Finance

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Interesting things from the week

By Matt Button |  Jan 25, 2019  | open-source-software, finance, art, tdd

A collection of 5 things from the week that I found interesting, am enjoying, or am working on.

This week:

  • Sometimes Netflix preview images and descriptions get out of sync, creating amusing mashups
  • Troy Hunt’s tips on personal finance for technology professionals
  • Patrick McKenzie on: Should you choose a commercial license or donation model to monetise your open source project?
  • A great thread discussing when to practice TDD (Test Driven Development)
  • Blender 3D tutorial: Create a Modern Bedroom in Blender in 35 Minutes
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Tony Alexander Presentation Notes - things which affected the NZ housing market over the decades

By Matt Button |  Jan 2, 2019  | investment, finance, property

I recently attended a presentation by BNZ’s Chief Economist, Tony Alexander.

One of the topics that really caught my attention was his perspective on the things that influenced the housing market over the decades, from the 1950’s until today.

I wrote up a bunch of notes to summarise his points.

If you’re interested in getting a bit of perspective on the NZ property market, and some of the things that caused it’s near-relentless movement upward over time, then read on.

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How to scrape Yahoo Finance and extract fundamental stock market data using Python, LXML, and Pandas

By Matt Button |  Jan 24, 2019  | python, pandas, lxml, scraping, finance, featured

In this blog post I’ll show you how to scrape Income Statement, Balance Sheet, and Cash Flow data for companies from Yahoo Finance using Python, LXML, and Pandas.

I’ll use data from Mainfreight NZ (MFT.NZ) as an example.

The screenshot below shows what you can expect to get by following the steps in this blog post:

The first few columns of a Pandas DataFrame containing MFT.NZ Balance Sheet data

By following the steps in this blog post, you’ll also generate a DataFrame containing data from the Income Statement, and Cash Flow statement.

After creating Pandas DataFrames, I’ll show you how to export everything to an Excel file, so you’ll have output that looks something like this:

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