Humber | Personal Finance with Python | Buch | sack.de

Humber Personal Finance with Python



Using pandas, Requests, and Recurrent

1. Auflage 2018, 117 Seiten, Kartoniert, Book, Format (B × H): 154 mm x 233 mm, Gewicht: 2175 g
ISBN: 978-1-4842-3801-1
Verlag: Springer, Berlin


Humber Personal Finance with Python

Deal with data, build up financial formulas in code from scratch, and evaluate and think about money in your day-to-day life. This book is about Python and personal finance and how you can effectively mix the two together.
In Personal Finance with Python you will learn Python and finance at the same time by creating a profit calculator, a currency converter, an amortization schedule, a budget, a portfolio rebalancer, and a purchase forecaster. Many of the examples use pandas, the main data manipulation tool in Python. Each chapter is hands-on, self-contained, and motivated by fun and interesting examples.
Although this book assumes a minimal familiarity with programming and the Python language, if you don't have any, don't worry. Everything is built up piece-by-piece and the first chapters are conducted at a relaxed pace. You'll need Python 3.6 (or above) and all of the setup details are included.
What You'll Learn - Work with data in pandas

- Calculate Net Present Value and Internal Rate Return

- Query a third-party API with Requests

- Manage secrets

- Build efficient loops

- Parse English sentences with Recurrent

- Work with the YAML file format

- Fetch stock quotes and use Prophet to forecast the future

Who This Book Is For
Anyone interested in Python, personal finance, and/or both! This book is geared towards those who want to manage their money more effectively and to those who just want to learn or improve their Python.

Zielgruppe


Professional/practitioner


Autoren/Hrsg.


Weitere Infos & Material


0. Introduction 1. Setup 2. Profit 3. Convert 4. Amortize 5. Budget 6. Invest 7. Spend Afterword: Next


Humber, Max
Max Humber is a Data Engineer interested in improving finance with technology. He works for Wealthsimple, and previously served as the first data scientist for the online lending platform Borrowell. He has spoken at Pycon, ODSC, PyData, useR, and BigDataX in Colombia, London, Berlin, Brussels, and Toronto.


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