# Turn a dictionary of currencies (e.g. "USD"=>"name"=>"United States dollar","symbol"=>"$") into a vector of strings. function currencies_from_dict(dict::Dict) return [string(v["name"], " (", get(v, "symbol", "?"), ")") for (_, v) in dict] end
Runs `analyze_country` on every element of `codes` and returns a DataFrame. """ function batch_analyze(codes::VectorString; gdp_table=nothing) rows = [] for c in codes try rpt = analyze_country(c; gdp_table=gdp_table) push!(rows, ( name = rpt.info.name, iso2 = rpt.info.iso2, iso3 = rpt.info.iso3, pop = rpt.info.population, area_km2 = rpt.info.area_km2, density = rpt.density, gdp_per_cap = rpt.gdp_per_capita, econ_weight = rpt.economic_weight )) catch e @warn "Failed for $c: $e" end end return DataFrame(rows) end julia pais anal
data = JSON3.read(String(resp.body))[1] # REST Countries returns an array; we take the first match # Turn a dictionary of currencies (e
* `name_or_code` can be a common name (“France”), an ISO‑2 code (“FR”), or an ISO‑3 code (“FRA”). """ function batch_analyze(codes::VectorString
# Optional GDP integration gdp_per_capita = missing econ_weight = missing if gdp_table !== nothing if haskey(gdp_table, info.iso3) gdp_per_capita = gdp_table[info.iso3] econ_weight = gdp_per_capita * info.population else @warn "GDP per‑capita not found for ISO‑3 code $(info.iso3)." end end