Here are 65 books that Effective Pandas fans have personally recommended if you like
Effective Pandas.
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I started my career as a research scientist building machine learning algorithms for weather forecasting. Twenty years later, I found myself at a precision agriculture startup creating models that provided guidance to farmers on when to plant, what to plant, etc. So, I am part of the movement from academia to industry. Now, at Google Cloud, my team builds cross-industry solutions and I see firsthand what our customers need in their data science teams. This set of books is what I suggest when a CTO asks how to upskill their workforce, or when a graduate student asks me how to break into the industry.
What if you are faced with a problem for which a standard approach doesn’t yet exist? In such a case, you will need to be able to figure out the approach from the first principles. This book will help you learn how to derive insights starting from raw data.
'A statistical national treasure' Jeremy Vine, BBC Radio 2
'Required reading for all politicians, journalists, medics and anyone who tries to influence people (or is influenced) by statistics. A tour de force' Popular Science
Do busier hospitals have higher survival rates? How many trees are there on the planet? Why do old men have big ears? David Spiegelhalter reveals the answers to these and many other questions - questions that can only be addressed using statistical science.
Statistics has played a leading role in our scientific understanding of the world for centuries, yet we are all familiar with the way…
It is April 1st, 2038. Day 60 of China's blockade of the rebel island of Taiwan.
The US government has agreed to provide Taiwan with a weapons system so advanced that it can disrupt the balance of power in the region. But what pilot would be crazy enough to run…
I started my career as a research scientist building machine learning algorithms for weather forecasting. Twenty years later, I found myself at a precision agriculture startup creating models that provided guidance to farmers on when to plant, what to plant, etc. So, I am part of the movement from academia to industry. Now, at Google Cloud, my team builds cross-industry solutions and I see firsthand what our customers need in their data science teams. This set of books is what I suggest when a CTO asks how to upskill their workforce, or when a graduate student asks me how to break into the industry.
As a data scientist in the industry, it is very helpful to understand the business context behind the problems that you are solving. In many cases, you are trying to predict behavior—who is likely to buy an item, who is likely to click on a link, who is likely to repay a loan, etc.
This book by Eric Siegel is a great introduction to predictive analytics as used in real-life. It will help you frame data science problems in standard ways. For example, suppose you are asked to score sales leads so that salespeople can prioritize their efforts. How would you do it? The common way to frame this problem is to predict the customer lifetime value (LTV) of every sales lead. Before you can do prediction, you have to be able to do analysis though.
The way you estimate the LTV is to break the problem into three sub-problems:…
"The Freakonomics of big data." -Stein Kretsinger, founding executive of Advertising.com
Award-winning | Used by over 30 universities | Translated into 9 languages
An introduction for everyone. In this rich, fascinating - surprisingly accessible - introduction, leading expert Eric Siegel reveals how predictive analytics (aka machine learning) works, and how it affects everyone every day. Rather than a "how to" for hands-on techies, the book serves lay readers and experts alike by covering new case studies and the latest state-of-the-art techniques.
Prediction is booming. It reinvents industries and runs the world. Companies, governments, law…
I started my career as a research scientist building machine learning algorithms for weather forecasting. Twenty years later, I found myself at a precision agriculture startup creating models that provided guidance to farmers on when to plant, what to plant, etc. So, I am part of the movement from academia to industry. Now, at Google Cloud, my team builds cross-industry solutions and I see firsthand what our customers need in their data science teams. This set of books is what I suggest when a CTO asks how to upskill their workforce, or when a graduate student asks me how to break into the industry.
It is not enough for a data scientist to be able to analyze data and build ML models. You have to be able to communicate the insights to decision-makers concisely and accurately. This book shows you bad and good visualizations — you’ll be surprised by how often you would have defaulted to the bad way without the guidance provided by this book!
Effective visualization is the best way to communicate information from the increasingly large and complex datasets in the natural and social sciences. But with the increasing power of visualization software today, scientists, engineers, and business analysts often have to navigate a bewildering array of visualization choices and options.
