本教程全面介绍了使用Python处理CSV/Excel文件并进行探索性数据分析（EDA）的完整流程。通过一个真实的电商销售数据集，涵盖了数据导入、清洗、预处理、合并、数据探索、可视化等多个环节。教程详细讲解了如何使用pandas、NumPy、matplotlib和seaborn等关键Python库，从原始数据中提取有价值的业务洞察，为数据分析师提供了实用的技能和方法。

📊 **数据导入与环境设置：** 教程首先介绍了如何安装必要的Python库，包括pandas、numpy、matplotlib和seaborn，以及使用pandas读取Excel文件。 提供了读取特定行或列的方法，为后续的数据处理奠定了基础。

🔍 **数据理解与探索：** 教程详细介绍了电商销售数据集的结构，包括销售数据、客户信息、库存数据等。 针对销售数据，展示了如何进行基本的数据探索，如查看数据结构、统计描述，以及不同类别和地区的订单分布情况，帮助用户熟悉数据集。

🧹 **数据清洗与准备：** 教程演示了如何处理数据质量问题，使用“Data_Issues”表进行数据清洗练习。 教程还展示了如何清洗主要销售数据，确保数据的准确性和一致性，为后续分析做好准备。

🔗 **数据合并与连接：** 教程讲解了如何将来自不同工作表的数据合并和连接，例如，将销售数据与客户数据合并，进行更深入的分析。 此外，还演示了如何连接库存数据，以便分析产品层面的指标，从而获得更全面的业务视角。

📈 **探索性数据分析与可视化：** 教程详细介绍了如何进行销售业绩分析、客户细分分析、支付方式分析、退货率分析和交叉制表分析。 并通过创建各种可视化图表，如基本可视化和使用Seaborn的高级可视化，帮助用户更好地理解数据，发现关键业务洞察。

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.

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 filesClean and preprocess dataExplore and analyze data through statistics and visualizationDraw meaningful insights from business data

We’ll be using several key Python libraries:

pandas: For data manipulation and analysisnumpy: For numerical operationsmatplotlib 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 filesImport 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 ordersCustomer_Data: Customer demographic informationInventory: Product inventory detailsMonthly_Summary: Pre-aggregated monthly sales dataData_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.

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