advanced_sales_wheel_9.py

# advanced_sales_wheel_9.py
# Retail Sales Dashboard – Version 9
# Developed by Tryfon Papadopoulos – GitHub: trifpap
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.patches import Wedge
import textwrap

plt.style.use("seaborn-v0_8")

# -----------------------------
# Load Dataset
# -----------------------------

file = input("Enter sales file (csv or xlsx): ")

if file.endswith(".csv"):
    df = pd.read_csv(file)
elif file.endswith(".xlsx"):
    df = pd.read_excel(file)
else:
    raise ValueError("File must be CSV or XLSX")

df["Date"] = pd.to_datetime(df["Date"])

# -----------------------------
# KPI Calculations
# -----------------------------

total_sales_value = df["Sales"].sum()

category_sales_full = df.groupby("Category")["Sales"].sum().sort_values(ascending=False)

best_category = category_sales_full.idxmax()
best_category_sales = category_sales_full.max()

product_sales_full = df.groupby("Product")["Sales"].sum().sort_values(ascending=False)

best_product = product_sales_full.idxmax()
best_product_sales = product_sales_full.max()

# -----------------------------
# Top 6 Categories
# -----------------------------

top_categories = category_sales_full.head(6)
other_sales = category_sales_full.iloc[6:].sum()

category_sales = top_categories.copy()

if other_sales > 0:
    category_sales["Others"] = other_sales

subcategories = df.groupby("Category")["Subcategory"].unique()

# -----------------------------
# Helper
# -----------------------------

def wrap_text(text,width):
    return "\n".join(textwrap.wrap(str(text),width))

# -----------------------------
# Prepare Wheel Data
# -----------------------------

data = []

for category in category_sales.index:

    sales = category_sales[category]

    if category == "Others":
        subs = ["Multiple"]
    else:
        subs = subcategories[category]

    data.append((category,list(subs),sales))

total_sales = category_sales.sum()

# -----------------------------
# Dashboard Layout
# -----------------------------

#fig = plt.figure(figsize=(18,12))
# A4
fig = plt.figure(figsize=(16,11))
fig.patch.set_facecolor("#f5f5f5")

# charts moved lower for KPI space
ax_wheel  = fig.add_axes([0.03,0.22,0.47,0.50])
ax_bar    = fig.add_axes([0.56,0.62,0.40,0.20])
ax_line   = fig.add_axes([0.56,0.36,0.40,0.18])
ax_growth = fig.add_axes([0.56,0.08,0.40,0.20])
ax_labels = fig.add_axes([0.03,0.04,0.47,0.16])

ax_wheel.set_aspect("equal")

# -----------------------------
# Dashboard Title
# -----------------------------

fig.suptitle(
"Retail Sales Dashboard - Version 9 - Developed by Tryfon Papadopoulos",
fontsize=22,
weight="bold",
y=0.98
)

# -----------------------------
# KPI Cards
# -----------------------------

kpi_box = dict(
boxstyle="round,pad=0.9",
facecolor="#f2f2f2",
edgecolor="gray"
)

kpi_y = 0.87

fig.text(
0.15,kpi_y,
f"Total Sales\n${total_sales_value:,.0f}\n ",
fontsize=14,
weight="bold",
bbox=kpi_box,
ha="center"
)

fig.text(
0.50,kpi_y,
f"Top Category\n{best_category}\n${best_category_sales:,.0f}",
fontsize=14,
bbox=kpi_box,
ha="center"
)

fig.text(
0.85,kpi_y,
f"Best Product\n{best_product}\n${best_product_sales:,.0f}",
fontsize=14,
bbox=kpi_box,
ha="center"
)

# -----------------------------
# Chart 1 – Category Wheel
# -----------------------------

values = [d[2] for d in data]

angles = np.cumsum([0] + [v/total_sales*360 for v in values])

colors = list(plt.get_cmap("tab20").colors[:len(values)])

radius = 2.1
label_data = []

for i,(category,subs,sales) in enumerate(data):

    start = angles[i]
    end = angles[i+1]

    wedge = Wedge(
        (0,0),
        radius,
        start,
        end,
        facecolor=colors[i],
        edgecolor="white",
        linewidth=4
    )

    ax_wheel.add_patch(wedge)

    angle = (start+end)/2
    rad = np.deg2rad(angle)

    x = 0.60*radius*np.cos(rad)
    y = 0.60*radius*np.sin(rad)

    percent = sales/total_sales*100

    text = (
        f"{i+1}. {wrap_text(category,12)}\n"
        f"${sales:,.0f}\n"
        f"({percent:.1f}%)"
    )

    ax_wheel.text(
        x,y,text,
        ha="center",
        va="center",
        fontsize=11,
        weight="bold"
    )

    label_data.append(f"{i+1}. {category} → {', '.join(subs)}")

ax_wheel.set_xlim(-2.4,2.4)
ax_wheel.set_ylim(-2.4,2.4)
ax_wheel.axis("off")

ax_wheel.set_title(
"Sales Distribution (Top Categories)",
fontsize=16,
weight="bold"
)

