This hands-on tutorial will walk you through the entire process of working with CSV/Excel files and conducting exploratory data analysis (EDA) in Python. We’ll use a realistic e-commerce sales dataset that includes transactions, customer information, inventory data, and more. Table of contents Introduction Data analysis is an essential skill in today’s data-driven world. In this tutorial, we’ll learn how to: Import data from Excel files Clean and preprocess data Explore and analyze data through statistics and visualization Draw meaningful insights from business data We’ll be using several key Python libraries: pandas: For data manipulation and analysis numpy: For numerical operations matplotlib and seaborn: For data visualization Setting Up Your Environment First, let’s install the necessary libraries: openpyxl and xlrd are backends that pandas uses to read Excel files Import the libraries in your Python script: Understanding Our Dataset Our sample dataset represents an e-commerce company’s sales data. It contains five sheets: Sales_Data: Main transactional data with 1,000 orders Customer_Data: Customer demographic information Inventory: Product inventory details Monthly_Summary: Pre-aggregated monthly sales data Data_Issues: A sample of data with intentional quality problems for practice You can download the dataset here Reading Excel Files Now that we have our dataset, let’s start by reading the Excel file: You should see output showing the available sheets and their dimensions. Reading Specific Rows or Columns Sometimes you might only want to read specific parts of a large Excel file: Basic Data Exploration Let’s explore our sales data to understand its structure and contents: Let’s look at the distribution of orders across different categories and regions: Data Cleaning and Preparation Let’s practice data cleaning using the “Data_Issues” sheet, which was specifically created with common data problems: Now let’s clean the data: Let’s also clean our main sales data: Merging and Joining Data Now let’s combine data from different sheets to gain richer insights: Let’s also join inventory data to analyze product-level metrics: Exploratory Data Analysis Now let’s perform some meaningful exploratory data analysis to understand our business: Sales Performance Analysis Customer Segment Analysis Payment Method Analysis Return Rate Analysis Cross-Tabulation Analysis Correlation Analysis Data Visualization Now let’s create visualizations to better understand our data: Basic Visualizations Advanced Visualizations with Seaborn Complex Visualizations Conclusion In this tutorial, we explored the full workflow of handling CSV and Excel files in Python, from importing and cleaning raw data to conducting insightful exploratory data analysis (EDA). Using a realistic e-commerce dataset, we learned how to merge and join datasets, handle common data quality issues, and extract key business insights through statistical analysis and visualization. We also covered essential Python libraries like pandas, NumPy, matplotlib, and seaborn. By the end, you should be equipped with practical EDA skills to transform raw data into actionable insights for real-world applications. NikhilNikhil is an intern consultant at Marktechpost. He is pursuing an integrated dual degree in Materials at the Indian Institute of Technology, Kharagpur. Nikhil is an AI/ML enthusiast who is always researching applications in fields like biomaterials and biomedical science. With a strong background in Material Science, he is exploring new advancements and creating opportunities to contribute.Nikhilhttps://www.marktechpost.com/author/nikhil0980/Google Releases Agent Development Kit (ADK): An Open-Source AI Framework Integrated with Gemini to Build, Manage, Evaluate and Deploy Multi AgentsNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper from ByteDance Introduces MegaScale-Infer: A Disaggregated Expert Parallelism System for Efficient and Scalable MoE-Based LLM ServingNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces an LLM+FOON Framework: A Graph-Validated Approach for Robotic Cooking Task Planning from Video InstructionsNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces Inference-Time Scaling Techniques: Microsoft’s Deep Evaluation of Reasoning Models on Complex Tasks