"Learning Pandas 2.0" is an essential guide for anyone looking to harness the power of Python's premier data manipulation library. With this comprehensive resource, you will not only master core Pandas 2.0 concepts but also learn how to employ its advanced features to perform efficient data manipulation and analysis.

Throughout the book, you will acquire a deep understanding of Pandas 2.0's data structures, indexing, and selection techniques. Gain expertise in loading, storing, and cleaning data from various file formats and sources, ensuring data integrity and consistency. As you progress, you will delve into advanced data transformation, merging, and aggregation methods to extract meaningful insights and generate insightful reports.

"Learning Pandas 2.0" also covers specialized data processing needs like time series data, DateTime operations, and geospatial analysis. Furthermore, this book demonstrates how to integrate Pandas 2.0 with machine learning libraries like Scikit-learn, TensorFlow, and PyTorch for predictive analytics. This will empower you to build powerful data-driven models to solve complex problems and enhance your decision-making capabilities.

Key Learnings

• Master core Pandas 2.0 concepts, including data structures, indexing, and selection for efficient data manipulation.
• Load, store, and clean data from various file formats and sources, ensuring data integrity and consistency.
• Perform advanced data transformation, merging, and aggregation techniques for insightful analysis and reporting.
• Harness time series data, DateTime operations, and geospatial analysis for specialized data processing needs.
• Visualize data effectively using Seaborn, Plotly, and advanced geospatial visualization tools.
• Integrate Pandas 2.0 with machine learning libraries like Scikit-learn, TensorFlow, and PyTorch for predictive analytics.

Table of Content

1. Introduction to Pandas 2.0
2. Data Read, Storage, and File Formats
3. Indexing and Selecting Data
4. Data Manipulation and Transformation
5. Time Series and DateTime Operations
6. Performance Optimization and Scaling
7. Machine Learning with Pandas 2.0
8. Text Data and Natural Language Processing
9. Geospatial Data Analysis

"Hands-On Multi-Cloud Kubernetes" is an essential guide for anyone looking to understand Kubernetes and how it can be used to manage multi-cloud infrastructure. With eight comprehensive chapters, this book provides hands-on experience in setting up Kubernetes clusters, administering deployments and updates, and working with AWS and GCP tools.

Readers will learn to work with various powerful tools, including Helm, FluxCD, Virtual Kubelet, Submariner, and KubeFed. With GitOps principles and workflows, they will practice continuous delivery and learn to manage secrets and config maps. They will build and deploy serverless clusters using Virtual Kubelet and learn to scale them across multiple cloud environments. They will even be introduced to cross-cluster networking with Submariner, where they will learn to perform service discovery, load balancing, and monitor networking metrics.

Managing multi-cluster Kubernetes can be a daunting task, but with KubeFed, readers will gain the skills necessary to set up and deploy multicluster federations, making it easier than ever to administer their own infrastructure. And with multi-cloud CI/CD pipelines using Jenkins, they will perform end-to-end multi-cloud operations, ensuring their code is delivered quickly and efficiently.

Finally, the book covers security in Kubernetes, giving readers the tools and knowledge to configure RBAC, Kubernetes network policies, and secure data over Kubernetes clusters. They will even learn to use Open Policy Agent to manage compliance, ensuring that their infrastructure is powerful and secure.

Key Learnings

• Learn Multi-cloud Kubernetes from fundamentals to advanced concepts and tools
• Setting up and managing Kubernetes clusters on multi-cloud infrastructure
• Working with powerful tools like Helm, FluxCD, and Virtual Kubelet
• Utilize Submariner for cross-cluster networking, service discovery, and load balancing
• CI/CD pipelines with Jenkins for end-to-end multi-cloud operations
• Practice GitOps principles and workflows for continuous delivery
• Building and deploying serverless clusters using Virtual Kubelet
• Managing multiple Kubernetes clusters as a single entity with KubeFed
• Security in Kubernetes with RBAC, network policies, and Open Policy Agent

Table of Content

1. Introduction to Multi-cloud Kubernetes
2. Kubernetes Cluster Management and Deployment
3. Using FluxCD
4. Virtual Kubelet and Serverless Clusters
5. Networking with Submariner
6. MultiCluster Management and Federation
7. Multi-cloud CI/CD Pipelines
8. Security in Multi-cloud Kubernetes

Audience

This book is ideal for cloud professionals, the DevOps team, Kubernetes developers, and networking professionals to explore multi-cloud networking, working with multi clusters, deploying Kubernetes, and getting skilled with various innovative Kubernetes tools. Knowing cloud networking or Kubernetes is sufficient to begin with the book.