Personal Finance with Python: Using Pandas, Requests, and Recurrent

ISBN-13: 9781484238011 / Angielski / Miękka / 2018 / 117 str.

Max Humber

Recenzje

"The book is not only a love letter to pandas--each financial aspect treated in the book becomes a love letter for Requests, Matplotlib, Recurrent, NumPy, YAML, and the like." (Pierre Radulescu-Banu, Computing Reviews, September 27, 2019)

Spis treści

0. Introduction

1. Setup

2. Profit

3. Convert

4. Amortize

5. Budget

6. Invest

7. Spend

Afterword: Next

O autorze

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.

Blurb

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.

You will:

• 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