🎯 Project Objective
To build a Data Analysis Dashboard in Python that allows users to:
- Load datasets (CSV, Excel)
- Analyze and visualize data
- Gain actionable insights through charts and summary statistics
This project demonstrates:
- Data handling with
pandas - Data visualization using
matplotlibandseaborn - Creating interactive or automated dashboards
Project: Data Analysis Dashboard
Project Description
The Data Analysis Dashboard app allows users to explore a dataset through Python by:
- Viewing column names and first few rows
- Calculating statistics like mean, median, sum
- Creating charts like bar plots, line graphs, pie charts
- Exporting analyzed results
Use Case Example:
Analyze sales data, student performance, or financial records for insights.
Python Example Code
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# Load dataset
file_path = "sales_data.csv"
df = pd.read_csv(file_path)
# Display basic information
print("Columns:", df.columns)
print("\nFirst 5 rows:\n", df.head())
print("\nSummary Statistics:\n", df.describe())
# Data Cleaning (if necessary)
df.fillna(0, inplace=True)
# Example 1: Bar chart of sales by product
plt.figure(figsize=(8,5))
sns.barplot(x="Product", y="Sales", data=df)
plt.title("Sales by Product")
plt.xlabel("Product")
plt.ylabel("Sales")
plt.show()
# Example 2: Pie chart of sales distribution by region
region_sales = df.groupby("Region")["Sales"].sum()
region_sales.plot(kind="pie", autopct="%1.1f%%", figsize=(6,6))
plt.title("Sales Distribution by Region")
plt.ylabel("")
plt.show()
# Example 3: Line chart of sales over months
monthly_sales = df.groupby("Month")["Sales"].sum()
monthly_sales.plot(kind="line", marker='o')
plt.title("Monthly Sales Trend")
plt.xlabel("Month")
plt.ylabel("Total Sales")
plt.show()
# Export analyzed data to Excel
df.to_excel("analyzed_sales.xlsx", index=False)
✅ Key Features
- Load and preview any dataset (CSV/Excel)
- Calculate summary statistics
- Visualize data with bar, line, and pie charts
- Export analyzed data to Excel
- Optional: Add filters for interactive analysis
Leave a Reply