---
product_id: 462261723
title: "Python for Data Analysis 3e: Data Wrangling with pandas, NumPy, and Jupyter"
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---

# Python for Data Analysis 3e: Data Wrangling with pandas, NumPy, and Jupyter

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## Description

Buy Python for Data Analysis 3e: Data Wrangling with pandas, NumPy, and Jupyter 3 by Mckinney, Wes (ISBN: 9781098104030) from desertcart's Book Store. Everyday low prices and free delivery on eligible orders.

Review: Improving python knowledge. - Helpful book to start studying/ relearning python.
Review: Excellent - This book is very thorough and written very clearly. I bought it to assist my learning about DS/ML/AI. As Wes says in the book, the vast majority of the work in this area is data "wrangling", or getting the data into a form suitable for analysis. This book is very comprehensive and covers every aspect of the process. It starts with some introductions to Python, IPython and Jupyter. It doesn't really assume any Python knowledge, but I knew and worked with Python already and would describe it as a "whistle-stop tour". IPython is a more interactive (and dare I say, better) Python shell/interpreter which is great for exploring / testing stuff out. Jupyter is simply a web-based tool which can use IPython and has a slightly different workflow based on "cells" which can be executed. After that, there is a brief section on NumPy, and it highlights the key ideas: vectorised operations with multi-dimensional arrays. That is, to create the result of summing the elements of two arrays, a and b, you can simply write a + b. This makes for quick and convenient batch operations on data with no for loops in sight. These arrays offer a compact and highly efficient way to work with data, and they the "lingua franca" of the Python data world. The bulk of what follows is on pandas, which makes working with numpy even easier and is a heavyweight tool for loading data from a variety of formats (and storing it), cleaning it, processing it, aggregating, joining, visualising (with help from matplotlib) - everything you could want really. pandas is amazing software, and like most great software projects, it has the right abstractions (like the DataFrame), integrates seamlessly with a lot of other data libraries, and starts to feels intuitive after a while. There is also a lot of information on working with time data, and a brief chapter on building models. My favourite part was probably the end-to-end data analysis examples where Wes explores datasets from movies to baby names. Seeing the whole process there was great. The appendices are well worth reading, if not skimming, since there are more NumPy and IPython tricks. Could this book be improved in any way? Yes, I think a lot of reads a bit like a reference. Some more "real-world" examples would have been better in a lot of cases - but you do see those in the data analysis chapter. Also, the reason for using simpler examples was probably to demonstrate the point more clearly and independent of other chapters. Overall though, this gets a strong recommendation from me if you want to get more into data and machine learning. Thanks to Wes McKinney, we not only have pandas, but this book to navigate it.

## Technical Specifications

| Specification | Value |
|---------------|-------|
| Best Sellers Rank | 24,933 in Books ( See Top 100 in Books ) 7 in Data Mining (Books) 10 in Database Applications 15 in Introduction to Programming |
| Customer reviews | 4.6 4.6 out of 5 stars (491) |
| Dimensions  | 17.78 x 3.81 x 22.86 cm |
| Edition  | 3rd |
| ISBN-10  | 109810403X |
| ISBN-13  | 978-1098104030 |
| Item weight  | 885 g |
| Language  | English |
| Print length  | 550 pages |
| Publication date  | 26 Aug. 2022 |
| Publisher  | O'Reilly Media |

## Images

![Python for Data Analysis 3e: Data Wrangling with pandas, NumPy, and Jupyter - Image 1](https://m.media-amazon.com/images/I/91QBEYSpnLL.jpg)

## Customer Reviews

### ⭐⭐⭐⭐⭐ Improving python knowledge.
*by N***O on 15 December 2025*

Helpful book to start studying/ relearning python.

### ⭐⭐⭐⭐⭐ Excellent
*by H***. on 15 August 2023*

This book is very thorough and written very clearly. I bought it to assist my learning about DS/ML/AI. As Wes says in the book, the vast majority of the work in this area is data "wrangling", or getting the data into a form suitable for analysis. This book is very comprehensive and covers every aspect of the process. It starts with some introductions to Python, IPython and Jupyter. It doesn't really assume any Python knowledge, but I knew and worked with Python already and would describe it as a "whistle-stop tour". IPython is a more interactive (and dare I say, better) Python shell/interpreter which is great for exploring / testing stuff out. Jupyter is simply a web-based tool which can use IPython and has a slightly different workflow based on "cells" which can be executed. After that, there is a brief section on NumPy, and it highlights the key ideas: vectorised operations with multi-dimensional arrays. That is, to create the result of summing the elements of two arrays, a and b, you can simply write a + b. This makes for quick and convenient batch operations on data with no for loops in sight. These arrays offer a compact and highly efficient way to work with data, and they the "lingua franca" of the Python data world. The bulk of what follows is on pandas, which makes working with numpy even easier and is a heavyweight tool for loading data from a variety of formats (and storing it), cleaning it, processing it, aggregating, joining, visualising (with help from matplotlib) - everything you could want really. pandas is amazing software, and like most great software projects, it has the right abstractions (like the DataFrame), integrates seamlessly with a lot of other data libraries, and starts to feels intuitive after a while. There is also a lot of information on working with time data, and a brief chapter on building models. My favourite part was probably the end-to-end data analysis examples where Wes explores datasets from movies to baby names. Seeing the whole process there was great. The appendices are well worth reading, if not skimming, since there are more NumPy and IPython tricks. Could this book be improved in any way? Yes, I think a lot of reads a bit like a reference. Some more "real-world" examples would have been better in a lot of cases - but you do see those in the data analysis chapter. Also, the reason for using simpler examples was probably to demonstrate the point more clearly and independent of other chapters. Overall though, this gets a strong recommendation from me if you want to get more into data and machine learning. Thanks to Wes McKinney, we not only have pandas, but this book to navigate it.

### ⭐⭐⭐⭐⭐ Highly recommended
*by K***M on 29 August 2025*

Whao!!! I love this textbook as it helped me to love coding with great interest. I worked on it weekly in order to improve my skills on Python coding for data analysis. I will recommend this book for anyone interested in data cleaning, cleansing, data wrangling, and visualisation. It contains a lot of hands-on examples for anyone that is a beginner in data analysis and to those who are professionals. It is highly recommended.

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*Last updated: 2026-03-18*