#product_description

[iwebscrapingblogs]

How To Scrape Walmart for Product Information Using Python

In the ever-expanding world of e-commerce, Walmart is one of the largest retailers, offering a wide variety of products across numerous categories. If you're a data enthusiast, researcher, or business owner, you might find it useful to scrape Walmart for product information such as prices, product descriptions, and reviews. In this blog post, I'll guide you through the process of scraping Walmart's website using Python, covering the tools and libraries you'll need as well as the code to get started.

Why Scrape Walmart?

There are several reasons you might want to scrape Walmart's website:

Market research: Analyze competitor prices and product offerings.

Data analysis: Study trends in consumer preferences and purchasing habits.

Product monitoring: Track changes in product availability and prices over time.

Business insights: Understand what products are most popular and how they are being priced.

Tools and Libraries

To get started with scraping Walmart's website, you'll need the following tools and libraries:

Python: The primary programming language we'll use for this task.

Requests: A Python library for making HTTP requests.

BeautifulSoup: A Python library for parsing HTML and XML documents.

Pandas: A data manipulation library to organize and analyze the scraped data.

First, install the necessary libraries:

shell

Copy code

pip install requests beautifulsoup4 pandas

How to Scrape Walmart

Let's dive into the process of scraping Walmart's website. We'll focus on scraping product information such as title, price, and description.

1. Import Libraries

First, import the necessary libraries:

python

Copy code

import requests from bs4 import BeautifulSoup import pandas as pd

2. Define the URL

You need to define the URL of the Walmart product page you want to scrape. For this example, we'll use a sample URL:

python

Copy code

url = "https://www.walmart.com/search/?query=laptop"

You can replace the URL with the one you want to scrape.

3. Send a Request and Parse the HTML

Next, send an HTTP GET request to the URL and parse the HTML content using BeautifulSoup:

python

Copy code

response = requests.get(url) soup = BeautifulSoup(response.text, "html.parser")

4. Extract Product Information

Now, let's extract the product information from the HTML content. We will focus on extracting product titles, prices, and descriptions.

Here's an example of how to do it:

python

Copy code

# Create lists to store the scraped data product_titles = [] product_prices = [] product_descriptions = [] # Find the product containers on the page products = soup.find_all("div", class_="search-result-gridview-item") # Loop through each product container and extract the data for product in products: # Extract the title title = product.find("a", class_="product-title-link").text.strip() product_titles.append(title) # Extract the price price = product.find("span", class_="price-main-block").find("span", class_="visuallyhidden").text.strip() product_prices.append(price) # Extract the description description = product.find("span", class_="price-characteristic").text.strip() if product.find("span", class_="price-characteristic") else "N/A" product_descriptions.append(description) # Create a DataFrame to store the data data = { "Product Title": product_titles, "Price": product_prices, "Description": product_descriptions } df = pd.DataFrame(data) # Display the DataFrame print(df)

In the code above, we loop through each product container and extract the title, price, and description of each product. The data is stored in lists and then converted into a Pandas DataFrame for easy data manipulation and analysis.

5. Save the Data

Finally, you can save the extracted data to a CSV file or any other desired format:

python

Copy code

df.to_csv("walmart_products.csv", index=False)

Conclusion

Scraping Walmart for product information can provide valuable insights for market research, data analysis, and more. By using Python libraries such as Requests, BeautifulSoup, and Pandas, you can extract data efficiently and save it for further analysis. Remember to use this information responsibly and abide by Walmart's terms of service and scraping policies.

[codehunter]

Flask Get all products from table and iterate over them

I'm writing a website that uses Flask as the backend, and I am trying to add all products from a table into the webpage.

I've never used SQLAlchemy or anything like it, so I'm not sure how exactly to get all the products from a database, and then categorize them as I need for the website.

