Python for Network and Data Science
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Python for Network and Data Science

About the Bundle

Python Essentials for Networking and Parallel Computing

Aspiring data scientists and network administrators will find what they need in this bundle.

With topics like automation, monitoring, and security, "Python Networking 101" lays the groundwork for a career in networking with Python. An excellent companion piece is "Parallel Python with Dask," which explores parallel computing with the help of the Dask library—an essential tool for working with massive datasets and speeding up Python workflows.

When read as a whole, these books provide a solid grounding in Python that is applicable to a wide range of data science and networking applications.

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  • Categories

    • APIs
    • Python
    • Networking
    • Distributed Systems
    • Artificial Intelligence
    • Functional Programming
    • Software Engineering

About the Books

Python Networking 101

Navigating essentials of networking, socket programming, AsyncIO, network testing, simulations and Ansible
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Python Networking 101 is the ultimate guide for aspiring network administrators looking to build their network management and automation skills using Python. With a comprehensive and hands-on approach, this book covers the most important aspects of networking, including network fundamentals, network automation, monitoring, security, topology, and testing.

The book begins with an overview of the Python language and its libraries used for networking tasks. Each chapter then focuses on a specific networking task, providing readers with a deep understanding of the topic and practical demonstrations using Python libraries. By the end of each chapter, readers will be well-versed in the execution and implementation of these tasks.

Throughout the book, readers will learn about the best Python libraries network administrators prefer, including Netmiko, Paramiko, SNMP, Flask, AsyncIO, and more. Practical examples and exercises will help them gain hands-on experience working with these libraries to achieve various networking objectives. The book also discusses advanced network automation techniques, providing insights into network automation frameworks, such as Ansible, and how to build custom network automation solutions using Python.

By the end of the book, readers will be equipped with the knowledge to integrate Python with network management tools, making them efficient and effective network administrators.

Key Learnings

  • Master Python language and its networking libraries for network administration tasks.
  • Monitor and analyze network performance and troubleshoot issues effectively.
  • Enhance network security using Python libraries and best practices.
  • Get well-versed with Netmiko, Paramiko, Socket, PySNMP, AsyncIO, and SimPy.
  • Develop custom network services and interact with RESTful APIs using Python.
  • Improve performance with asynchronous programming using AsyncIO in network applications.
  • Get hands-on with Ansible to create playbooks and perform every possible network automation.
  • Perform network testing and simulation, and analyze results for optimized performance.
  • Manage and automate network configuration changes and ensure compliance.
  • Leverage advanced network automation techniques and frameworks for efficient administration.

Table of Content

  1. Introduction to Python and Networking Libraries
  2. TCP, UDP and Socket Programming
  3. Working with Application Layer
  4. Exploring Network Automation
  5. Network Monitoring and Analysis
  6. Network Security and Python
  7. Working with APIs and Network Services
  8. Network Programming with AsyncIO
  9. Network Testing and Simulation
  10. Network Configuration Management
  11. Ansible and Python


"Python Networking 101" is designed to provide readers with the skills required to excel as a network administrators. The practical approach, coupled with real-world examples, ensures readers can implement the techniques learned in their professional careers. Knowing Python and the basics of computer networks is sufficient, to begin with this book.

Parallel Python with Dask

Make code reusable and deployed for high performance web apps
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Unlock the Power of Parallel Python with Dask: A Perfect Learning Guide for Aspiring Data Scientists

Dask has revolutionized parallel computing for Python, empowering data scientists to accelerate their workflows. This comprehensive guide unravels the intricacies of Dask to help you harness its capabilities for machine learning and data analysis.

Across 10 chapters, you'll master Dask's fundamentals, architecture, and integration with Python's scientific computing ecosystem. Step-by-step tutorials demonstrate parallel mapping, task scheduling, and leveraging Dask arrays for NumPy workloads. You'll discover how Dask seamlessly scales Pandas, Scikit-Learn, PyTorch, and other libraries for large datasets.

Dedicated chapters explore scaling regression, classification, hyperparameter tuning, feature engineering, and more with clear examples. You'll also learn to tap into the power of GPUs with Dask, RAPIDS, and Google JAX for orders of magnitude speedups.

This book places special emphasis on practical use cases related to scalability and distributed computing. You'll learn Dask patterns for cluster computing, managing resources efficiently, and robust data pipelines. The advanced chapters on DaskML and deep learning showcase how to build scalable models with PyTorch and TensorFlow.

With this book, you'll gain practical skills to:

  • Accelerate Python workloads with parallel mapping and task scheduling
  • Speed up NumPy, Pandas, Scikit-Learn, PyTorch, and other libraries
  • Build scalable machine learning pipelines for large datasets
  • Leverage GPUs efficiently via Dask, RAPIDS and JAX
  • Manage Dask clusters and workflows for distributed computing
  • Streamline deep learning models with DaskML and DL frameworks

Packed with hands-on examples and expert insights, this book provides the complete toolkit to harness Dask's capabilities. It will empower Python programmers, data scientists, and machine learning engineers to achieve faster workflows and operationalize parallel computing.

Table of Content

  1. Introduction to Dask
  2. Dask Fundamentals
  3. Batch Data Parallel Processing with Dask
  4. Distributed Systems and Dask
  5. Advanced Dask: APIs and Building Blocks
  6. Dask with Pandas
  7. Dask with Scikit-learn
  8. Dask and PyTorch
  9. Dask with GPUs
  10. Scaling Machine Learning Projects with Dask

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