A comprehensive guide to Python tools for data science, covering NumPy, Pandas, Sklearn, and data visualization. Perfect for beginners and professionals.

Key Features

• Comprehensive guide to Python data science libraries
• Practical hands-on exercises
• Includes sections on data cleaning and visualization

Book Description

This book, part of the best-selling Pocket Primer series, offers a comprehensive introduction to essential Python tools for data scientists. It begins with an overview of Python basics, followed by in-depth coverage of NumPy and Pandas, focusing on their features and applications. The text also addresses the critical tasks of writing regular expressions and performing data cleaning.Further sections delve into data visualization techniques and the use of Sklearn and SciPy, providing practical knowledge and skills for handling complex data analysis tasks. This structured approach ensures that readers gain a complete understanding of the tools and techniques necessary for effective data science.Designed to be accessible yet thorough, this book includes numerous code samples to reinforce learning. Companion files with source code are available for download, making it an invaluable resource for anyone looking to master Python for data science and enhance their data analysis capabilities.

What you will learn

• Install and use Python libraries effectively
• Handle data cleaning tasks with ease
• Perform data analysis with Pandas
• Create visualizations using Matplotlib and Seaborn
• Conduct statistical analysis with Sklearn
• Utilize Google Colaboratory for Python projects

Who this book is for

Ideal for individuals who have a basic understanding of Python and want to delve into data science. Suitable for beginners, intermediate learners, and professionals looking to enhance their skills in Python libraries. Prior programming knowledge is beneficial.

A comprehensive guide to Python tools for data science, covering NumPy, Pandas, Sklearn, and data visualization. Perfect for beginners and professionals.

Key Features

• Comprehensive guide to Python data science libraries
• Practical hands-on exercises
• Includes sections on data cleaning and visualization

Book Description

This book, part of the best-selling Pocket Primer series, offers a comprehensive introduction to essential Python tools for data scientists. It begins with an overview of Python basics, followed by in-depth coverage of NumPy and Pandas, focusing on their features and applications. The text also addresses the critical tasks of writing regular expressions and performing data cleaning.Further sections delve into data visualization techniques and the use of Sklearn and SciPy, providing practical knowledge and skills for handling complex data analysis tasks. This structured approach ensures that readers gain a complete understanding of the tools and techniques necessary for effective data science.Designed to be accessible yet thorough, this book includes numerous code samples to reinforce learning. Companion files with source code are available for download, making it an invaluable resource for anyone looking to master Python for data science and enhance their data analysis capabilities.

What you will learn

• Install and use Python libraries effectively
• Handle data cleaning tasks with ease
• Perform data analysis with Pandas
• Create visualizations using Matplotlib and Seaborn
• Conduct statistical analysis with Sklearn
• Utilize Google Colaboratory for Python projects

Who this book is for

Ideal for individuals who have a basic understanding of Python and want to delve into data science. Suitable for beginners, intermediate learners, and professionals looking to enhance their skills in Python libraries. Prior programming knowledge is beneficial.

A comprehensive guide to Python tools for data science, covering NumPy, Pandas, Sklearn, and data visualization. Perfect for beginners and professionals.

Key Features

• Comprehensive guide to Python data science libraries
• Practical hands-on exercises
• Includes sections on data cleaning and visualization

Book Description

This book, part of the best-selling Pocket Primer series, offers a comprehensive introduction to essential Python tools for data scientists. It begins with an overview of Python basics, followed by in-depth coverage of NumPy and Pandas, focusing on their features and applications. The text also addresses the critical tasks of writing regular expressions and performing data cleaning.Further sections delve into data visualization techniques and the use of Sklearn and SciPy, providing practical knowledge and skills for handling complex data analysis tasks. This structured approach ensures that readers gain a complete understanding of the tools and techniques necessary for effective data science.Designed to be accessible yet thorough, this book includes numerous code samples to reinforce learning. Companion files with source code are available for download, making it an invaluable resource for anyone looking to master Python for data science and enhance their data analysis capabilities.

What you will learn

• Install and use Python libraries effectively
• Handle data cleaning tasks with ease
• Perform data analysis with Pandas
• Create visualizations using Matplotlib and Seaborn
• Conduct statistical analysis with Sklearn
• Utilize Google Colaboratory for Python projects

Who this book is for

Ideal for individuals who have a basic understanding of Python and want to delve into data science. Suitable for beginners, intermediate learners, and professionals looking to enhance their skills in Python libraries. Prior programming knowledge is beneficial.