Here is the code I'm using in views.py

@app.route('/products')def products(): product = [ { 'product_name': 'T-Shirts', 'product_price': '18.00', 'product_img': '', 'product_description': 'A t-shirt.' }, { 'product_name': 'TShirts 2', 'product_price': '25.00', 'product_img': '', 'product_description': 'A t-shirt, again.' }, { 'product_name': 'TShirts 3', 'product_price': '25.00', 'product_img': '', 'product_description': 'A t-shirt, again.' }, { 'product_name': 'TShirts 4', 'product_price': '25.00', 'product_img': '', 'product_description': 'A t-shirt, again.' }, { 'product_name': 'TShirts 5', 'product_price': '25.00', 'product_img': '', 'product_description': 'A t-shirt, again.' }, { 'product_name': 'TShirts 6', 'product_price': '25.00', 'product_img': '', 'product_description': 'A t-shirt, again.' }, { 'product_name': 'TShirts 7', 'product_price': '25.00', 'product_img': '', 'product_description': 'A t-shirt, again.' }, { 'product_name': 'TShirts 8', 'product_price': '25.00', 'product_img': '', 'product_description': 'A t-shirt, again.' }, { 'product_name': 'TShirts 9', 'product_price': '25.00', 'product_img': '', 'product_description': 'A t-shirt, again.' }, { 'product_name': 'TShirts 10', 'product_price': '25.00', 'product_img': '', 'product_description': 'A t-shirt, again.' }, { 'product_name': 'TShirts 11', 'product_price': '25.00', 'product_img': '', 'product_description': 'A t-shirt, again.' }, { 'product_name': 'TShirts 12', 'product_price': '25.00', 'product_img': '', 'product_description': 'A t-shirt, again.' }, { 'product_name': 'TShirts 13', 'product_price': '25.00', 'product_img': '', 'product_description': 'A t-shirt, again.' }, { 'product_name': 'TShirts 14', 'product_price': '25.00', 'product_img': '', 'product_description': 'A t-shirt, again.' }, { 'product_name': 'TShirts 15', 'product_price': '25.00', 'product_img': '', 'product_description': 'A t-shirt, again.' }, ] return render_template("products.html", title="Products", products = product)

Here is the code in my template (This works perfectly):

{% extends "base.html" %}{% block content %} <div class="row"> <div class="large-4 small-12 columns"> <img src="{{ url_for('static', filename='img/Straight_Up_Performance_Logo.png') }}"> <div class="hide-for-small panel"> <h3>Straight Up Racing</h3> <h5 class="subheader">Straight Up Racing believes that the best way to get in touch with our customers is for them to call us. Give us a call at (406) 239-4975 to order yours today! </h5> </div> </div> <div class="large-8 columns"> <div class="row container1"> {% for product in products %} <div class="large-4 small-4 columns"> <ul class="pricing-table"> {% if product.product_name %}<li class="title">{{ product.product_name }}</li>{% endif %} {% if product.product_price %}<li class="price">${{ product.product_price }} + S&H</li>{% endif %} {% if product.product_img %}<li class="bullet-item">{{ product.product_img }}</li>{% endif %} {% if product.product_description %}<li class="description">{{ product.product_description }}</li>{% endif %} </ul> </div> {% endfor %} </div> </div> </div>{% endblock %}

Here is the code that created the Product table:

class Product(db.Model): product_id = db.Column(db.Integer, primary_key=True) product_name = db.Column(db.String(64), index = True) product_price = db.Column(db.Float, index=True) product_img = db.Column(db.String(200), index=True) product_description = db.Column(db.String(1000), index=True) def __repr__(self): return '<Product %r>' % (self.product_name)

My question isn't why it's not currently pulling data from the database, I know it's using what I manually define. I don't know how to go about pulling it from the database using SQLAlchemy?

https://codehunter.cc/a/flask/flask-get-all-products-from-table-and-iterate-over-them

[fealesecurity]

Virgin Coconut Oil Benefits

Organic India's virgin coconut oil offers a multitude of benefits. Rich in natural antioxidants and healthy fats, it promotes skin and hair health, boosts immunity, aids digestion, and supports overall well-being.

[organic-india-green-tea]

Clean Kit

Clean Kit

The Organic India Clean-7 Days Kit is a set of organic herbal supplements designed to support the body's natural detoxification process and promote overall wellness. The kit contains seven different products that are formulated to work together to help cleanse the body of toxins, improve digestion, boost energy levels, and enhance mental clarity.

Each product in the Clean-7 Days Kit is made with a blend of organic herbs and natural ingredients that are carefully selected for their detoxifying and nourishing properties. These ingredients include herbs such as turmeric, ginger, neem, and ashwagandha, as well as other beneficial compounds like probiotics and fiber.

