08_tilemap_ChartNet-sample_15e07f5f51b5f852/original.py

# Variation: ChartType=Multi-Axes Chart, Library=matplotlib
import pandas as pd
import matplotlib.pyplot as plt

# ----- Updated Data (minor tweaks, added Colombia) -----
countries = [
    "South Africa", "Thailand", "Venezuela", "Chile", "Argentina",
    "Nigeria", "India", "Kenya", "Bangladesh (SA)", "Ethiopia", "Ghana",
    "Uganda", "Mozambique", "Rwanda", "Eritrea", "Tanzania", "Zambia",
    "Namibia", "Botswana", "Peru", "Colombia"
]

# Slightly adjusted 2024 shares (+0.2 on most, new value for Colombia)
base_2024 = [
    17.1, 57.7, 37.8, 31.3, 25.3,
    44.8, 22.9, 28.4, 34.0, 31.4,
    27.3, 27.8, 31.4, 32.9, 29.3,
    20.8, 25.5, 22.3, 18.8, 30.2,
    28.5
]

region_map = {
    "South Africa": "Sub‑Saharan Africa", "Nigeria": "Sub‑Saharan Africa",
    "Kenya": "Sub‑Saharan Africa", "Ethiopia": "Sub‑Saharan Africa",
    "Ghana": "Sub‑Saharan Africa", "Uganda": "Sub‑Saharan Africa",
    "Mozambique": "Sub‑Saharan Africa", "Rwanda": "Sub‑Saharan Africa",
    "Eritrea": "Sub‑Saharan Africa", "Tanzania": "Sub‑Saharan Africa",
    "Zambia": "Sub‑Saharan Africa", "Namibia": "Sub‑Saharan Africa",
    "Botswana": "Sub‑Saharan Africa",
    "India": "South Asia", "Bangladesh (SA)": "South Asia", "Thailand": "South Asia",
    "Venezuela": "Latin America", "Chile": "Latin America", "Argentina": "Latin America",
    "Peru": "Latin America", "Colombia": "Latin America"
}

records = []
for country, v2024 in zip(countries, base_2024):
    v2022 = round(v2024 - 1.5, 1)   # approximate 2022 value
    v2023 = round(v2024 - 0.5, 1)   # approximate 2023 value
    avg_share = round((v2022 + v2023 + v2024) / 3, 2)
    growth_rate = round((v2024 - v2022) / v2022 * 100, 2)  # % increase from 2022 to 2024
    records.append({
        "Country": country,
        "Region": region_map[country],
        "AvgShare": avg_share,
        "GrowthRate": growth_rate
    })

df = pd.DataFrame(records)

# Sort by AvgShare for clearer visual ordering
df = df.sort_values("AvgShare", ascending=False)

# ----- Multi‑Axes Chart (Bar + Line) -----
fig, ax1 = plt.subplots(figsize=(12, 6))

# Bar chart for average share
bars = ax1.bar(
    df["Country"],
    df["AvgShare"],
    color=plt.cm.Paired(range(len(df))),
    label="Avg Share (%)"
)
ax1.set_xlabel("Country")
ax1.set_ylabel("Average Female Employment Share (%)", color="tab:blue")
ax1.tick_params(axis="y", labelcolor="tab:blue")
ax1.set_xticklabels(df["Country"], rotation=45, ha="right")

# Secondary y‑axis for growth rate
ax2 = ax1.twinx()
line = ax2.plot(
    df["Country"],
    df["GrowthRate"],
    color="tab:red",
    marker="o",
    linewidth=2,
    label="Growth Rate (2022‑2024) %"
)
ax2.set_ylabel("Growth Rate (%)", color="tab:red")
ax2.tick_params(axis="y", labelcolor="tab:red")

# Unified legend
handles = [bars, line[0]]
labels = [h.get_label() for h in handles]
ax1.legend(handles, labels, loc="upper left")

plt.title("Average Vulnerable Female Employment Share & Growth (2022‑2024) by Country")
plt.tight_layout()
plt.savefig("female_employment_multi_axes.png", dpi=300)
plt.close()