Complete Guide: Working With Csv/excel Files And Eda In Python

This hands-on tutorial will locomotion you done nan full process of moving pinch CSV/Excel files and conducting exploratory information study (EDA) successful Python. We’ll usage a realistic e-commerce income dataset that includes transactions, customer information, inventory data, and more.

Introduction

Data study is an basal accomplishment successful today’s data-driven world. In this tutorial, we’ll study really to:

• Import information from Excel files
• Clean and preprocess data
• Explore and analyse information done statistic and visualization
• Draw meaningful insights from business data

We’ll beryllium utilizing respective cardinal Python libraries:

• pandas: For information manipulation and analysis
• numpy: For numerical operations
• matplotlib and seaborn: For information visualization

Setting Up Your Environment

First, let’s instal nan basal libraries:

• openpyxl and xlrd are backends that pandas uses to publication Excel files
• Import nan libraries successful your Python script:

Understanding Our Dataset

Our sample dataset represents an e-commerce company’s income data. It contains 5 sheets:

1. Sales_Data: Main transactional information pinch 1,000 orders
2. Customer_Data: Customer demographic information
3. Inventory: Product inventory details
4. Monthly_Summary: Pre-aggregated monthly income data
5. Data_Issues: A sample of information pinch intentional value problems for practice

You tin download nan dataset here

Reading Excel Files

Now that we person our dataset, let’s commencement by reference nan Excel file:

You should spot output showing nan disposable sheets and their dimensions.

Reading Specific Rows aliases Columns

Sometimes you mightiness only want to publication circumstantial parts of a ample Excel file:

Basic Data Exploration

Let’s research our income information to understand its building and contents:

Let’s look astatine nan distribution of orders crossed different categories and regions:

Data Cleaning and Preparation

Let’s believe information cleaning utilizing nan “Data_Issues” sheet, which was specifically created pinch communal information problems:

Now let’s cleanable nan data:

Let’s besides cleanable our main income data:

Merging and Joining Data

Now let’s harvester information from different sheets to summation richer insights:

Let’s besides subordinate inventory information to analyse product-level metrics:

Exploratory Data Analysis

Now let’s execute immoderate meaningful exploratory information study 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 amended understand our data:

Basic Visualizations

Advanced Visualizations pinch Seaborn

Complex Visualizations

Conclusion

In this tutorial, we explored nan afloat workflow of handling CSV and Excel files successful Python, from importing and cleaning earthy information to conducting insightful exploratory information study (EDA). Using a realistic e-commerce dataset, we learned really to merge and subordinate datasets, grip communal information value issues, and extract cardinal business insights done statistical study and visualization. We besides covered basal Python libraries for illustration pandas, NumPy, matplotlib, and seaborn. By nan end, you should beryllium equipped pinch applicable EDA skills to toggle shape earthy information into actionable insights for real-world applications.