# -----------------------------------
# Chart 2: Top 10 Products by Sales
# -----------------------------------

top_products = (
    df.groupby(["Product", "Category", "Subcategory"])["Sales"]
    .sum()
    .sort_values(ascending=False)
    .head(10)
    .reset_index()
)

# Create multi-line label
top_products["Label"] = (
    top_products["Product"]
    + "\n("
    + top_products["Category"]
    + " → "
    + top_products["Subcategory"]
    + ")"
)

# Wrap long labels
#top_products["Label"] = top_products["Label"].apply(lambda x: wrap_text(x,25))
top_products["Label"] = top_products["Label"].apply(lambda x: wrap_text(x,35))

labels = list(top_products["Label"][::-1])
values = list(top_products["Sales"][::-1])

# Create spacing between bars
y_pos = np.arange(len(labels)) * 2.4

bars = ax_bar.barh(
    y_pos,
    values,
    height=1.9,
    color="steelblue"
)

# Apply labels
ax_bar.set_yticks(y_pos)
ax_bar.set_yticklabels(labels)

# Improve label formatting
for label in ax_bar.get_yticklabels():
    label.set_horizontalalignment("right")
    label.set_linespacing(1.0)

# Add space between labels and bars
ax_bar.tick_params(axis="y", pad=10, labelsize=8)

# Value labels inside bars
for bar in bars:

    width = bar.get_width()

    ax_bar.text(
        width * 0.97,
        bar.get_y() + bar.get_height()/2,
        f"${width:,.0f}",
        ha="right",
        va="center",
        color="white",
        fontsize=10,
        weight="bold"
    )

# Titles and formatting
ax_bar.set_title("Top 10 Products by Sales", fontsize=14)
ax_bar.set_xlabel("Sales")

ax_bar.set_xlim(0, max(values) * 1.10)

# Add subtle grid
ax_bar.grid(axis="x", linestyle="--", alpha=0.4)

# -----------------------------
# Chart 3 – Monthly Trend
# -----------------------------

monthly_sales = df.resample("ME",on="Date")["Sales"].sum()

ax_line.plot(monthly_sales.index,monthly_sales.values,marker="o")

ax_line.fill_between(
monthly_sales.index,
monthly_sales.values,
alpha=0.2
)

ax_line.set_title("Monthly Sales Trend")
ax_line.set_xlabel("Month")
ax_line.set_ylabel("Sales")

ax_line.grid(True,linestyle="--",alpha=0.5)

# -----------------------------
# Chart 4 – Category Growth
# -----------------------------

monthly_category = df.groupby(
[pd.Grouper(key="Date",freq="ME"),"Category"]
)["Sales"].sum().unstack()

growth = monthly_category.pct_change().mean().sort_values(ascending=False)

ax_growth.bar(
growth.index,
growth.values,
color="orange"
)

ax_growth.set_title("Category Growth Rate")
ax_growth.set_ylabel("Growth %")

ax_growth.tick_params(axis="x",rotation=45)

# -----------------------------
# Category Details
# -----------------------------

ax_labels.axis("off")

ax_labels.text(
#0.01,
0.06,
0.90,
"Category Details",
fontsize=13,
weight="bold"
)

half = len(label_data)//2 + len(label_data)%2

left = label_data[:half]
right = label_data[half:]

for i,text in enumerate(left):

    ax_labels.text(
        #0.01,
        0.06,    
        0.70 - i*0.18,
        wrap_text(text,40),
        fontsize=11
    )

for i,text in enumerate(right):

    ax_labels.text(
        #0.50,
        0.56,
        0.70 - i*0.18,
        wrap_text(text,40),
        fontsize=11
    )

# -----------------------------
# Save Dashboard
# -----------------------------

# Save PNG
plt.savefig(
"retail_sales_dashboard.png",
dpi=300,
bbox_inches="tight"
)

# Save PDF
plt.savefig(
"retail_sales_dashboard.pdf",
bbox_inches="tight"
)

print("Dashboard saved as: retail_sales_dashboard.png and retail_sales_dashboard.pdf")

plt.show()