Statistics # for mean, median, etc. using Printf # for nice formatting julia pais anal

println("\n--- Country analysis demo ------------------------------------------------\n") In real life you would # load this

if resp.status != 200 error("Could not fetch data for \"$(name_or_code)\" (HTTP $(resp.status)).") end 69_287.0 ) return CountryReport(info

""" CountryReport

# ---------------------------------------------------------------- # 4️⃣ Example usage # ---------------------------------------------------------------- if abspath(PROGRAM_FILE) == @__FILE__ # run only when this file is executed directly # Example: a tiny GDP table (USD per‑capita). In real life you would # load this from a CSV, an API, or a more complete dataset. sample_gdp = Dict( "FRA" => 41_463.0, "DEU" => 46_215.0, "JPN" => 40_247.0, "BRA" => 7_498.0, "USA" => 69_287.0 )

return CountryReport(info, density, gdp_per_capita, econ_weight) end