WORLD DATA By Country (2020)
Extracted data of Wikipedia's lists of countries by criterion
@kaggle.daniboy370_world_data_by_country_2020
Extracted data of Wikipedia's lists of countries by criterion
@kaggle.daniboy370_world_data_by_country_2020
The kernel aims to extract data from Wikipedia's list of countries by category, and visualize it. The database itself, contains a HUGE amount of analyzed data at different categories, waiting anxiously for someone to present them elegantly ( 😏 ), and compare the trends between the different countries.
The list contains 143 analyses of countries with respect to a specific criterion. Practically, I will refer to several criteria that I found interesting, however the reader is free to add as much as he pleases :
| Criterion | File |
|---|---|
| GDP per capita | df_{GDP} |
| Population growth | df_{Pop-Growth} |
| Life expectancy | df_{Life-exp} |
| Median age | df_{Med-age} |
| Meat consumption | df_{Meat-cons} |
| Sex-ratio | df_{GDP} |
| Suicide rate | df_{Suicide} |
| Urbanization | df_{Urban} |
| Fertility rate | df_{Fertile} |
The well processed data should be able to provide such a visualization ( for example ) :
Choose criterion >> Extract data >> Examine & Clean >> Convert to dataframe >> Visualize :
$$ \text{Enjoy !}$$
CREATE TABLE fertility (
"country" VARCHAR,
"fertility" DOUBLE,
"iso_code" VARCHAR
);CREATE TABLE gdp_per_capita (
"country" VARCHAR,
"gdp_per_capita" DOUBLE,
"iso_code" VARCHAR
);CREATE TABLE life_expectancy (
"country" VARCHAR,
"life_expectancy" DOUBLE,
"iso_code" VARCHAR
);CREATE TABLE meat_consumption (
"country" VARCHAR,
"meat_consumption" DOUBLE,
"iso_code" VARCHAR
);CREATE TABLE median_age (
"country" VARCHAR,
"median_age" DOUBLE,
"iso_code" VARCHAR
);CREATE TABLE population_growth (
"country" VARCHAR,
"population_growth" DOUBLE,
"iso_code" VARCHAR
);CREATE TABLE sex_ratio (
"country" VARCHAR,
"sex_ratio" DOUBLE,
"iso_code" VARCHAR
);CREATE TABLE suicide_rate (
"country" VARCHAR,
"suicide_rate" DOUBLE,
"iso_code" VARCHAR
);CREATE TABLE urbanization_rate (
"country" VARCHAR,
"urbanization_rate" DOUBLE,
"iso_code" VARCHAR
);Anyone who has the link will be able to view this.