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Pandas Data Analysis Challenge

Background

Embark on a data analysis journey using Python's Pandas library. This challenge simulates real-world data analysis tasks within an e-commerce context, focusing on customer insights, product trends, and profitability.

Files

  • Module 4 Challenge files.

Setup

  • Repository: pandas-challenge-1
  • Clone and push changes to GitHub or GitLab.

Challenge Instructions

Part 1: Explore the Data

  • Import CSV data.
  • Examine column names and statistics.
  • Investigate data to answer key questions about item categories, subcategories, and client activity.

Part 2: Transform the Data

  • Calculate subtotals, shipping prices, total prices, costs, and profits.
  • Apply transformations to enhance data analysis capabilities.

Part 3: Confirm Your Work

  • Verify transformed data against actual order receipts.

Part 4: Summarize and Analyze

  • Analyze spending for top clients.
  • Summarize findings in a presentation-ready format.
  • Write a concise summary of insights gained from the data.

Hints and Considerations

  • Utilize Pandas documentation for advanced functions.
  • Implement well-named functions to streamline operations and improve code readability.
  • Follow the analytical process, defining questions and exploring data thoroughly.

Requirements

Data Exploration

  • Display column names and descriptive statistics.
  • Identify top item categories and subcategories.
  • Determine clients with the most data entries.
  • Reveal the quantity ordered by the top client.

Data Transformation

  • Create columns for subtotals, shipping prices, total prices, costs, and profits.
  • Ensure accurate calculation of financial metrics.

Data Confirmation

  • Match calculated total prices with provided order receipts.

Data Summary and Analysis

  • Calculate total revenue for top clients.
  • Construct a summary DataFrame for top clients with key financial metrics.
  • Develop a function to convert currency to millions and format data for presentation.
  • Provide a brief summary of analytical findings.

Submission

Submit your GitHub repository URL containing the challenge work for evaluation.