sales_daep.xlsxdashboard.py import streamlit as st import pandas as pd import matplotlib.pyplot as plt import seaborn as sns

Load Excel

df = pd.read_excel("sales_daep.xlsx")

st.title("Excel Dashboard with Streamlit")

st.write("Here’s a preview of your data:") st.dataframe(df)

st.set_page_config(page_title="Excel Dashboard", layout="wide")

####################### Try the upper part first ####### ################ Comment below lines ###############

Load Excel file

@st.cache_data

def load_data(): return pd.read_excel("sales_daep.xlsx")

df = load_data()

Sidebar filters

st.write(df.columns) st.sidebar.header("Filters") segment = st.sidebar.selectbox("Segment", df["Segment"].unique()) period = st.sidebar.selectbox("period", df["Year"].unique())

Filter data

filtered_df = df[(df["Segment"] == segment) & (df["Year"] == period)]

KPIs

total_spend = filtered_df["Sales"].sum() avg_spend = filtered_df["Sales"].mean()

col1, col2 = st.columns(2) col1.metric("Total Sales", f"${total_spend:,.0f}") col2.metric("Average Sales", f"${avg_spend:,.0f}")

Data table

st.subheader("Filtered Data") st.dataframe(filtered_df)

Bar chart

st.subheader("Sales by Category of Goods") st.bar_chart(filtered_df.groupby("Category of Goods")["Sales"].sum())

####################### Additional Visualizations #######################

1. Sales by Category of Goods

st.subheader("Sales by Category of Goods") category_sales = filtered_df.groupby("Category of Goods")["Sales"].sum().reset_index() fig, ax = plt.subplots(figsize=(8,5)) sns.barplot(data=category_sales, x="Category of Goods", y="Sales", palette="Blues_d", ax=ax) ax.set_ylabel("Sales ($)") ax.set_xlabel("Category of Goods") ax.set_title("Sales by Category of Goods") st.pyplot(fig)

2. Sales by Sub-Category

st.subheader("Sales by Sub-Category") subcat_sales = filtered_df.groupby("Sub-Category")["Sales"].sum().reset_index() fig, ax = plt.subplots(figsize=(10,5)) sns.barplot(data=subcat_sales, x="Sub-Category", y="Sales", palette="Greens_d", ax=ax) ax.set_ylabel("Sales ($)") ax.set_xlabel("Sub-Category") ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha="right") ax.set_title("Sales by Sub-Category") st.pyplot(fig)

3. Sales by Region

st.subheader("Sales by Region") region_sales = filtered_df.groupby("Region")["Sales"].sum().reset_index() fig, ax = plt.subplots(figsize=(6,4)) sns.barplot(data=region_sales, x="Region", y="Sales", palette="Oranges_d", ax=ax) ax.set_ylabel("Sales ($)") ax.set_xlabel("Region") ax.set_title("Sales by Region") st.pyplot(fig)

4. Top 10 Products by Sales

st.subheader("Top 10 Products by Sales") top_products = filtered_df.groupby("Product Name")["Sales"].sum().nlargest(10).reset_index() fig, ax = plt.subplots(figsize=(10,5)) sns.barplot(data=top_products, y="Product Name", x="Sales", palette="Purples_d", ax=ax) ax.set_xlabel("Sales ($)") ax.set_ylabel("Product Name") ax.set_title("Top 10 Products by Sales") st.pyplot(fig)

5. Pie Chart: Sales Share by Category of Goods

st.subheader("Sales Share by Category of Goods") sales_category = filtered_df.groupby("Category of Goods")["Sales"].sum() fig, ax = plt.subplots(figsize=(6,6)) ax.pie(sales_category, labels=sales_category.index, autopct="%1.1f%%", startangle=140, colors=sns.color_palette("pastel")) ax.set_title("Sales Share by Category of Goods") st.pyplot(fig)

6. Heatmap: Region vs Category of Goods

st.subheader("Sales Heatmap: Region vs Category of Goods") sales_heatmap = filtered_df.pivot_table( values="Sales", index="Region", columns="Category of Goods", aggfunc="sum", fill_value=0 ) fig, ax = plt.subplots(figsize=(10,6)) sns.heatmap(sales_heatmap, annot=True, fmt=".0f", cmap="YlGnBu", ax=ax) ax.set_title("Sales Heatmap by Region and Category") st.pyplot(fig)