[a2zwebinfotech-blog-com]

Content is the primary tool in your Digital Marketing kit. Content writing for websites requires skills to attract audiences and provide them appropriate information to keep them on websites for maximum possible time.

For more Information Visit here: https://bit.ly/3dPELea

[dsfragrances]

#_MIMOSA_ABSOLUTE #Natural_Absolute_100% #Product_Description Name : #Mimosa_Absolute_Oil Botanical Name : #_Acacia_Decurrens Plant Part : Leaves & Flowers HSN CODE : 3301 Extraction Mathod : #_Solvent_Extracted Color : #_Yellow Storage : Cool & Dry Place https://www.dsfragrancesindia.com/absolute-oil.htm https://www.instagram.com/p/CMIP1ctB0Lg/?igshid=1ez63h974e4fo

[smartgadgetsbd-blog]

#Meizu_EP51 Wireless Bluetooth Earphone Stereo Headset #Waterproof Sports Earphone With MIC Microphone #Supporting Apt-X --- #Price: 2150TK --- #Product_Description: - IPX4 waterproof degree, free to do sports even for heavy sweat - Bluetooth connection, no wire's trouble while exercising - Nano water resistance layer, anti-sweat, more durable to use - Ergonomic designed earbud tips, ultra-soft, very comfortable to wear - Drive-by-wire, with microphone and volume control - High strength, anti-tangled elastic cable --- #Order_Process: For order just send a message our inbox with your Name, Full Address, Product name & Phone number. --- #Delivery_Process: #Home_Delivery available inside DHAKA. Product will be Deliver #within_24_Hours. For outside of DHAKA, Courier charge applicable and You have to pay Advance 100tk, just for confirmation. (minimum order value 500TK) https://www.instagram.com/p/BvKoi66AW1R/?utm_source=ig_tumblr_share&igshid=1kf1qm51n6z7k

[iwebscraping]

How to Extract Product Data from Walmart with Python and BeautifulSoup

Walmart is the leading retailer with both online stores as well as physical stores around the world. Having a larger product variety in the portfolio with $519.93 Billion of net sales, Walmart is dominating the retail market as well as it also provides ample data, which could be utilized to get insights on product portfolios, customer’s behavior, as well as market trends.

In this tutorial blog, we will extract product data from Walmart s well as store that in the SQL databases. We use Python for scraping a website. The package used for the scraping exercise is called BeautifulSoup. Together with that, we have also utilized Selenium as it helps us interact with Google Chrome.

Scrape Walmart Product Data

The initial step is importing all the required libraries. When, we import the packages, let’s start by setting the scraper’s flow. For modularizing the code, we initially investigated the URL structure of Walmart product pages. A URL is an address of a web page, which a user refers to as well as can be utilized for uniquely identifying the page.

Here, in the given example, we have made a listing of page URLs within Walmart’s electronics department. We also have made the list of names of different product categories. We would use them in future to name the tables or datasets.

You may add as well as remove the subcategories for all major product categories. All you require to do is going to subcategory pages as well as scrape the page URL. The address is general for all the available products on the page. You may also do that for maximum product categories. In the given image, we have showed categories including Toys and Food for the demo.

In addition, we have also stored URLs in the list because it makes data processing in Python much easier. When, we have all the lists ready, let’s move on for writing a scraper.

Also, we have made a loop for automating the extraction exercise. Although, we can run that for only one category as well as subcategory also. Let us pretend, we wish to extract data for only one sub-category like TVs in ‘Electronics’ category. Later on, we will exhibit how to scale a code for all the sub-categories.

Here, a variable pg=1 makes sure that we are extracting data for merely the first URL within an array ‘url_sets’ i.e. merely for the initial subcategory in main category. When you complete that, the following step might be to outline total product pages that you would wish to open for scraping data from. To do this, we are extracting data from the best 10 pages.

Then, we loop through a complete length of top_n array i.e. 10 times for opening the product pages as well as scrape a complete webpage structure in HTML form code. It is like inspecting different elements of web page as well as copying the resultants’ HTML code. Although, we have more added a limitation that only a part of HTML structure, which lies in a tag ‘Body’ is scraped as well as stored as the object. That is because applicable product data is only within a page’s HTML body.

