Dataset: Population By Age And Sex From 1950 To 2100

About this Dataset

Population By Age And Sex From 1950 To 2100

Context

UN shared world population data from 1950 to 2100. It's not just contains country names, year, age groups, sex and totals. It also contains different perspective of country groups. Geographical groups, Economical groups and lots of different grouping perspectives that data contains.

Content

UN raw data contains more than 1.4 billion rows. Each row contains locationId, location, time, age group, male and female population. For each year, each location there are 21 rows exists. It makes working on data a little bit harder.

I pivoted the data. Generate separate columns for each age groups and sex. At the end 1.4 billion rows turned to 66.893 rows.

Acknowledgements

PopMale_x_y > Male population in the age group (from x to y) (thousands)
PopFemale_x_y > Female population in the age group (from x to y) (thousands)
PopTotal_x_y > Total population in the age group (from x to y) (thousands)

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

Tables

Populationbyagesex

@kaggle.ahmethoso_wpp_population_by_age_and_sex.populationbyagesex

29.53 MB
66893 rows
71 columns


CREATE TABLE populationbyagesex (
  "id" BIGINT,
  "locid" BIGINT,
  "location" VARCHAR,
  "time" BIGINT,
  "popmale_0_4" DOUBLE,
  "popfemale_0_4" DOUBLE,
  "poptotal_0_4" DOUBLE,
  "popmale_5_9" DOUBLE,
  "popfemale_5_9" DOUBLE,
  "poptotal_5_9" DOUBLE,
  "popmale_10_14" DOUBLE,
  "popfemale_10_14" DOUBLE,
  "poptotal_10_14" DOUBLE,
  "popmale_15_19" DOUBLE,
  "popfemale_15_19" DOUBLE,
  "poptotal_15_19" DOUBLE,
  "popmale_20_24" DOUBLE,
  "popfemale_20_24" DOUBLE,
  "poptotal_20_24" DOUBLE,
  "popmale_25_29" DOUBLE,
  "popfemale_25_29" DOUBLE,
  "poptotal_25_29" DOUBLE,
  "popmale_30_34" DOUBLE,
  "popfemale_30_34" DOUBLE,
  "poptotal_30_34" DOUBLE,
  "popmale_35_39" DOUBLE,
  "popfemale_35_39" DOUBLE,
  "poptotal_35_39" DOUBLE,
  "popmale_40_44" DOUBLE,
  "popfemale_40_44" DOUBLE,
  "poptotal_40_44" DOUBLE,
  "popmale_45_49" DOUBLE,
  "popfemale_45_49" DOUBLE,
  "poptotal_45_49" DOUBLE,
  "popmale_50_54" DOUBLE,
  "popfemale_50_54" DOUBLE,
  "poptotal_50_54" DOUBLE,
  "popmale_55_59" DOUBLE,
  "popfemale_55_59" DOUBLE,
  "poptotal_55_59" DOUBLE,
  "popmale_60_64" DOUBLE,
  "popfemale_60_64" DOUBLE,
  "poptotal_60_64" DOUBLE,
  "popmale_65_69" DOUBLE,
  "popfemale_65_69" DOUBLE,
  "poptotal_65_69" DOUBLE,
  "popmale_70_74" DOUBLE,
  "popfemale_70_74" DOUBLE,
  "poptotal_70_74" DOUBLE,
  "popmale_75_79" DOUBLE,
  "popfemale_75_79" DOUBLE,
  "poptotal_75_79" DOUBLE,
  "popmale_80_84" DOUBLE,
  "popfemale_80_84" DOUBLE,
  "poptotal_80_84" DOUBLE,
  "popmale_85_89" DOUBLE,
  "popfemale_85_89" DOUBLE,
  "poptotal_85_89" DOUBLE,
  "popmale_90_94" DOUBLE,
  "popfemale_90_94" DOUBLE,
  "poptotal_90_94" DOUBLE,
  "popmale_95_99" DOUBLE,
  "popfemale_95_99" DOUBLE,
  "poptotal_95_99" DOUBLE,
  "popmale_100plus" DOUBLE,
  "popfemale_100plus" DOUBLE,
  "poptotal_100plus" DOUBLE,
  "popmale" DOUBLE,
  "popfemale" DOUBLE,
  "poptotal" DOUBLE,
  "yeardatacompleted" BIGINT
);