This practical book takes you through many commonly encountered visualization problems, and it provides guidelines on how to turn large datasets into clear and compelling figures. What visualization type is best for the story you want to tell? How do you make informative figures that are visually pleasing? Author Claus O. Wilke…
A Duke with rigid opinions, a Lady whose beliefs conflict with his, a long disputed parcel of land, a conniving neighbour, a desperate collaboration, a failure of trust, a love found despite it all.
Alexander Cavendish, Duke of Ravensworth, returned from war to find that his father and brother had…
I am Wes McKinney, creator of the Python pandas project and author of Python for Data Analysis. I have been using Python for data work since 2007 and have worked extensively in the open source community to build accessible and fast data processing tools for Python programmers.
This is a great follow-up book to Python Data Science Handbook.
Co-authored by one of the core developers of scikit-learn, this provides a deeper introduction to doing machine learning work in Python. This will give you a solid foundation to be able to move on later to deeper topics including deep learning or other AI topics.
Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination. You'll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Muller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the…
I started my career as a research scientist building machine learning algorithms for weather forecasting. Twenty years later, I found myself at a precision agriculture startup creating models that provided guidance to farmers on when to plant, what to plant, etc. So, I am part of the movement from academia to industry. Now, at Google Cloud, my team builds cross-industry solutions and I see firsthand what our customers need in their data science teams. This set of books is what I suggest when a CTO asks how to upskill their workforce, or when a graduate student asks me how to break into the industry.
In industry, your data is very likely to live within a data warehouse such as BigQuery, Redshift, or Snowflake. Therefore, to be an effective data scientist in the industry, you should learn how to use data warehouses effectively.
Once you learn data warehousing and SQL with any one of these products, it is quite easy to pick up another. So which one do you start with?
You can use Snowflake on all three of the major public clouds. Because it’s a standalone product, it is the most similar to a “traditional” data warehouse and can be picked up easily even if you are not familiar with cloud computing. That makes it a good data warehouse to start with, and is the reason my second book pick is this book on Snowflake.
BigQuery is also available on all three major public clouds, but it works best (and is used most commonly)…
Explore the modern market of data analytics platforms and the benefits of using Snowflake computing, the data warehouse built for the cloud.
With the rise of cloud technologies, organizations prefer to deploy their analytics using cloud providers such as Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform. Cloud vendors are offering modern data platforms for building cloud analytics solutions to collect data and consolidate into single storage solutions that provide insights for business users. The core of any analytics framework is the data warehouse, and previously customers did not have many choices of platform to use.
I am Wes McKinney, creator of the Python pandas project and author of Python for Data Analysis. I have been using Python for data work since 2007 and have worked extensively in the open source community to build accessible and fast data processing tools for Python programmers.
Polars is a new modern data frame library, powered by Apache Arrow, that has grown like wildfire in the Python ecosystem in recent years. It provides many of the same capabilities as pandas, but with substantially better performance and scalability.
I recommend that Python programmers become proficient at both Polars and pandas, since while pandas is ubiquitous, more and more work will shift to Polars in the coming years.
Want to speed up your data analysis and work with larger-than-memory datasets? Python Polars offers a blazingly fast, multithreaded, and elegant API for data loading, manipulation, and processing. With this hands-on guide, you'll walk through every aspect of Polars and learn how to tackle practical use cases using real-world datasets.
Jeroen Janssens and Thijs Nieuwdorp from Xomnia in Amsterdam show you how this superfast DataFrame library is perfect for efficient data wrangling, ETL pipelines, and so much more. This book helps you quickly learn the syntax and understand Polars' underlying concepts. You don't need to have experience with pandas or…
The Duke's Christmas Redemption
by
Arietta Richmond,
A Duke who has rejected love, a Lady who dreams of a love match, an arranged marriage, a house full of secrets, a most unneighborly neighbor, a plot to destroy reputations, an unexpected love that redeems it all.
