fig, axes = plt.subplots(1, 3, figsize=(13, 4))

cols   = ["revenue_usd", "units_sold", "return_rate_pct"]
labels = ["Revenue (USD)", "Units Sold", "Return Rate (%)"]
colors = ["#4a90d9", "#2ecc71", "#e67e22"]

for ax, col, label, color in zip(axes, cols, labels, colors):
    ax.hist(df[col], bins=6, color=color, edgecolor="white",
            linewidth=0.8, alpha=0.85)

    mean_val   = df[col].mean()
    median_val = df[col].median()
    ax.axvline(mean_val,   color="#e74c3c", linestyle="--",
               linewidth=1.8, label="Mean")
    ax.axvline(median_val, color="black",   linestyle="-.",
               linewidth=1.8, label="Median")

    skew_val = df[col].skew()
    ax.set_title(f"{label}\nskew = {skew_val:.3f}",
                 fontsize=11, fontweight="bold")
    ax.legend(fontsize=9)

plt.suptitle("Histograms with Mean vs Median — Detecting Skewness",
             fontsize=13, fontweight="bold", y=1.03)
plt.tight_layout()
plt.savefig("histograms_skewness.png", dpi=150)
plt.show()