Month: December 2016

Scraping housing prices using Python Scrapy Part 2

This is the continuation of the previous post on “Scraping housing prices using Python Scrapy“. In this session, we will use Xpath to retrieve the corresponding fields from the targeted website instead of just having the full html page. For a preview on how to extract the information from a particular web page, you can refer to the following post “Retrieving stock news and Ex-date from SGX using python“.

Parsing the web page using Scrapy will require the use of Scrapy spider “parse” function. To test out the function, it might be an hassle to run Scrapy crawl command each time you try out a field as this means making requests to the website every single time.

There are two ways to go about it. One way is to let Scrapy cache the data. The other is to make use of the html webpage downloaded in the previous session. I have not really try out caching the information using scrapy but it is possible to run using Scrapy Middleware. Some of the links below might help to provide some ideas.

  1. https://doc.scrapy.org/en/0.12/topics/downloader-middleware.html
  2. http://stackoverflow.com/questions/22963585/using-middleware-to-ignore-duplicates-in-scrapy
  3. http://stackoverflow.com/questions/40051215/scraping-cached-pages

For utilizing the downloaded copy of the html page which is what I have been using, the following script demonstrate how it is done. The downloaded page is taken from this property website link. Create an empty script and input the following snippets, run the script as normal python script.

    import os, sys, time, datetime, re
    from scrapy.http import HtmlResponse

    #Enter file path
    filename = r'targeted file location'

    with open(filename,'r') as f:
        html =  f.read()

    response = HtmlResponse(url="my HTML string", body=html) # Key line to allow Scrapy to parse the page

    item = dict()

    for sel in response.xpath("//tr")[10:]:
        item['id'] = sel.xpath('td/text()')[0].extract()
        item['block_add'] = sel.xpath('td/a/span/text()')[0].extract()
        individual_block_link = sel.xpath('td/a/@href')[0].extract()
        item['individual_block_link'] = response.urljoin(individual_block_link)
        item['date'] = sel.xpath('td/text()')[3].extract()

        price = sel.xpath('td/text()')[4].extract()
        price = int(price.replace(',',''))
        price_k = price/1000
        item['price'] = price
        item['price_k'] = price_k
        item['size'] = sel.xpath('td/text()')[5].extract()
        item['psf'] = sel.xpath('td/text()')[6].extract()
        #agent = sel.xpath('td/a/span/text()')[1].extract()
        item['org_url_str'] = response.url

        for k, v in item.iteritems():
            print k, v

Once verified there are no issue retrieving the various components, we can paste the portion to the actual Scrapy spider parse function. Remember to exclude the statement “response = HtmlResponse …”.

From the url, we noticed that the property search results are available in multiple pages. The idea is to traverse each page and obtain the desired information from each page. This would need Scrapy to know the next url to go to. To parse the information, the same method can be use to retrieve the url link to the next page.

Below show the parse function use in the Scrapy spider.py.

def parse(self, response):

    for sel in response.xpath("//tr")[10:]:
        item = ScrapePropertyguruItem()
        item['id'] = sel.xpath('td/text()')[0].extract()
        item['block_add'] = sel.xpath('td/a/span/text()')[0].extract()
        individual_block_link = sel.xpath('td/a/@href')[0].extract()
        item['individual_block_link'] = response.urljoin(individual_block_link)
        item['date'] = sel.xpath('td/text()')[3].extract()

        price = sel.xpath('td/text()')[4].extract()
        price = int(price.replace(',',''))
        price_k = price/1000
        item['price'] = price
        item['price_k'] = price_k
        item['size'] = sel.xpath('td/text()')[5].extract()
        item['psf'] = sel.xpath('td/text()')[6].extract()
        #agent = sel.xpath('td/a/span/text()')[1].extract()
        item['org_url_str'] = response.url

        yield item

    #get next page link
    next_page = response.xpath("//div/div[6]/div/a[10]/@href")
    if next_page:
        page_url = response.urljoin(next_page[0].extract())
        yield scrapy.Request(page_url, self.parse)

For the next post, I will share how to migrate the running of spider to Scrapy Cloud

Related Posts

  1. Scraping housing prices using Python Scrapy
  2. Retrieving stock news and Ex-date from SGX using python

Automating Google Sheets with Python

This post demonstrate basic use of python to read/edit Google sheets. For fast setup, you can visit this link. Below is the setup procedure copied from the link itself.

  1. Use this wizard to create or select a project in the Google Developers Console and automatically turn on the API. Click Continue, then Go to credentials.
  2. On the Add credentials to your project page, click the Cancel button.
  3. At the top of the page, select the OAuth consent screen tab. Select an Email address, enter a Product name if not already set, and click the Save button.
  4. Select the Credentials tab, click the Create credentials button and select OAuth client ID.
  5. Select the application type Other, enter the name “Google Sheets API Quickstart”, and click the Create button.
  6. Click OK to dismiss the resulting dialog.
  7. Click the file_download (Download JSON) button to the right of the client ID.
  8. Move this file to your working directory and rename it client_secret.json.

The next stepĀ  will be to install the google client using pip.

pip install --upgrade google-api-python-client

The final step is to copy the sample from the same link. For the first time running the script, you would need to sign in with Google. Use the below command to link the sheets credentials to the targeted gmail account. Follow the instruction as from the prompt.

$ python name_of_script.py --noauth_local_webserver

You can easily access/modify the contents of the sheets especially if it is in the table format by linking it with Python Pandas.

# authorization: reference from link
credentials = get_credentials()
http = credentials.authorize(httplib2.Http())
discoveryUrl = ('https://sheets.googleapis.com/$discovery/rest?'
'version=v4')
service = discovery.build('sheets', 'v4', http=http,
discoveryServiceUrl=discoveryUrl)

# Target spreadsheet
spreadsheetId = 'your_spreadsheet_name'
rangeName = 'Sheet1!A1:N'

# read from spreadsheet
result = service.spreadsheets().values().get(
spreadsheetId=spreadsheetId, range=rangeName).execute()
values = result.get('values', [])

import pandas
# Pandas Dataframe with values and header
data_df = pd.DataFrame(values[1:], columns = values[0])
print data_df

Related Posts:

  1. Automating Ms Powerpoint with Python: https://simply-python.com/2014/07/04/rapid-generation-of-powerpoint-report-with-template-scanning
  2. Using Excel with Python: https://simply-python.com/2014/08/20/manage-and-extract-data-using-python-and-excel-tables