Q01. Age

Q01. Age#

Question#

What is your age?

Choices#

  1. 10s (less than 19 yeas old)

  2. 20s

  3. 30s

  4. 40s

  5. 50s

  6. 60s

  7. 70s

  8. 80s

  9. 90s+ (more than 90 years old)

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 10:03:20.208 | INFO     | titanite.preprocess:categorical_data:135 - Categorize

names = ["q01"]
bd = ti.analysis.breakdowns(data, names)
bd.graph.cols(1)

20代、30代の回答が多かった

Breakdowns#

  • q01と他の質問の内訳を確認した

names = ["q01", "q02"]
bd = ti.analysis.breakdowns(data, names)
bd.graph.cols(1)

  • 30代は、女性の回答比率が高め

names = ["q01", "q03_regional"]
bd = ti.analysis.breakdowns(data, names)
bd.graph.cols(1)

names = ["q01", "q05"]
bd = ti.analysis.breakdowns(data, names)
bd.graph.cols(1)

  • 20代は博士課程

  • 30代はポスドク

  • 40代以上はパーマネント

names = ["q01", "q06"]
bd = ti.analysis.breakdowns(data, names)
bd.graph.cols(1)

names = ["q01", "q07"]
bd = ti.analysis.breakdowns(data, names)
bd.graph.cols(1)

names = ["q01", "q08"]
bd = ti.analysis.breakdowns(data, names)
bd.graph.cols(1)

names = ["q01", "q09"]
bd = ti.analysis.breakdowns(data, names)
bd.graph.cols(1)

names = ["q01", "q11"]
bd = ti.analysis.breakdowns(data, names)
bd.graph.cols(1)

names = ["q01", "q12_genderbalance"]
bd = ti.analysis.breakdowns(data, names)
bd.graph.cols(1)

names = ["q01", "q12_diversity"]
bd = ti.analysis.breakdowns(data, names)
bd.graph.cols(1)

names = ["q01", "q12_equity"]
bd = ti.analysis.breakdowns(data, names)
bd.graph.cols(1)

names = ["q01", "q12_inclusion"]
bd = ti.analysis.breakdowns(data, names)
bd.graph.cols(1)
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