Membership History and Tenure

# --- COLLAPSIBLE SETUP BLOCK ---
import pandas as pd
import plotly.express as px
import sys
from pathlib import Path

cwd = Path.cwd().resolve()
sys.path[:0] = [str(cwd), str(cwd.parent)]

from book_utils import api_get, fetch_all, full_name, wrap_label, searchable_table

# --- COLLAPSIBLE ROSTER BLOCK ---
# Pull one page of the legislator roster. full_name is available directly here.
people = pd.DataFrame(fetch_all("people", page_size=100, max_pages=1))
people[["id", "full_name", "first_name", "last_name"]].head()

# --- COLLAPSIBLE MEMBERSHIP-EXPANSION BLOCK ---
# For each sampled legislator, fetch their full record with congress memberships,
# then emit one row per (person, congress) served. This "long" shape makes tenure
# and position analysis straightforward.
SAMPLE_SIZE = 40

membership_rows = []
for person_id in people["id"].dropna().head(SAMPLE_SIZE):
    person, _ = api_get(f"people/{person_id}", include_congresses="true")
    name = full_name(person)
    for membership in person.get("congresses_served") or []:
        membership_rows.append(
            {
                "person_id": person_id,
                "name": name,
                "congress_number": membership.get("congress_number"),
                "congress_ordinal": membership.get("congress_ordinal"),
                "position": membership.get("position"),
            }
        )

memberships = pd.DataFrame(membership_rows)
memberships.head()

# --- COLLAPSIBLE TENURE BLOCK ---
# Collapse memberships to one row per (person, position), counting the distinct
# congresses served. Sorting descending surfaces the longest-serving members.
tenure = (
    memberships.groupby(["person_id", "name", "position"], as_index=False)
    .agg(congresses_served=("congress_number", "nunique"))
    .sort_values("congresses_served", ascending=False)
)
tenure.head(20)

# Client-side searchable tenure table.
tenure_display = tenure.rename(
    columns={"name": "Legislator", "position": "Position", "congresses_served": "Congresses served"}
)[["Legislator", "Position", "Congresses served"]]
searchable_table(tenure_display, caption="Sampled legislator tenure (searchable)")

# --- COLLAPSIBLE TOP-TENURE CHART BLOCK ---
# Take the longest-serving members in the sample and wrap their names for the axis.
top_tenure = tenure.head(15).sort_values("congresses_served").copy()
top_tenure["label"] = top_tenure["name"].map(lambda n: wrap_label(n, width=24))

fig = px.bar(
    top_tenure,
    x="congresses_served",
    y="label",
    color="position",
    orientation="h",
    custom_data=["name"],
    title="Longest-Serving Legislators (Sampled)",
    labels={"congresses_served": "Congresses served", "label": "Legislator", "position": "Position"},
)
fig.update_traces(hovertemplate="%{customdata[0]}<br>Congresses served: %{x}<extra></extra>")
fig.update_layout(margin={"l": 160}, yaxis_title="")
fig

# --- COLLAPSIBLE DISTRIBUTION BLOCK ---
px.histogram(
    tenure,
    x="congresses_served",
    color="position",
    barmode="group",
    title="Sampled Legislator Tenure Distribution",
    labels={"congresses_served": "Congresses served", "count": "Legislators", "position": "Position"},
)