Humber Personal Finance with Python

Using pandas, Requests, and Recurrent
1. ed
ISBN: 978-1-4842-3802-8
Verlag: Apress
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 130 Seiten

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 LearnWork 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.



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.

Autoren/Hrsg.

Humber, Max

Inhaltsverzeichnis

1;Table of Contents;4
2;About the Author;8
3;About the Technical Reviewer;9
4;Introduction;10
5;Chapter 1: Setup;16
5.1;Anaconda;16
5.2;nteract;20
5.2.1;pip install;23
5.3;Data;23
6;Chapter 2: Profit;24
6.1;Mining;25
6.2;ROI;26
6.3;IRR;27
6.3.1;=IRR();27
6.4;pandas;30
6.4.1;read_excel;30
6.4.2;xnpv;32
6.4.3;xirr;35
6.5;Again!;37
6.6;Conclusion;39
7;Chapter 3: Convert;40
7.1;openexchangerates.org;41
7.2;Secrets;42
7.3;Documentation;43
7.4;Encapsulate;46
7.4.1;show_alternative;48
7.4.2;.apply;49
7.5;Conclusion;53
8;Chapter 4: Amortize;54
8.1;Banks;55
8.2;Amortization;56
8.3;Payment;56
8.4;Loop A;57
8.5;Loop B;61
8.6;Functionize;63
8.7;Evaluate;64
8.8;Conclusion;67
9;Chapter 5: Budget;68
9.1;Dates;68
9.1.1;datetime;70
9.2;Timest70
9.2.1;.normalize;71
9.3;Horizon;72
9.4;Flows;73
9.5;Totals;76
9.6;Visualization;77
9.7;Updating;78
9.8;Vacation I;80
9.9;English;82
9.9.1;get_dates;84
9.10;Fun;86
9.11;YAML;89
9.12;Functionize;91
9.13;Vacation II;92
9.14;Loading YAML;94
9.15;Conclusion;95
10;Chapter 6: Invest;96
10.1;Trade-Offs;97
10.2;Instantiate;97
10.3;Prices;102
10.4;Orders;103
10.5;Deposit;105
10.6;Simulate;106
10.7;Quotes;107
10.7.1;get_price;110
10.7.2;get_historical;113
10.8;Portfolio;115
10.9;Rebalance;116
10.10;Conclusion;117
11;Chapter 7: Spend;118
11.1;Prophet;118
11.2;Purchases;119
11.3;Forecast;120
11.4;Visualize;122
11.5;Conclusion;124
12;Appendix: Next;125
13;Index;128