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Key benefits

Perform efficient data analysis and manipulation tasks using pandas 1.x

Apply pandas to different real-world domains with the help of step-by-step examples

Make the most of pandas as an effective data exploration tool

Description

Extracting valuable business insights is no longer a ‘nice-to-have’, but an essential skill for anyone who handles data in their enterprise. Hands-On Data Analysis with Pandas is here to help beginners and those who are migrating their skills into data science get up to speed in no time. This book will show you how to analyze your data, get started with machine learning, and work effectively with the Python libraries often used for data science, such as pandas, NumPy, matplotlib, seaborn, and scikit-learn. Using real-world datasets, you will learn how to use the pandas library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will learn how to conduct exploratory data analysis by calculating summary statistics and visualizing the data to find patterns. In the concluding chapters, you will explore some applications of anomaly detection, regression, clustering, and classification using scikit-learn to make predictions based on past data. This updated edition will equip you with the skills you need to use pandas 1.x to efficiently perform various data manipulation tasks, reliably reproduce analyses, and visualize your data for effective decision making – valuable knowledge that can be applied across multiple domains.

Who is this book for?

This book is for data science beginners, data analysts, and Python developers who want to explore each stage of data analysis and scientific computing using a wide range of datasets. Data scientists looking to implement pandas in their machine learning workflow will also find plenty of valuable know-how as they progress. You’ll find it easier to follow along with this book if you have a working knowledge of the Python programming language, but a Python crash-course tutorial is provided in the code bundle for anyone who needs a refresher.

What you will learn

Understand how data analysts and scientists gather and analyze data

Perform data analysis and data wrangling using Python

Combine, group, and aggregate data from multiple sources

Create data visualizations with pandas, matplotlib, and seaborn

Apply machine learning algorithms to identify patterns and make predictions

Use Python data science libraries to analyze real-world datasets

Solve common data representation and analysis problems using pandas

Build Python scripts, modules, and packages for reusable analysis code

What do you get with Print?

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Brenton Chang Aug 04, 2021

As an analyst in a cyber security operation center role, I live and breathe data. The more, the better. Pandas is a natural fit for organizing, navigating and analyzing diverse data at scale. However, if you’ve ever tried leveraging Pandas to do this, you quickly realize how difficult it can be. The documentation is ambiguous and due to the diversity of the how others leverage Pandas it’s difficult to find scenarios and code examples that line up with your needs. Enter “Hands-On Data Analysis with Pandas”. Molin does a great job at organizing and presenting all you need to get started leveraging both pandas and Jupyter notebooks. She also clearly and concisely explains the fundamental of machine learning and statistical analysis. Her mastery is in both understanding the discipline and the libraries used to get the work done. I not only reference the book to help with organizing and analyzing my data, I also reference the book to support my visualization and plotting requirements. There aren’t many books out there that are both this comprehensive and good at teaching a very complex subject. If you are in cyber security you need this book.

Amazon Verified review

John Renne May 24, 2023

This easy to follow book is exactly what I needed at this stage of my learning curve. I love how the author takes the reader through accessing real world data that are messy and in some cases missing. Accessing real world data with APIs is a tool I appreciate leaning and then seeing the shortcomings of the real world data has been what most other books are missing. This book will better allow me to translate the lessons to my own needs. Well done!

SJ Jul 14, 2021

For those new to data science and data analysis in general, the book does a good job of providing just enough background and explanation of the underlying statistical concepts to allow one to understand and gain the most benefit from following along with the examples presented. This is far more useful than just presenting code snippets and pretty plots while skimming over the "why".As a PhD researcher, I use Pandas almost every day and have done for years (previously as a systematic portfolio manager); I still came across several methods and functionalities covered in the book that I either hadn't had too much previous exposure to or that at least were of benefit to provide a solid "knowledge refresher" I wasn't even aware I needed.Overall it's a very solid offering from Stefanie Molin, for whom it was clearly a "labour of love".

Pranshu Jaryal May 05, 2021

This is a great book for whosoever wants to jump into the domain of Data Analysis. Pandas is the most important Python Library that one should master if he or she wants to excel in this domain. This book covers all the necessary topics under Pandas like Data Wrangling, Aggregation, and Visualization. Apart from the theory, one can also find an Application to work on to practice real-world problems. Therefore if you are a Data Enthusiast then this is a must-read book for you.

BBCReview Dec 02, 2021

This is a killer book for the Python and data wrangling professional, making all other books look like elementary school treatments. I've read 7 Pandas books and 32 Python books that have Pandas sections. Stepanie Molin's is by far the strongest, most detailed, easiest to follow, best-exampled book, and easiest to understand of any of the 39 books read. All other books pale in comparison to this must-read book from Molin.The datasets are intuitive. Not a boring texty book. Instead, lots of example code appears on every single page, illustrating the features. The story and example code flow together, not skipping around or showing disjointed points. The chapters follow your workflow, from data ingest and EDA to data cleaning, data wrangling, visualizataion, and finally to applications.Thorough treatments are given to data cleaning, data wrangling, and data enrichment as separate topics, going into deep details on how to reshape and reindex data frames, how to do proper joins on data frames, left, right, inner, and outer, and how to do many other data cleaning and wrangling steps. For exaple, you'll learn how to set a new index, and why you should do that. And when inserting rows from different dataframes, you can leave yourself a new indicator column that shows you which table added the row. Pandas has many features like this that professionals should know, and Stephanie Molin shows the "how to".Of course there's a GitHub link so you can download the example datasets. Honestly, I'm only up through data wrangling - have not even reached the financial analysis, machine learning, and advanced visualization code. I can hardly wait to work all the examples in person. (As you know, reading is good, but building the code is by far the most effective way to learn.)Thanks Stephanie for devoting the time to making this a wonderful detailed and usable guide on how to use Pandas to solve my customer's problems. What a joy to read and use. This is the first and best book you should buy for Pandas.

Hands-On Data Analysis with Pandas: A Python data science handbook for data collection, wrangling, analysis, and visualization , Second Edition

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