Q02. Gender Identity

Q02. Gender Identity#

Question#

What gender do you identify as?

Choices#

  1. Female

  2. Make

  3. Non-binary

  4. Prefer to self-identify

  5. Prefer not to answer

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 10:03:31.855 | INFO     | titanite.preprocess:categorical_data:135 - Categorize

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

Breakdowns#

names = ["q02", "q03_regional"]
bd = ti.analysis.breakdowns(data, names)
bd.graph.cols(1)

names = ["q02", "q05"]
bd = ti.analysis.breakdowns(data, names)
bd.graph.cols(1)

names = ["q02", "q06"]
bd = ti.analysis.breakdowns(data, names)
bd.graph.cols(1)

  • 男性は分野ごとにばらつきがある

  • 女性は割とフラット

names = ["q02", "q07"]
bd = ti.analysis.breakdowns(data, names)
bd.graph.cols(1)

names = ["q02", "q08"]
bd = ti.analysis.breakdowns(data, names)
bd.graph.cols(1)

names = ["q02", "q09"]
bd = ti.analysis.breakdowns(data, names)
bd.graph.cols(1)

names = ["q02", "q11"]
bd = ti.analysis.breakdowns(data, names)
bd.graph.cols(1)

names = ["q02", "q12_genderbalance"]
bd = ti.analysis.breakdowns(data, names)
bd.graph.cols(1)
Contents