Lady Charlotte Wyndham, given in an arranged marriage to a man she…
I am Wes McKinney, creator of the Python pandas project and author of Python for Data Analysis. I have been using Python for data work since 2007 and have worked extensively in the open source community to build accessible and fast data processing tools for Python programmers.
This is one of the best books I’ve read on how to write better code and build more maintainable software in Python. It is well-written, concise, and to the point.
Brett’s book is a perfect companion to the other books on this list, which are more focused on data analysis and using specific libraries to build data systems.
It's easy to start developing programs with Python, which is why the language is so popular. However, Python's unique strengths, charms, and expressiveness can be hard to grasp, and there are hidden pitfalls that can easily trip you up. This second edition of Effective Python will help you master a truly "Pythonic" approach to programming, harnessing Python's full power to write exceptionally robust and well-performing code. Using the concise, scenario-driven style pioneered in Scott Meyers' best-selling Effective C++, Brett Slatkin brings together 90 Python best practices, tips, and shortcuts, and explains them with realistic…
I am Wes McKinney, creator of the Python pandas project and author of Python for Data Analysis. I have been using Python for data work since 2007 and have worked extensively in the open source community to build accessible and fast data processing tools for Python programmers.
While this book has a good amount of overlap with my book, it provides a valuable introduction to scikit-learn, one of the most popular libraries for machine learning in Python. There is also excellent content to improve your data visualization skills with matplotlib.
For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all-IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is…
I’ve been teaching and writing Python code (and managing others while they write Python code) for over 20 years. After all that time Python is still my tool of choice, and many times Python is the key part of how I explore and think about problems. My experience as a teacher also has prompted me to dig in and look for the simplest way of understanding and explaining the elegant way that Python features fit together.
I like this book not just because it’s a complete guide to the many ins and outs of data cleaning with Python, but also because David lays out the types of problems and the issues behind them. There are always trade-offs in data cleaning and this book lays out those trade-offs better than any other I’ve seen. This is one of the few books that as I go through it, I struggle to think of anything that could have been said better.
Think about your data intelligently and ask the right questions
Key Features
Master data cleaning techniques necessary to perform real-world data science and machine learning tasks
Spot common problems with dirty data and develop flexible solutions from first principles
Test and refine your newly acquired skills through detailed exercises at the end of each chapterBook Description
Data cleaning is the all-important first step to successful data science, data analysis, and machine learning. If you work with any kind of data, this book is your go-to resource, arming you with the insights and heuristics experienced data scientists had to learn the…
This book follows the journey of a writer in search of wisdom as he narrates encounters with 12 distinguished American men over 80, including Paul Volcker, the former head of the Federal Reserve, and Denton Cooley, the world’s most famous heart surgeon.
In these and other intimate conversations, the book…
Some programmers learn through online articles, videos, and blog posts. Not me. I need a throughline—a consistent, expert distillation of the material to take me from where I am to where I want to be. I am not good at patching together information from disparate sources. I need a great book. I have a PhD in computer science education, and I want to know what helps people learn. More importantly, I want to know how we can use such discoveries to write more effective books. The books I appreciate most are those that demonstrate not only mastery of the subject matter but also mastery of teaching.
I used this book for several years starting in 2013 when the first edition came out. It absolutely holds up today. Learning the Python language (the syntax) is one thing. Learning how to design programs using this syntax is another. We need both but, unfortunately, many books forgo the latter for the former. Not this book! I like the Problem Solving and Worked Example sections: they help learners apply a disciplined, step-by-step strategy to programming projects. There are multiple, varied contexts here as well, which helps capture a broader base of learners. Bonus feature: the Computing & Society boxes.
Python for Everyone, 3rd Edition is an introduction to programming designed to serve a wide range of student interests and abilities, focused on the essentials, and on effective learning. It is suitable for a first course in programming for computer scientists, engineers, and students in other disciplines. This text requires no prior programming experience and only a modest amount of high school algebra. Objects are used where appropriate in early chapters and students start designing and implementing their own classes in Chapter 9. New to this edition are examples and exercises that focus on various aspects of data science.
