# --- Create dataframe (Table CtG1A.3) ---
data = {
    "Year": [
        *["2020–22"] * 48,
        *["2015–17"] * 48
    ],
    "Indigenous_status": [
        *(["Aboriginal and Torres Strait Islander"] * 32 +
          ["Non-Indigenous"] * 8 +
          ["Difference"] * 8),
        *(["Aboriginal and Torres Strait Islander"] * 32 +
          ["Non-Indigenous"] * 8 +
          ["Difference"] * 8)
    ],
    "Sex": [
        *([ "Males"] * 16 + ["Females"] * 16 + ["Males"] * 4 + ["Females"] * 4 + ["Males"] * 4 + ["Females"] * 4),
        *([ "Males"] * 16 + ["Females"] * 16 + ["Males"] * 4 + ["Females"] * 4 + ["Males"] * 4 + ["Females"] * 4)
    ],
    "Measure": (["Mean", "LCI", "UCI"] * 32)[:96],
    "Quintile": (["Q1", "Q2", "Q3", "Q4"] * 24)[:96],
    "Value": [
        # Aboriginal and Torres Strait Islander 2020–22
        69.5, 71.7, 74.3, 74.6, 69.2, 71.2, 73.4, 73.3, 70.4, 72.6, 75.6, 76.1,
        74.0, 75.7, 78.0, 77.0, 73.8, 75.3, 77.2, 75.7, 75.0, 76.5, 79.2, 78.3,
        # Non-Indigenous 2020–22 (Means only)
        78.4, 79.5, 80.8, 82.0,
        82.3, 83.0, 84.0, 84.6,
        # Difference 2020–22
        8.9, 7.8, 6.5, 7.4,
        8.3, 7.3, 6.0, 7.6,
 
        # Aboriginal and Torres Strait Islander 2015–17
        69.7, 71.8, 72.6, 74.0, 68.9, 70.9, 71.4, 72.8, 70.5, 72.7, 73.8, 75.2,
        73.8, 76.7, 75.9, 77.7, 73.0, 75.8, 74.8, 76.6, 74.6, 77.6, 77.0, 78.8,
        # Non-Indigenous 2015–17 (Means only)
        77.8, 79.0, 80.3, 81.6,
        81.9, 82.7, 83.5, 84.5,
        # Difference 2015–17
        8.1, 7.2, 7.8, 7.7,
        8.1, 6.0, 7.5, 6.8
    ]
}
 
df = pd.DataFrame(data)
 
# --- Clean data ---
df["Quintile"] = pd.Categorical(df["Quintile"], categories=["Q1", "Q2", "Q3", "Q4"], ordered=True)
df["Year"] = df["Year"].astype(str)
 
# --- Filter mean values only for main plot ---
df_mean = df[df["Measure"] == "Mean"]
 
# --- Set plot style ---
sns.set(style="whitegrid", context="talk")
 
# --- Plot 1: Life expectancy by quintile and sex (Indigenous vs Non-Indigenous) ---
plt.figure(figsize=(11.7, 8.3))  # A4 size in inches (landscape)
sns.lineplot(
    data=df_mean[df_mean["Indigenous_status"] != "Difference"],
    x="Quintile", y="Value",
    hue="Sex", style="Indigenous_status",
    markers=True, dashes=False, linewidth=2.2
)
plt.title("Life Expectancy by SEIFA (IRSD) Quintile and Indigenous Status\nAustralia, 2015–17 vs 2020–22")
plt.ylabel("Life Expectancy (years)")
plt.xlabel("Socioeconomic Quintile (Q1–Q4)")
plt.legend(title="Sex / Indigenous Status")
plt.tight_layout()
plt.show()
 
# --- Plot 2: Life expectancy gap (Difference group) ---
plt.figure(figsize=(11.7, 8.3))  # A4 size
sns.lineplot(
    data=df_mean[df_mean["Indigenous_status"] == "Difference"],
    x="Quintile", y="Value",
    hue="Sex", style="Year",
    markers=True, linewidth=2.2, dashes=True
)
plt.title("Life Expectancy Gap (Aboriginal & Torres Strait Islander vs Non-Indigenous)\nBy Socioeconomic Quintile")
plt.ylabel("Gap (years)")
plt.xlabel("Socioeconomic Quintile (Q1–Q4)")
plt.legend(title="Sex / Year")
plt.tight_layout()
plt.show()

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