Introduction to Python for Econometrics, Statistics and Data Analysis 5th Edition Kevin Sheppard

Introduction to Python for Econometrics, Statistics and Data Analysis 5th Edition Kevin Sheppard – Ebook PDF Instant Download/Delivery

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• Author: Kevin Sheppard

Python is a popular general purpose programming language which is well suited to a wide range of problems. Recent developments have extended Python’s range of applicability to econometrics, statistics and general numerical analysis. Python – with the right set of add-ons – is comparable to domain-specific languages such as R, MATLAB or Julia.

This book provides an introduction to Python for a beginning programmer. They may also be useful for an experienced Python programmer interested in using NumPy, SciPy, and matplotlib for numerical and statistical analaysis. They should also be useful for students, researchers or practitioners who require a versatile platform for econometrics, statistics or general numerical analysis (e.g. numeric solutions to economic models or model simulation).

Table contents:

Chapter 1. The Role of Statistics and the Data Analysis Process

Chapter 2. Built-in Data Types

Chapter 3. Arrays

Chapter 4. Basic Math

Chapter 5. Basic Functions and Numerical Indexing

Chapter 6. Special Arrays

Chapter 7. Array Functions

Chapter 8. Importing and Exporting Data

Chapter 9. Inf, NaN and Numeric Limits

Chapter 10. Logical Operators and Find

Chapter 11. Advanced Selection and Assignment

Chapter 12. Flow Control, Loops and Exception Handling

Chapter 13. Code Style

Chapter 14. Dates and Times

Chapter 15. Graphics

Chapter 16. pandas

Chapter 17. Structured Arrays

Chapter 18. Custom Function and Modules

Chapter 19. Probability and Statistical Functions

Chapter 20. Simulating Random Variables

Chapter 21. Statistical Analysis with statsmodels

Chapter 22. Non-linear Function Optimization

Chapter 23. String Manipulation

Chapter 24. File System Operations

Chapter 25. Performance and Code Optimization

Chapter 26. Improving Performance using Numba

Chapter 27. Improving Performance using Cython

Chapter 28. Executing Code in Parallel

Chapter 29. Object-Oriented Programming (OOP)

Chapter 30. Other Interesting Python Packages

Chapter 31. Examples

Chapter 32. Quick Reference

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