This entity can be used for pulling relevant product data for different products, which were listed on an active page. For doing that, we have identified that a tag having product data is the ‘div’ tag having a class, ‘search-result-gridview-item-wrapper’. Therefore, in next step, we have used a find_all function for scraping all the occurrences from the given class. We have stored this data in the temporary object named ‘codelist’.

After that, we have built the URL of separate products. For doing so, we have observed that different product pages begin with a basic string called ‘https://walmart.com/ip’. All unique-identifies were added only before this string. A unique identifier was similar as a string values scraped from a ‘search-result-gridview-item-wrapper’ items saved above. Therefore, in the following step, we have looped through a temporary object code list, for constructing complete URL of any particular product’ page.

With this URL, we will be able to scrape particular product-level data. To do this demo, we have got details like unique Product codes, Product’s name, Product page URL, Product_description, name of current page’s category where a product is positioned, name of the active subcategory where the product is positioned on a website (which is called active breadcrumb), Product pricing, ratings (Star ratings), number of reviews or ratings for a product as well as other products suggested on the Walmart’s site similar or associated to a product. You may customize this listing according to your convinience.

The code given above follows the following step of opening an individual product page, based on the constructed URLs as well as scraping the products’ attributes, as given in the listing above. When you are okay with a listing of attributes getting pulled within a code, the last step for a scraper might be to attach all the product data in the subcategory within a single frame data. The code here shows that.

A data frame called ‘df’ would have all the data for products on the best 10 pages of a chosen subcategory within your code. You may either write data on the CSV files or distribute it to the SQL database. In case, you need to export that to the MySQL database within the table named ‘product_info’, you may utilize the code given below:

You would need to provide the SQL database credentials and when you do it, Python helps you to openly connect the working environment with the database as well as push the dataset straight as the SQL dataset. In the above code, in case the table having that name exists already, the recent code would replace with the present table. You may always change a script to evade doing so. Python provides you an option to 'fail', 'append', or 'replace' data here.

It is the basic code structure, which can be improved to add exclusions to deal with missing data or later loading pages. In case, you choose to loop the code for different subcategories, a complete code would look like:

