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Preface Part 1£®Python and Finance 1£® Why Python for Finance The Python Programming Language A Brief History of Python The Python Ecosystem The Python User Spectrum The Scientific Stack Technology in Finance Technology Spending Technology as Enabler Technology and Talent as Barriers to Entry Ever-Increasing Speeds£¬ Frequencies£¬ and Data Volumes The Rise of Real-Time Analytics Python for Finance Finance and Python Syntax Efficiency and Productivity Through Python From Prototyping to Production Data-Driven and AI-First Finance Data-Driven Finance AI-First Finance Conclusion Further Resources 2£® Python Infrastructure conda as a Package Manager Installing Miniconda Basic Operations with conda conda as a Virtual Environment Manager Using Docker Containers Docker Images and Containers Building an Ubuntu and Python Docker Image Using Cloud Instances RSA Public and Private Keys Jupyter Notebook Configuration File Installation Script for Python and Jupyter Notebook Script to Orchestrate the Droplet Setup Conclusion Further Resources Part II£®Mastering the Basics 3£® Data Types and Structures Basic Data Types Integers Floats Booleans Strings Excursion£º Printing and String Replacements Excursion£º Regular Expressions Basic Data Structures Tuples Lists Excursion£º Control Structures Excursion£º Functional Programming Dicts Sets Conclusion Further Resources 4£® Numerical Computing with NumPy Arrays of Data Arrays with Python Lists The Python array Class Regular NumPy Arrays Part III£® Financial data science Part IV£® Algorithmic Trading Part V£® Derivatives Analytics