Python Fundamentals For Data Analytics
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Python Fundamentals for Data Analytics
DESCRIPTION Python is a simple, easy-to-learn, and one of the top programming languages across the globe. As a result of advancements in AI, data mining, and numerical computing fields, Python has become a popular programming language catering to various stakeholders. It is a powerful tool for working with a variety of data. This book provides the basics of Python and an introduction to data analytics. This book offers a complete introduction to Python programming, covering everything from the basics to the advanced topics. It starts by explaining core concepts like syntax and the Python interpreter, then dives into data structures, control flow, functions, and modules. You will also learn about data analysis and visualization with popular libraries like NumPy, Pandas, Matplotlib, and Seaborn. It wraps up with practical case studies, showing how to apply Python in real-world scenarios effectively. The book serves as a step-by-step guide to performing data analysis. Its content is designed so that even a novice can learn and perform data analysis and visualization simply by following the instructions in the book. KEY FEATURES ● The book covers a wide range of topics, from Python fundamentals to advanced data analysis techniques. ● It includes practical exercises and real-world case studies to illustrate the applications of Python for data analysis. ● The book explains complex concepts in a clear and understandable manner. WHAT YOU WILL LEARN ● Understand the basics of programming languages and the role of the Python interpreter. ● Read about different data structures like lists, sets, tuples, and dictionaries, and understand their applications. ● Learn how to work with files in Python, including reading, writing, and appending data. ● Discover how to use NumPy and Pandas for efficient data manipulation and analysis. ● Learn how to create informative visualizations using Matplotlib and Seaborn. WHO THIS BOOK IS FOR This book is designed for students studying UG or PG courses in the computer science and applications domain. Learning Python is a simple way to begin the journey of data analytics. One of the in-demand domains in the job market, and research is data analytics. This book will be helpful for students interested in this domain. TABLE OF CONTENTS 1. Programming Languages and Python Interpreter 2. Python Fundamentals 3. Project Jupyter and JupyterLab Environment 4. Collection Types 5. Conditional Branching 6. Iterating Constructs 7. Functions and Methods 8. Modules 9. File Operations 10. Working with Data 11. Data Visualization 12. Case Studies Appendix I: Abbreviations
Python Data Science Handbook
Author: Jake VanderPlas
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2016-11-21
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 the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms
Python for Data Analysis
Author: Wes McKinney
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2017-09-25
Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. Use the IPython shell and Jupyter notebook for exploratory computing Learn basic and advanced features in NumPy (Numerical Python) Get started with data analysis tools in the pandas library Use flexible tools to load, clean, transform, merge, and reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Analyze and manipulate regular and irregular time series data Learn how to solve real-world data analysis problems with thorough, detailed examples