FreeComputerBooks.com
Links to Free Computer, Mathematics, Technical Books all over the World


 Title Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter
 Author(s) Wes McKinney
 Publisher: O'Reilly Media; 3rd edition (September 20, 2022); eBook (Open Edition)
 Paperback 548 pages
 eBook HTML
 Language: English
 ISBN10: 1491912057
 ISBN13: 9781491912058
 Share This:
Book Description
The focus of the book is specifically on Python programming, libraries, and tools as opposed to data analysis methodology. This is the Python programming you need for data analysis. You'll learn the latest versions of pandas, NumPy, and Jupyter in the process.
About the Authors Wes McKinney is an American software developer and entrepreneur. He's now an active member of the Python data community and is an advocate for the use of Python in data analysis, finance, and statistical computing applications.
 Python Programming
 Data Analysis and Data Mining
 Data Science
 Big Data
 Machine Learning
 Books by O'Reilly®
 Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter (Wes McKinney)
 The Mirror Site (1)  PDF
 The Mirror Site (2)  PDF
 The Mirror Site (3)  ePub (233 pages)

Julia Data Science (Jose Storopoli, et al.)
An accessible, intuitive, and highly efficient makes Julia a formidable language for data science. This book will get readers up to speed on key features of the Julia language and illustrate its facilities for data science and machine learning work.

O'Reilly® Python Data Science Handbook: Essential Tools
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, ScikitLearn, and other related tools.

Python for Everybody: Exploring Data in Python 3
This book is designed to introduce students to programming and software development through the lens of exploring data. You can think of the Python programming language as your tool to solve data problems that are beyond the capability of a spreadsheet.

Think Stats, 2nd Edition: Exploratory Data Analysis in Python
This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python.

Elements of Data Science using Python (Allen B. Downey)
This book is an introduction to data science for people with no programming experience. The goal is to present a small, powerful subset of Python that allows you to do real work in data science as quickly as possible.

Mining Social Media using Python: Finding Stories in Data
This book shows you how to use Python and key data analysis tools to find the stories buried in social media. Perform advanced data analysis using Python, Jupyter Notebooks, and the pandas library.

Introduction to Probability for Data Science (Stanley Chan)
This book is an introductory textbook in undergraduate probability in the context of data science to emphasize the inseparability between data (computing) and probability (theory) in our time, with examples in both MATLAB and Python.

Data Science at the Command Line, 2nd Ed. (Jeroen Janssens)
This handson guide demonstrates how the flexibility of the command line can help you become a more efficient and productive data scientist. Learn how to combine small, yet powerful, commandline tools to quickly obtain, scrub, explore, and model your data.

Data Engineering Cookbook: The Plumbing of Data Science
This is a practical and comprehensive guide. You will learn the basics of data engineering. Then you will learn the technologies and frameworks required to build data pipelines to work with large datasets.

Computational and Inferential: The Foundations of Data Science
Step by step, you'll learn how to leverage algorithmic thinking and the power of code, gain intuition about the power and limitations of current machine learning methods, and effectively apply them to real business problems.

Data Science: Theories, Models, Algorithms, and Analytics
It provides a bucket full of information regarding Data Science, covers a wide variety of sections by giving access to theories, data science algorithms, tools and analytics. You'll explore the right approach to best practices to guide you along the way.

The Data Engineer's Guide to Apache Spark (Databricks)
This book is for data engineers looking to leverage the immense growth of Apache Spark to build faster and more reliable data pipelines. It leverages Spark's amazing speed, scalability, simplicity, and versatility to build practical Big Data solutions.

R for Data Science: Visualize, Model, Transform, Tidy, Import
This book teaches you how to do data science with R: You'll learn how to get your data into R, get it into the most useful structure, transform it, visualize it and model it, how data science can help you work with the uncertainty and capture the opportunities.
:






















