Zillow is a leading real estate listing website in USA. The app has data of millions of homes. These homes include the ones for sale, rent, or even ones which are yet to reach the market. It also provides rent and property estimates. So lets learn Zillow data scraping to scrape real estate listings from it. Scraping Zillow Real Estate Data using WebHarvy. WebHarvy is a visual web scraper which can be configured to scrape data from any website. In this article we will see how WebHarvy can be used to scrape real estate data from Zillow. Scraping Property Data from Zillow listings. Zillow Group is committed to ensuring digital accessibility for individuals with disabilities. We are continuously working to improve the accessibility of our web experience for everyone, and we welcome feedback and accommodation requests. If you wish to report an issue or seek an accommodation, please let us know. Zillow Prize - Build a machine learning algorithm that can challenge Zestimates, the Zillow real estate price estimation algorithm. Featured competitions attract some of the most formidable experts, and offer prize pools going as high as a million dollars. However, they remain accessible to anyone and everyone.

Web scraping can often lead to you having scraped address data which are unstructured. If you have come across a large number of freeform address as a single string, for example – “9 Downing St Westminster London SW1A, UK”, you know how hard it would be to validate, compare and deduplicate these addresses. To start with you’ll have to split this address into a more structured form with house number, street name, city, state, country and zip code separately. It’s quite easy to parse address in Python and this tutorial will show you how.

Available Python Address Parser Packages

Python provides few packages to parse address in python –

• Address – This package is an address parsing library, it takes the guesswork out of using addresses in your applications.
• USAAddress – USAAddress is a python library for parsing unstructured address strings into address components, using advanced NLP methods. You can try their web interface at the link here.
• Street Address – Used as a street address formatter and parser. Based on the test cases from http://pyparsing.wikispaces.com/file/view/streetAddressParser.py
  

These packages get the job done for most of the addresses, using Natural Language Processing.

Address Parsing using the Google Maps Geocoding API

In this tutorial, we will show you how to convert a freeform single string address into a structured address with Latitude and Longitude using Google Maps Geocoding API. You can also use this API for Reverse Geocoding. i.e., you can convert geo-coordinates into addresses.

What is Geocoding?

Geocoding is the process of converting addresses such as – “71 Pilgrim Avenue Chevy Chase, Md 20815” into geographic coordinates like – latitude 38.9292172, longitude -77.07120479.

Google Maps Geocoding API

Google Maps Geocoding API is a service that provides geocoding and reverse geocoding for an address. So this Python script is a kind of wrapper for this API.

Each Google Maps Web Service request requires an API key that is freely available with a Google Account at Google Developers Console. The type of API key you need is a Server key.

How to get an API Key

1. Visit the Google Developers Console and log in with a Google Account.
2. Select one of your existing projects, or create a new project.
3. Enable the Geocoding API.
4. Create a new Server Key.
5. You can restrict requests to a particular IP address, but it is optional.

Important: Do not share your API Key, take care to keep them secure. You can delete an old one and generate a new one if needed.

API Usage Limits

Standard usage: 2500 free requests per day and 50 requests per second

Shift gears. Premium usage: 100,000 requests per day and 50* server-side requests per second

* The default limit can be changed

A Simple Demo – Parse Address using Python

The script below can accept address strings as a CSV, or you can just paste the addresses into a list. The script would output the results as a clean CSV file.

If the embed to parse address in python above does not work, you can get the code from GIST here.

Save the file and run the script in command prompt or terminal as:

Once it completes running, you will get an output in a CSV file data.csv. You can modify the file name from line no. 47. You can also modify the code to supply the file name as a positional argument too.

You can go ahead and modify the lines that read the addresses and writes it, to read from a data pipeline and write it to a database. It’s relatively easy, but beyond the scope of this simple demonstration.

Let us know in comments below how this script to parse address in python worked for you or if you have a better solution.

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The amounts of time students spend on this class greatly vary, based on their backgrounds, and what they may already know. Some former students told us they spent about 40-60 hours on each homework assignment (we have 4 big assignments, and no exams), and some reported much less. For example, for the homework assignment about D3 visualization programming, students who are completely new to javascript, css, and html likely will spend significantly more time than their peers who have already tried them before. Some former students who do not have a computer science background found the homework assignments challenging, would take significant time and effort, but were rewarding, fun, and 'do-able.'

Students have at least 3 weeks to complete each homework assignment. Some students waited until the last week, and could not finish.It is critical to plan ahead and prepare for the significant time needed.

Some programming assignments involve high-level languages or scripting (e.g., Python, Java, SQL etc.). Some assignments involve web programming and D3 (e.g., Javascript, CSS, HTML). For example, an assignment on Hadoop and Spark may require you to learn some basic Java and Scala quickly, which should not be too challenging if you already know another high-level language like Python or C++. It is unlikely that you all know tools/skills needed in the programing tasks, so you are expected to learn many of them on the fly.

Basic linear algebra, probability and statistics knowledge is also expected.

Trulia Home Values

• 8GB RAM (16GB recommended)
• 512GB disk (SSD recommended). Some assignments use data files that are more than a few GBs, and some uses virtual machines that can easily take up more than tens of GBs. It is typical for some project teams to use large datasets that are more than a few or tens of GBs.
• Dual-core Core i5 (8th generation or better recommended)

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