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 titanite as ti

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

Pandas: 3.0.2
Titanite: 0.7.0

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()

2026-05-11 20:43:17.930 | ERROR    | titanite.config:load_toml:87 - File not found: ../../../sandbox/config.toml

---------------------------------------------------------------------------
Exit                                      Traceback (most recent call last)
Cell In[2], line 4
      1 f_cfg = "../../../sandbox/config.toml"
      2 f_csv = "../../../data/test_data/prepared_data.csv"
      3 d = ti.Data(read_from=f_csv, load_from=f_cfg)
----> 4 config = d.config()
      5 data = d.read()

File ~/work/surveys/surveys/titanite/config.py:261, in Data.config(self)
    259 def config(self):
    260     c = Config(load_from=self.load_from)
--> 261     return c.load_config()

File ~/work/surveys/surveys/titanite/config.py:95, in Config.load_config(self)
     94 def load_config(self) -> dict:
---> 95     config = self.load_toml()
     96     return config

File ~/work/surveys/surveys/titanite/config.py:88, in Config.load_toml(self)
     86 if not fname.exists():
     87     logger.error(f"File not found: {fname}")
---> 88     raise typer.Exit(code=1)
     90 with fname.open("rb") as f:
     91     config = tomllib.load(f)

Exit: 

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,
)