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Datuak inportatzen#
using DataFrames
using CSV
wp = DataFrame(CSV.File("./world_pop.csv"))
first(wp, 5)5×8 DataFrame
| Row | Country | Population 2024 | Population 2023 | Area (km2) | Density (/km2) | Growth Rate | World % | World Rank |
|---|---|---|---|---|---|---|---|---|
| String | Int64 | Int64 | String7 | Float64 | Float64 | Float64? | Int64 | |
| 1 | India | 1441719852 | 1428627663 | 3M | 485.0 | 0.0092 | 0.1801 | 1 |
| 2 | China | 1425178782 | 1425671352 | 9.4M | 151.0 | -0.0003 | 0.178 | 2 |
| 3 | United States | 341814420 | 339996563 | 9.1M | 37.0 | 0.0053 | 0.0427 | 3 |
| 4 | Indonesia | 279798049 | 277534122 | 1.9M | 149.0 | 0.0082 | 0.035 | 4 |
| 5 | Pakistan | 245209815 | 240485658 | 770.9K | 318.0 | 0.0196 | 0.0306 | 5 |
describe(wp)8×7 DataFrame
| Row | variable | mean | min | median | max | nmissing | eltype |
|---|---|---|---|---|---|---|---|
| Symbol | Union… | Any | Union… | Any | Int64 | Type | |
| 1 | Country | Afghanistan | Zimbabwe | 0 | String | ||
| 2 | Population 2024 | 3.46886e7 | 526 | 5.62636e6 | 1441719852 | 0 | Int64 |
| 3 | Population 2023 | 3.43744e7 | 518 | 5.6439e6 | 1428627663 | 0 | Int64 |
| 4 | Area (km2) | 1.1K | < 1 | 0 | String7 | ||
| 5 | Density (/km2) | 453.788 | 0.14 | 98.5 | 21674.0 | 0 | Float64 |
| 6 | Growth Rate | 0.00920043 | -0.0309 | 0.00795 | 0.0483 | 0 | Float64 |
| 7 | World % | 0.00444649 | 0.0 | 0.00075 | 0.1801 | 6 | Union{Missing, Float64} |
| 8 | World Rank | 117.5 | 1 | 117.5 | 234 | 0 | Int64 |
names(wp)8-element Vector{String}:
"Country"
"Population 2024"
"Population 2023"
"Area (km2)"
"Density (/km2)"
"Growth Rate"
"World %"
"World Rank"
Datuen eraldaketa (Data wrangling)#
wp.id = 1:nrow(wp)
first(wp, 5)5×9 DataFrame
| Row | Country | Population 2024 | Population 2023 | Area (km2) | Density (/km2) | Growth Rate | World % | World Rank | id |
|---|---|---|---|---|---|---|---|---|---|
| String | Int64 | Int64 | String7 | Float64 | Float64 | Float64? | Int64 | Int64 | |
| 1 | India | 1441719852 | 1428627663 | 3M | 485.0 | 0.0092 | 0.1801 | 1 | 1 |
| 2 | China | 1425178782 | 1425671352 | 9.4M | 151.0 | -0.0003 | 0.178 | 2 | 2 |
| 3 | United States | 341814420 | 339996563 | 9.1M | 37.0 | 0.0053 | 0.0427 | 3 | 3 |
| 4 | Indonesia | 279798049 | 277534122 | 1.9M | 149.0 | 0.0082 | 0.035 | 4 | 4 |
| 5 | Pakistan | 245209815 | 240485658 | 770.9K | 318.0 | 0.0196 | 0.0306 | 5 | 5 |
colnames = [:country, :pop2024, :pop2023, :area, :density, :growth_rate, :world_perc, :world_rank, :id]
rename!(wp, colnames)
describe(wp)9×7 DataFrame
| Row | variable | mean | min | median | max | nmissing | eltype |
|---|---|---|---|---|---|---|---|
| Symbol | Union… | Any | Union… | Any | Int64 | Type | |
| 1 | country | Afghanistan | Zimbabwe | 0 | String | ||
| 2 | pop2024 | 3.46886e7 | 526 | 5.62636e6 | 1441719852 | 0 | Int64 |
| 3 | pop2023 | 3.43744e7 | 518 | 5.6439e6 | 1428627663 | 0 | Int64 |
| 4 | area | 1.1K | < 1 | 0 | String7 | ||
| 5 | density | 453.788 | 0.14 | 98.5 | 21674.0 | 0 | Float64 |
| 6 | growth_rate | 0.00920043 | -0.0309 | 0.00795 | 0.0483 | 0 | Float64 |
| 7 | world_perc | 0.00444649 | 0.0 | 0.00075 | 0.1801 | 6 | Union{Missing, Float64} |
| 8 | world_rank | 117.5 | 1 | 117.5 | 234 | 0 | Int64 |
| 9 | id | 117.5 | 1 | 117.5 | 234 | 0 | Int64 |
wp_clean = select!(wp, :id, :country, :pop2024, :growth_rate);Kontuz ibili, harridura-markak (!) jatorrizko taula ere aldatzen baitu! Gogoratu ez garela kopiak egiten ari, memoriako objektu berberetara zuzentzen duten erakusle (pointer) berriak sortzen baizik.
