✅ Python Project Time Series Trend Analysis with Pandas & Seaborn

 

✅ Python Project Time Series Trend Analysis with Pandas & Seaborn 📊📅

📌 Task:
Analyze time-based data to uncover trends, seasonality, and growth patterns — essential for forecasting and business insights.

🧠 What You’ll Learn

  • Handling time series with Pandas

  • Calculating rolling averages and trend lines

  • Visualizing seasonal patterns

  • Grouping and resampling data by time intervals

✅ Python Code Example

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

# 1. Load time-series data
data = pd.read_csv("sales_data.csv", parse_dates=["Date"])
data.set_index("Date", inplace=True)

# 2. Resample monthly and calculate total sales
monthly_sales = data["Sales"].resample("M").sum()

# 3. Line plot: Monthly sales trend
plt.figure(figsize=(10, 5))
sns.lineplot(x=monthly_sales.index, y=monthly_sales.values)
plt.title("Monthly Sales Trend")
plt.xlabel("Month")
plt.ylabel("Total Sales")
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()

# 4. Plot rolling average alongside actual sales
plt.figure(figsize=(10, 5))
monthly_sales.plot(label="Actual Sales", alpha=0.5)
monthly_sales.rolling(3).mean().plot(label="3-Month Rolling Avg", linewidth=2)
plt.title("Sales Trend with Rolling Average")
plt.xlabel("Month")
plt.ylabel("Total Sales")
plt.legend()
plt.tight_layout()
plt.show()

🛠️ Requirements

pip install pandas seaborn matplotlib

🔧 Try Enhancing

  • Resample weekly with .resample('W') for weekly trends

  • Detect anomalies or outliers

  • Group data by Year or Quarter for broader insights

💡 Real-World Use Cases

✅ Forecasting monthly/quarterly revenue
✅ Detecting seasonal sales patterns
✅ Monitoring business growth trends

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