Q04. Hometown

Q04. Hometown#

Question#

Which geographical region do you most strongly associate with?

Choices#

  1. Asia / Japan

  2. Asia / Eastern Asia

  3. Asia / South-Eastern Asia

  4. Asia / Southern Asia

  5. Asia / Central Asia

  6. Asia / Western Asia

  7. Africa / Northern Africa

  8. Africa / Western Africa

  9. Africa / Middle Africa

  10. Africa / Eastern Africa

  11. Africa / Southern Africa

  12. Europe / North Europe

  13. Europe / West Europe

  14. Europe / Central Europe

  15. Europe / East Europe

  16. Europe / South Europe

  17. America / North America

  18. America / Central America

  19. America / South America

  20. Oceania

  21. Prefer not to answer

These choices are based on the United Nations Geoscheme.

Responses#

import pandas as pd
import hvplot.pandas
import titanite as ti

print(f"Pandas: {pd.__version__}")
print(f"Titanite: {ti.__version__}")

%opts magic unavailable (pyparsing cannot be imported)
%compositor magic unavailable (pyparsing cannot be imported)

Pandas: 2.2.3
Titanite: 0.6.0

f_cfg = "../../../sandbox/config.toml"
f_csv = "../../../data/test_data/prepared_data.csv"
d = ti.Data(read_from=f_csv, load_from=f_cfg)
config = d.config()
data = d.read()

2025-05-12 10:03:43.265 | INFO     | titanite.preprocess:categorical_data:135 - Categorize

data["q04"].value_counts().hvplot.bar(
    title="Q04: Hometown",
    xlabel="Hometown",
    ylabel="Entries",
    width=600,
    height=600,
    rot=90,
)

data["q04_regional"].value_counts().hvplot.bar(
    title="Q04: Hometown",
    xlabel="Hometown (Regional)",
    ylabel="Entries",
    width=600,
    height=600,
    rot=90,
)

data["q04_subregional"].value_counts().hvplot.bar(
    title="Q04: Hometown",
    xlabel="Hometown (Sub-Regional)",
    ylabel="Entries",
    width=600,
    height=600,
    rot=90,
)
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