✅ Python Project Data Visualization with Matplotlib & Seaborn

 

Python Project Data Visualization with Matplotlib & Seaborn 

📌 Task:
Create visual insights from data to spot trends, patterns, and outliers — helping with smarter business decisions.

🧠 What You’ll Learn

  • Plotting with matplotlib

  • Creating attractive charts using seaborn

  • Customizing titles, labels, and colors

  • Different types of visualizations: line, bar, heatmap, and more

✅ Python Code Example

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# 1. Load dataset
data = pd.read_csv("sales_data.csv")

# 2. Line plot: Sales over time
plt.figure(figsize=(10, 5))
sns.lineplot(data=data, x="Date", y="Sales")
plt.title("Sales Over Time")
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()

# 3. Bar plot: Total sales by region
plt.figure(figsize=(8, 5))
sns.barplot(data=data, x="Region", y="Sales", estimator=sum)
plt.title("Total Sales by Region")
plt.tight_layout()
plt.show()

# 4. Heatmap: Correlation matrix
plt.figure(figsize=(6, 4))
sns.heatmap(data.corr(), annot=True, cmap="coolwarm")
plt.title("Feature Correlation")
plt.tight_layout()
plt.show()

🛠️ Requirements

pip install pandas matplotlib seaborn

🔧 Try Enhancing

  • Add hue= to separate categories in plots

  • Experiment with scatter plots, pie charts, or histograms

  • Save plots using plt.savefig("filename.png")

💡 Real-World Use Cases

✅ Track sales trends and seasonal changes
✅ Compare products or categories
✅ Generate visual reports for management

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