--- product_id: 429567200 title: "Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter" price: "₹ 9128" currency: INR in_stock: true reviews_count: 8 url: https://www.desertcart.in/products/429567200-python-for-data-analysis-data-wrangling-with-pandas-numpy-jupyter store_origin: IN region: India --- # Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter **Price:** ₹ 9128 **Availability:** ✅ In Stock ## Quick Answers - **What is this?** Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter - **How much does it cost?** ₹ 9128 with free shipping - **Is it available?** Yes, in stock and ready to ship - **Where can I buy it?** [www.desertcart.in](https://www.desertcart.in/products/429567200-python-for-data-analysis-data-wrangling-with-pandas-numpy-jupyter) ## Best For - Customers looking for quality international products ## Why This Product - Free international shipping included - Worldwide delivery with tracking - 15-day hassle-free returns ## Description Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter: 9781098104030: Computer Science Books @ desertcart.com Review: Data Analysis simplified using Numpy and Pandas. - Off the bat, get this book! This text uses Numpy and Pandas for data analysis in a far more extensive way than many texts on the market. The text starts from a very basic principle: transforming lists into Pandas Series, moving on to other iterables and transforming them into both Series and DataFrames. The syntax in this book is straightforward. Nonetheless, this book's pages are less rigid, and care must be taken when flipping pages. Strengths: Simplicity of syntax: This book's Python codes are very simple and easily understood by anyone who has used basic Python for a while. For people who may be new to the language, the first two chapters gradually introduce basic Python language constructs that form the bedrock for the chapters that follow. While this text does not teach Python overall, it does a decent job of giving you the tools needed to analyse data from scratch. Organisation: Unlike other books that require readers to start from chapter 1 to the end, and make chapters dependent on previous chapters' codes, this book allows readers to jump seamlessly between chapters. I have moved pretty quickly through the text by jumping to topics that interest me or give me the kicks I need for a project I am working on. Its GitHub site also supports readers to customise codes for their own use. Datasets: The success of any analysis study is the practice on those tools one has acquired. This text provides numerous datasets that one could use for practice. Moreover, a reader can comfortably simulate their own data to learn. I understand simulated data may not be like real-world data, but they test your skills for future work. Should you need more practice with this text, the UCI data repository comes in handy. More packages: Although this book is heavily bent on Pandas and Numpy, it does an excellent job integrating other packages. For instance, statsmodels, scipy and other packages are used along with Pandas and Numpy, offering simple ways to orchestrate models and use them for prediction. Weaknesses: 1. Weak pages: A major flaw of this book is its weak pages. Although the binding is perfect, the pages themselves are too fragile. A few days ago, I was careless withmy coffee and I had a spill on the book. That little carelessness has deformed my otherwise lovely and daily motivational Pandas and Numpy book. 2. Lack of end-of-chapter exercises: This book would have had no competition had it had end-of-chapter exercises. For some of us who love to nail concepts to the very bone, practice makes perfect. The lack thereof makes readers seek practice elsewhere. I have always dreaded Numpy, and even worse, Pandas. This book has removed my dread and made me comfortable with these packages in the last month. Despite its weak pages and lack of exercises, this book offers simplified syntax, a huge list of datasets, better organisation and more packages that work in tandem with Pandas and Numpy. Review: Excellent Reference book - In the ever-evolving world of data analysis, "Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter" shines as a beacon for beginners and experienced data enthusiasts alike. It is a testament to the book's caliber that it manages to comprehensively cover Python's powerful data manipulation tools in such an approachable manner. The book's primary strength lies in its thorough exploration of data wrangling. For the uninitiated, data wrangling is the art of maneuvering raw data into a more digestible form for analysis, and this book nails it. By delving deep into the intricacies of libraries like pandas and NumPy What's particularly commendable is the balance between theory and practice. While it's brimming with technical details and explanations, it never feels overwhelming. Instead, readers are constantly engaged with practical examples, ensuring that learning is both meaningful and applicable. Whether you're a beginner stepping into the world of data or a seasoned pro, this book offers invaluable insights and skills that will enhance your data-wrangling journey. ## Technical Specifications | Specification | Value | |---------------|-------| | Best Sellers Rank | #26,368 in Books ( See Top 100 in Books ) #3 in Data Mining (Books) #6 in Data Processing #13 in Python Programming | | Customer Reviews | 4.6 out of 5 stars 505 Reviews | ## Images ![Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter - Image 1](https://m.media-amazon.com/images/I/91QBEYSpnLL.jpg) ## Customer Reviews ### ⭐⭐⭐⭐⭐ Data Analysis simplified using Numpy and Pandas. *by M***S on August 8, 2025* Off the bat, get this book! This text uses Numpy and Pandas for data analysis in a far more extensive way than many texts on the market. The text starts from a very basic principle: transforming lists into Pandas Series, moving on to other iterables and transforming them into both Series and DataFrames. The syntax in this book is straightforward. Nonetheless, this book's pages are less rigid, and care must be taken when flipping pages. Strengths: Simplicity of syntax: This book's Python codes are very simple and easily understood by anyone who has used basic Python for a while. For people who may be new to the language, the first two chapters gradually introduce basic Python language constructs that form the bedrock for the chapters that follow. While this text does not teach Python overall, it does a decent job of giving you the tools needed to analyse data from scratch. Organisation: Unlike other books that require readers to start from chapter 1 to the end, and make chapters dependent on previous chapters' codes, this book allows readers to jump seamlessly between chapters. I have moved pretty quickly through the text by jumping to topics that interest me or give me the kicks I need for a project I am working on. Its GitHub site also supports readers to customise codes for their own use. Datasets: The success of any analysis study is the practice on those tools one has acquired. This text provides numerous datasets that one could use for practice. Moreover, a reader can comfortably simulate their own data to learn. I understand simulated data may not be like real-world data, but they test your skills for future work. Should you need more practice with this text, the UCI data repository comes in handy. More packages: Although this book is heavily bent on Pandas and Numpy, it does an excellent job integrating other packages. For instance, statsmodels, scipy and other packages are used along with Pandas and Numpy, offering simple ways to orchestrate models and use them for prediction. Weaknesses: 1. Weak pages: A major flaw of this book is its weak pages. Although the binding is perfect, the pages themselves are too fragile. A few days ago, I was careless withmy coffee and I had a spill on the book. That little carelessness has deformed my otherwise lovely and daily motivational Pandas and Numpy book. 2. Lack of end-of-chapter exercises: This book would have had no competition had it had end-of-chapter exercises. For some of us who love to nail concepts to the very bone, practice makes perfect. The lack thereof makes readers seek practice elsewhere. I have always dreaded Numpy, and even worse, Pandas. This book has removed my dread and made me comfortable with these packages in the last month. Despite its weak pages and lack of exercises, this book offers simplified syntax, a huge list of datasets, better organisation and more packages that work in tandem with Pandas and Numpy. ### ⭐⭐⭐⭐⭐ Excellent Reference book *by A***I on October 12, 2023* In the ever-evolving world of data analysis, "Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter" shines as a beacon for beginners and experienced data enthusiasts alike. It is a testament to the book's caliber that it manages to comprehensively cover Python's powerful data manipulation tools in such an approachable manner. The book's primary strength lies in its thorough exploration of data wrangling. For the uninitiated, data wrangling is the art of maneuvering raw data into a more digestible form for analysis, and this book nails it. By delving deep into the intricacies of libraries like pandas and NumPy What's particularly commendable is the balance between theory and practice. While it's brimming with technical details and explanations, it never feels overwhelming. Instead, readers are constantly engaged with practical examples, ensuring that learning is both meaningful and applicable. Whether you're a beginner stepping into the world of data or a seasoned pro, this book offers invaluable insights and skills that will enhance your data-wrangling journey. ### ⭐⭐⭐⭐⭐ Great reference! *by B***N on August 13, 2025* As with all O’reilly books I’ve purchased, great quality and a great reference. Really good to go back to the fundamentals. ## Frequently Bought Together - Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter - Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python - Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems --- ## Why Shop on Desertcart? - 🛒 **Trusted by 1.3+ Million Shoppers** — Serving international shoppers since 2016 - 🌍 **Shop Globally** — Access 737+ million products across 21 categories - 💰 **No Hidden Fees** — All customs, duties, and taxes included in the price - 🔄 **15-Day Free Returns** — Hassle-free returns (30 days for PRO members) - 🔒 **Secure Payments** — Trusted payment options with buyer protection - ⭐ **TrustPilot Rated 4.5/5** — Based on 8,000+ happy customer reviews **Shop now:** [https://www.desertcart.in/products/429567200-python-for-data-analysis-data-wrangling-with-pandas-numpy-jupyter](https://www.desertcart.in/products/429567200-python-for-data-analysis-data-wrangling-with-pandas-numpy-jupyter) --- *Product available on Desertcart India* *Store origin: IN* *Last updated: 2026-05-19*