path: root/notebooks/2024-11-29-election-petition.py

# /// script
# requires-python = ">=3.13"
# dependencies = [
#     "marimo",
#     "matplotlib==3.10.5",
#     "pandas==2.3.1",
#     "python-dotenv==1.1.1",
#     "seaborn==0.13.2",
# ]
# ///

import marimo

__generated_with = "0.14.17"
app = marimo.App()


@app.cell(hide_code=True)
def _(mo):
    mo.md(
        r"""
    # Analysing the Petition for a General Election

    2024 general election results where downloaded from [here](https://commonslibrary.parliament.uk/research-briefings/cbp-10009/) on 29 November 2024. Petition results were downloaded using the following command on 29 November 2024:

    ```shell
    curl https://petition.parliament.uk/petitions/700143.json | jq > data/election-petition-results.json
    ```
    """
    )
    return


@app.cell
def _():
    import json
    from pathlib import Path

    import marimo as mo
    import matplotlib.pyplot as plt
    import pandas as pd
    import seaborn as sns
    from dotenv import find_dotenv

    ROOT_DIR = Path(find_dotenv("pyproject.toml")).parent
    DATA_DIR = ROOT_DIR / "data"
    ELECTION_RESULTS_PATH = DATA_DIR / "election-petition" / "HoC-GE2024-results-by-candidate.csv"
    PETITION_RESULTS_PATH = DATA_DIR / "election-petition" / "election-petition-results.json"

    TOP_PARTIES = [
        "Conservative",
        "Labour",
        "Liberal Democrat",
        "Reform UK",
        "Green",
        "Scottish National Party",
        "Plaid Cymru",
        "Independent",
    ]

    PARTY_COLOURS = {
        "Conservative": "#0087DC",
        "Labour": "#E4003B",
        "Liberal Democrat": "#FAA61A",
        "Reform UK": "#12B6CF",
        "Green": "#02A95B",
        "Scottish National Party": "#FDF38E",
        "Plaid Cymru": "#005B54",
        "Independent": "grey",
    }

    sns.set_style("ticks")
    sns.set_palette(PARTY_COLOURS.values())
    return (
        ELECTION_RESULTS_PATH,
        PETITION_RESULTS_PATH,
        TOP_PARTIES,
        json,
        mo,
        pd,
        sns,
    )


@app.cell
def _(ELECTION_RESULTS_PATH, PETITION_RESULTS_PATH, json, pd):
    with PETITION_RESULTS_PATH.open("r", encoding="utf-8") as f:
        petition_results_json = json.load(f)

    petition_results_df = (
        pd.DataFrame.from_records(
            petition_results_json["data"]["attributes"]["signatures_by_constituency"],
            exclude=["name", "mp"],
        )
        .rename(columns={"ons_code": "ons_id"})
    )

    df = (
        pd.read_csv(
            ELECTION_RESULTS_PATH,
            header=0,
            usecols=[
                "ONS ID",
                "Constituency name",
                "Region name",
                "Party name",
                "Votes",
                "Share",
            ]
        )
        .rename(columns={
            "ONS ID": "ons_id",
            "Constituency name": "constituency",
            "Region name": "region",
            "Party name": "party",
            "Votes": "votes",
            "Share": "share",
        })
        .replace({"party": {"Labour and Co-operative": "Labour"}})  # Labour in Wales
        .merge(petition_results_df, on="ons_id", how="left")
    )

    df = (
        df
        .join(df.groupby("ons_id")["votes"].sum().rename("total_votes"), how="left", on="ons_id")
        .assign(signatures_per_vote=lambda x: x["signature_count"] / x["total_votes"])
    )

    df = (
        df.merge(
            df
            .sort_values("votes", ascending=False)
            .groupby("ons_id")
            .first()[["party", "votes"]]
            .assign(winner=True)
            .reset_index(),
            on=["ons_id", "party", "votes"],
            how="left",
        )
        .fillna({"winner": False})
    )

    print(f'Total petition signatures: {petition_results_json["data"]["attributes"]["signature_count"]:,d}')
    print(f'Total petition signatures from per-consituency counts: {df.groupby("ons_id")["signature_count"].max().sum():,d}')
    return (df,)


@app.cell
def _(df):
    df
    return


@app.cell
def _(df, sns):
    ax = sns.histplot(
        df.groupby("ons_id")["signature_count"].max()
    )
    ax.set(xlabel="Signatures", ylabel="Constituencies", title="Distribution of Petition Signatures by Constituency")
    ax
    return


@app.cell
def _(mo):
    mo.md(r"""### Signature Distribution""")
    return


@app.cell
def _(TOP_PARTIES, df, sns):
    _top_n = 3
    _g = sns.displot(
        df[df['winner'] & df['party'].isin(TOP_PARTIES[:_top_n])],
        kind='hist',
        x='signature_count',
        hue='party',
        hue_order=TOP_PARTIES[:_top_n],
        col='party',
        col_order=TOP_PARTIES[:_top_n],
        height=3,
        legend=False,
    ).set_titles('{col_name}').set_axis_labels('Signatures', 'Constituencies')
    _g.figure
    return


@app.cell
def _(mo):
    mo.md(r"""### Signatures per 2024 General Election Vote""")
    return


@app.cell
def _(TOP_PARTIES, df, sns):
    _top_n = 3
    _g = (
        sns.displot(
            df[df["winner"] & df["party"].isin(TOP_PARTIES[:_top_n])],
            kind="hist",
            x="signatures_per_vote",
            hue="party",
            hue_order=TOP_PARTIES[:_top_n],
            col="party",
            col_order=TOP_PARTIES[:_top_n],
            height=3,
            legend=False,
        )
        .set_titles("{col_name}")
        .set_axis_labels("Signatures per Vote", "Constituencies")
    )
    _g.figure
    return


@app.cell
def _(mo):
    mo.md(r"""### Signatures vs. Votes""")
    return


@app.cell
def _(TOP_PARTIES, df, sns):
    _g = sns.lmplot(
        df[df["party"].isin(TOP_PARTIES)],
        x="votes",
        y="signature_count",
        col="party",
        col_wrap=4,
        col_order=TOP_PARTIES,
        hue="party",
        hue_order=TOP_PARTIES,
        height=3,
    ).set_titles("{col_name}").set_axis_labels("2024 General Election Votes", "Petition Signatures")
    _g.figure
    return


@app.cell
def _(mo):
    mo.md(r"""### Signatures vs. Vote Share""")
    return


@app.cell
def _(TOP_PARTIES, df, sns):
    _g = sns.lmplot(
        df[df["party"].isin(TOP_PARTIES)],
        x="share",
        y="signature_count",
        col="party",
        col_wrap=4,
        col_order=TOP_PARTIES,
        hue="party",
        hue_order=TOP_PARTIES,
        height=3,
    ).set_titles("{col_name}").set_axis_labels("2024 General Election Vote Share", "Petition Signatures")
    _g.figure
    return


@app.cell
def _(mo):
    mo.md(r"""### Signatures per Vote vs. Vote Share""")
    return


@app.cell
def _(TOP_PARTIES, df, sns):
    _g = sns.lmplot(
        df[df["party"].isin(TOP_PARTIES)],
        x="share",
        y="signatures_per_vote",
        col="party",
        col_wrap=4,
        col_order=TOP_PARTIES,
        hue="party",
        hue_order=TOP_PARTIES,
        height=3,
    ).set_titles("{col_name}").set_axis_labels("2024 General Election Vote Share", "Petition Signatures per Vote")
    _g.figure
    return


if __name__ == "__main__":
    app.run()