Update

scrape/Readme.md

@@ -0,0 +1,12 @@
+# Scrape Usse
+Funda scraper that automatically calculates the distance to several points in the Netherlands. Project relies heavily on a slightly modified version of the funda-scraper that is available on github.
+
+## Usage
+Update the ``URL`` parameter to use your filtered url from Funda. 
+```python
+URL = "https://www.funda.nl/zoeken/koop?selected_area=%5B%22utrecht,15km%22%5D&price=%22-400000%22&object_type=%5B%22house%22%5D"
+```
+
+Next you should be able to scrape the data from funda. See the RTD for more docs on how to setup OSRM and use the results.
+
+

scrape/scrape_README.md

@@ -0,0 +1,34 @@
+# FundaScraper
+
+`FundaScaper` provides you the easiest way to perform web scraping from Funda, the Dutch housing website. 
+You can find listings from either house-buyer or rental market, and you can find historical data from the past few year.
+
+
+## Install
+```
+pip install funda-scraper
+```
+
+## Quickstart 
+```
+from funda_scraper import FundaScraper
+
+scraper = FundaScraper(area="amsterdam", want_to="rent", find_past=False)
+df = scraper.run()
+df.head()
+```
+![image](https://i.imgur.com/mmN9mjQ.png)
+
+You can pass several arguments to `FundaScraper()` for customized scraping:
+- `area`: Specify the city or specific area you want to look for, eg. Amsterdam, Utrecht, Rotterdam, etc
+- `want_to`: You can choose either `buy` or `rent`, which finds houses either for sale or for rent. 
+- `find_past`: Specify whether you want to check the historical data. The default is `False`.
+- `n_pages`: Indicate how many pages you want to look up. The default is `1`. 
+
+
+## Advanced usage
+
+You can check the [example notebook](https://colab.research.google.com/drive/1hNzJJRWxD59lrbeDpfY1OUpBz0NktmfW?usp=sharing) for further details. 
+Please give me a [star](https://github.com/whchien/funda-scraper) if you find this project helpful. 
+
+

scrape/usse.py

@@ -56,13 +56,10 @@ def generate_json(houses):
         destination_location = res.point
         destination_location = [destination_location.longitude, destination_location.latitude]
         
-        # distance_matrix  = gmaps.distance_matrix(origin_locations['nfi_location'], destination_location, mode = 'driving')
-        
         out_dict['name'] = f"{address}_{count}" # Fix for duplicate names in dictionary.
         out_dict['position'] = destination_location
 
         
-        
         for key in houses.keys():
             out_dict[key] = houses.__getattr__(key).get(i)