How Do We Extract Store Location From Target.Com Using Python?
Web extracting is an efficient & faster way to acquire data of store sites for a specific website sooner grasping time to collect details by own. This blog is about Scrape Store Locations from Target.com contact details and store locations accessible on Target.com, which is a leading E-Commerce store in the USA.
Data Fields That Can Scraped
For this Blog, our extractor will scrape the data of store details by a specified zip code.
Name of Store
Store Address
Hours Open
Week Day
Phone Number
Pricing
Store Contact Number
Seller
Product Image
Product Image URL
Brand
Number of Reviews
Product Size
Description
Product ID
Product Variation
Rating Histogram
Customers Reviews
Online Availability Status
Store Availability Status
There are many data we can scrape from the store details page on Target like grocery & pharmacy timings, but as of now, we need to stick with these.
Extracting Logic
The explore outcome page utilizing Python Requests you need to Download HTML – if you have the URL. We utilize Python desires to load the complete HTML of the particular page.
Build URL of exploring outcome from Target.com. Let’s choose the location, New York. We will have to make this URL by own to extract outcome from that page. https://www.target.com/store-locator/find-stores?address=12901&capabilities=&concept=
Save the information to a JSON format.
Necessities
There are Web extracting blogs that utilize Python 3, we require some correspondences for parsing & downloading the HTML. Here are some of the correspondence.
Install Python 3 and Pip
You have this guidebook, how you can mount Python 3 in Linux– http://docs.python-guide.org/en/latest/starting/install3/linux/
Mac operator can also use thig guidebook – http://docs.python-guide.org/en/latest/starting/install3/osx/
Windows operators can click here – https://realpython.com/installing-python/
Install Packages
PIP to mount the required correspondence in Python ( https://pip.pypa.io/en/stable/installing/ )
UnicodeCSV for manage Unicode qualities in the result file. Install it utilizing pip unicodecsv.
Request Python, to download & make requests for the content HTML of the pages ( http://docs.python-requests.org/en/master/user/install/).
If you like the code, then you need to check the below-given link for Python 2.7 here.
Running the Extractor
Suppose the extractor is called target.py. Once you type name in prompt command laterally with a -h
usage: target.py [-h] zipcode positional arguments: zipcode Zipcode optional arguments: -h, --help show this help message and exit
The zip code is to discover the warehouse nearby a specific location.
In case, you find the entire Target warehouse in and nearby New-York we will put the zip code as 12901:
python target.py 12901
This will generate a JSON productivity file name 12901-locations. json will remain in a similar file like a script.
The output folder will look comparable to this.
{ "County": "Clinton", "Store_Name": "Plattsburgh", "State": "NY", "Street": "60 Smithfield Blvd", "Stores_Open": [ "Monday-Friday", "Saturday", "Sunday" ], "Contact": "(518) 247-4961", "City": "Plattsburgh", "Country": "United States", "Zipcode": "12901-2151", "Timings": [ { "Week Day": "Monday-Friday", "Open Hours": "8:00 a.m.-10:00 p.m." }, { "Week Day": "Saturday", "Open Hours": "8:00 a.m.-10:00 p.m." }, { "Week Day": "Sunday", "Open Hours": "8:00 a.m.-9:00 p.m." } ] }
You can download the given below code at
Limitations
This code will work for scraping information of Target warehouse for entire zip codes accessible at Target. If you need to extract the information of millions of pages you need to read.
If you want expert help for extracting compound websites, contact Web Screen Scraping for all your queries.