describe(wp_clean)4×7 DataFrame
| Row | variable | mean | min | median | max | nmissing | eltype |
|---|---|---|---|---|---|---|---|
| Symbol | Union… | Any | Union… | Any | Int64 | DataType | |
| 1 | id | 117.5 | 1 | 117.5 | 234 | 0 | Int64 |
| 2 | country | Afghanistan | Zimbabwe | 0 | String | ||
| 3 | pop2024 | 3.46886e7 | 526 | 5.62636e6 | 1441719852 | 0 | Int64 |
| 4 | growth_rate | 0.00920043 | -0.0309 | 0.00795 | 0.0483 | 0 | Float64 |
describe(wp)4×7 DataFrame
| Row | variable | mean | min | median | max | nmissing | eltype |
|---|---|---|---|---|---|---|---|
| Symbol | Union… | Any | Union… | Any | Int64 | DataType | |
| 1 | id | 117.5 | 1 | 117.5 | 234 | 0 | Int64 |
| 2 | country | Afghanistan | Zimbabwe | 0 | String | ||
| 3 | pop2024 | 3.46886e7 | 526 | 5.62636e6 | 1441719852 | 0 | Int64 |
| 4 | growth_rate | 0.00920043 | -0.0309 | 0.00795 | 0.0483 | 0 | Float64 |
Azpimultzoak hautatzea (Subsetting)#
Kate bat (adibidez, herrialde baten izena) edo balio bat presente dagoen egiaztatzeko in operadorea erabili daiteke.
"Tanzania" in wp.countrytrue
Herrialde zehatz bat zein indizetan dagoen jakiteko findall() edo findfirst() funtzioak erabili ditzakegu.
# funtzio anonimoekin
findall(x -> x == "Tanzania", wp.country)
# edo == funtzioa erabiliz
findall(==("Tanzania"), wp.country)1-element Vector{Int64}:
21
Horrek gure dataframe-aren azpimultzoak hainbat modutan hautatzeko aukera ematen digu:
# findall() edo findfirst() erabiliz
wp[findall(==("Tanzania"), wp.country), :]1×4 DataFrame
| Row | id | country | pop2024 | growth_rate |
|---|---|---|---|---|
| Int64 | String | Int64 | Float64 | |
| 1 | 21 | Tanzania | 69419073 | 0.0294 |
# edo "broadcasting" bidez, R-ko sintaxiaren antzera
wp[wp.country .== "Tanzania", :]1×4 DataFrame
| Row | id | country | pop2024 | growth_rate |
|---|---|---|---|---|
| Int64 | String | Int64 | Float64 | |
| 1 | 21 | Tanzania | 69419073 | 0.0294 |
wp.country .== "Tanzania" adierazpenak 0 eta 1 balioez osatutako bektore bat itzultzen du, eta bektore hori errenkadak hautatzeko erabiltzen da.