In this task, I worked with the Shopping Trends dataset and focused on applying data grouping and aggregation techniques using Python’s Pandas library. The primary objective was to practice how to summarize, compare, and analyze data across multiple categories, such as gender, location, product category, and subscription status. Grouping and aggregation are fundamental skills in data analytics, enabling us to quickly uncover patterns, averages, totals, and distributions hidden within large datasets. This task allowed me to transform raw transactional data into business-friendly summaries that provide meaningful and actionable insights. By the end, I had created a clean and structured set of outputs that highlighted customer behaviors, sales patterns, and gender-based purchasing differences, laying the foundation for advanced analytics and decision-making.
- Imported the dataset using Pandas.
- Explored dataset dimensions (rows, columns) and metadata using .head(), .info(), and column listings.
- Identified categorical (Gender, Location, Subscription Status, Category) and numerical (Age, Purchase Amount) fields for grouping operations.
I carried out multiple groupby() and agg() operations, each focusing on extracting valuable insights:
- Average Purchase Amount by Gender 👩🦰👨🦱 ⭐ Calculated the mean spending for male vs female customers. ⭐ Provided insights into gender-based purchasing behaviors.
- Total Spending by Product Category 🛍 ⭐ Summed purchase amounts across different product categories. ⭐ Identified which categories generated the highest revenue streams.
- Average Age of Customers by Location 🌍 ⭐ Grouped by customer location and calculated average age. ⭐ Useful for identifying which regions had younger or older demographics.
- Customer Count by Subscription Status 📩 ⭐ Counted customers under Subscribed vs Non-Subscribed groups. ⭐ Helpful in evaluating customer loyalty and marketing effectiveness.
- Multi-Aggregation Gender Summary 📊 ⭐ Produced a composite report summarizing gender differences with: ⭐ 🟢 Average Age ⭐ 🟢 Total Spending ⭐ 🟢 Average Spending ⭐ 🟢 Number of Customers
- Delivered a concise yet powerful breakdown for comparison.
- Gender Analysis: Helped highlight how males and females differ in average spending habits.
- Category Spending: Some categories consistently generated higher purchase volumes, showing strong demand.
- Demographic Patterns: Average ages by location reflected regional shopping behavior differences.
- Customer Loyalty: Subscription-based grouping showed how marketing and subscriptions influence purchase frequency.
- Holistic Gender Report: Multi-aggregation results provided a compact dashboard-like summary for quick decisions.
- Python (Jupyter Notebook / Script) → Environment for coding and structured output.
- Pandas → Core library for grouping, aggregation, and tabular manipulation.
- 📊 Mastered Grouping Operations → Learned to apply Pandas groupby() effectively with mean, sum, and count.
- 💡 Multi-Aggregation Expertise → Practiced agg() to perform multiple calculations simultaneously.
- ⚡ Data Summarization Skills → Converted raw tables into easy-to-read business summaries.
- 🌍 Business-Oriented Thinking → Understood how grouping operations directly support customer analysis, product trends, and loyalty metrics.
- 🧑💻 Reusable Analytics Script → Built a flexible template for grouping/aggregation tasks, applicable to real-world datasets.
A clean, professional grouping analysis that breaks down customer trends, category sales, gender-based insights, and subscription influence. This task not only improved my Pandas expertise but also strengthened my ability to generate compact and business-relevant summaries from large datasets.
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Show what the Code and Output looks like.