A Basic Dashboard combines multiple visualizations to present key insights in one view.
In Data Analytics, dashboards help decision-makers quickly understand performance, trends, and comparisons.
In this project, we will create a simple Sales Dashboard.
Step 1: Import Libraries
import pandas as pd
import matplotlib.pyplot as plt
Step 2: Create Sample Dataset
data = {
"Month": ["Jan", "Feb", "Mar", "Apr", "May", "Jun"],
"Sales": [10000, 15000, 20000, 18000, 25000, 22000],
"Expenses": [7000, 9000, 12000, 11000, 15000, 14000],
"Profit": [3000, 6000, 8000, 7000, 10000, 8000]
}df = pd.DataFrame(data)
Step 3: Create Dashboard Layout
We will use subplots to place multiple charts in one figure.
plt.figure(figsize=(12, 8))
1. Line Chart (Sales Trend)
plt.subplot(2, 2, 1)
plt.plot(df["Month"], df["Sales"], marker="o")
plt.title("Monthly Sales Trend")
plt.xticks(rotation=45)
2. Bar Chart (Profit by Month)
plt.subplot(2, 2, 2)
plt.bar(df["Month"], df["Profit"])
plt.title("Monthly Profit")
plt.xticks(rotation=45)
3. Pie Chart (Expense Distribution)
plt.subplot(2, 2, 3)
plt.pie(df["Expenses"], labels=df["Month"], autopct="%1.1f%%")
plt.title("Expense Distribution")
4. Line Chart (Sales vs Expenses)
plt.subplot(2, 2, 4)
plt.plot(df["Month"], df["Sales"], label="Sales")
plt.plot(df["Month"], df["Expenses"], label="Expenses")
plt.title("Sales vs Expenses")
plt.legend()
plt.xticks(rotation=45)
Final Step: Adjust Layout and Show
plt.tight_layout()
plt.show()
What This Dashboard Shows
- Sales growth trend
- Monthly profit comparison
- Expense distribution
- Sales vs expense comparison
Why Dashboard Projects are Important
Dashboards help:
Track business performance
Compare KPIs
Identify trends and patterns
Support data-driven decisions
Present insights in a professional format
Key Takeaway
A Basic Dashboard combines multiple charts into one visual report.
This is a foundational step toward building professional dashboards using tools like Power BI, Tableau, or advanced Python visualization libraries.