import  os import  selenium.webdriver import  csv import  time import  pandas   as   pd from  selenium   import    webdriver from  bs4   import   BeautifulSoup url_sets=["https://www.walmart.com/browse/tv-video/all-tvs/3944_1060825_447913", "https://www.walmart.com/browse/computers/desktop-computers/3944_3951_132982", "https://www.walmart.com/browse/electronics/all-laptop-computers/3944_3951_1089430_132960", "https://www.walmart.com/browse/prepaid-phones/1105910_4527935_1072335", "https://www.walmart.com/browse/electronics/portable-audio/3944_96469", "https://www.walmart.com/browse/electronics/gps-navigation/3944_538883/", "https://www.walmart.com/browse/electronics/sound-bars/3944_77622_8375901_1230415_1107398", "https://www.walmart.com/browse/electronics/digital-slr-cameras/3944_133277_1096663", "https://www.walmart.com/browse/electronics/ipad-tablets/3944_1078524"] categories=["TVs","Desktops","Laptops","Prepaid_phones","Audio","GPS","soundbars","cameras","tablets"] # scraper for pg in range(len(url_sets)):    # number of pages per category    top_n= ["1","2","3","4","5","6","7","8","9","10"]    # extract page number within sub-category    url_category=url_sets[pg]    print("Category:",categories[pg])    final_results = [] for i_1 in range(len(top_n)):    print("Page number within category:",i_1)    url_cat=url_category+"?page="+top_n[i_1]    driver= webdriver.Chrome(executable_path='C:/Drivers/chromedriver.exe')    driver.get(url_cat)    body_cat = driver.find_element_by_tag_name("body").get_attribute("innerHTML")    driver.quit()    soupBody_cat = BeautifulSoup(body_cat) for tmp in soupBody_cat.find_all('div', {'class':'search-result-gridview-item-wrapper'}):    final_results.append(tmp['data-id'])     # save final set of results as a list         codelist=list(set(final_results)) print("Total number of prods:",len(codelist)) # base URL for product page url1= "https://walmart.com/ip" # Data Headers WLMTData = [["Product_code","Product_name","Product_description","Product_URL", "Breadcrumb_parent","Breadcrumb_active","Product_price",         "Rating_Value","Rating_Count","Recommended_Prods"]] for i in range(len(codelist)):    #creating a list without the place taken in the first loop    print(i)    item_wlmt=codelist[i]    url2=url1+"/"+item_wlmt    #print(url2) try:    driver= webdriver.Chrome(executable_path='C:/Drivers/chromedriver.exe') # Chrome driver is being used.    print ("Requesting URL: " + url2)    driver.get(url2)   # URL requested in browser.    print ("Webpage found ...")    time.sleep(3)    # Find the document body and get its inner HTML for processing in BeautifulSoup parser.    body = driver.find_element_by_tag_name("body").get_attribute("innerHTML")    print("Closing Chrome ...") # No more usage needed.    driver.quit()     # Browser Closed.    print("Getting data from DOM ...")    soupBody = BeautifulSoup(body) # Parse the inner HTML using BeautifulSoup    h1ProductName = soupBody.find("h1", {"class": "prod-ProductTitle prod-productTitle-buyBox font-bold"})    divProductDesc = soupBody.find("div", {"class": "about-desc about-product-description xs-margin-top"})    liProductBreadcrumb_parent = soupBody.find("li", {"data-automation-id": "breadcrumb-item-0"})    liProductBreadcrumb_active = soupBody.find("li", {"class": "breadcrumb active"})    spanProductPrice = soupBody.find("span", {"class": "price-group"})    spanProductRating = soupBody.find("span", {"itemprop": "ratingValue"})    spanProductRating_count = soupBody.find("span", {"class": "stars-reviews-count-node"})    ################# exceptions #########################    if divProductDesc is None:        divProductDesc="Not Available"    else:        divProductDesc=divProductDesc    if liProductBreadcrumb_parent is None:        liProductBreadcrumb_parent="Not Available"    else:        liProductBreadcrumb_parent=liProductBreadcrumb_parent    if liProductBreadcrumb_active is None:        liProductBreadcrumb_active="Not Available"    else:        liProductBreadcrumb_active=liProductBreadcrumb_active    if spanProductPrice is None:        spanProductPrice="NA"    else:        spanProductPrice=spanProductPrice    if spanProductRating is None or spanProductRating_count is None:        spanProductRating=0.0        spanProductRating_count="0 ratings"    else:        spanProductRating=spanProductRating.text        spanProductRating_count=spanProductRating_count.text    ### Recommended Products    reco_prods=[]    for tmp in soupBody.find_all('a', {'class':'tile-link-overlay u-focusTile'}):        reco_prods.append(tmp['data-product-id'])    if len(reco_prods)==0:        reco_prods=["Not available"]    else:        reco_prods=reco_prods    WLMTData.append([codelist[i],h1ProductName.text,ivProductDesc.text,url2,    liProductBreadcrumb_parent.text,    liProductBreadcrumb_active.text, spanProductPrice.text, spanProductRating,    spanProductRating_count,reco_prods]) except Exception as e:    print (str(e)) # save final result as dataframe    df=pd.DataFrame(WLMTData)    df.columns = df.iloc[0]    df=df.drop(df.index[0]) # Export dataframe to SQL import sqlalchemy database_username = 'ENTER USERNAME' database_password = 'ENTER USERNAME PASSWORD' database_ip       = 'ENTER DATABASE IP' database_name     = 'ENTER DATABASE NAME' database_connection = sqlalchemy.create_engine('mysql+mysqlconnector://{0}:{1}@{2}/{3}'. format(database_username, database_password, database_ip, base_name)) df.to_sql(con=database_connection, name='‘product_info’', if_exists='replace',flavor='mysql')

You may always add additional complexity into this code for adding customization to the scraper. For example, the given scraper will take care of the missing data within attributes including pricing, description, or reviews. The data might be missing because of many reasons like if a product get out of stock or sold out, improper data entry, or is new to get any ratings or data currently.

For adapting different web structures, you would need to keep changing your web scraper for that to become functional while a webpage gets updated. The web scraper gives you with a base template for the Python’s scraper on Walmart.

Want to extract data for your business? Contact iWeb Scraping, your data scraping professional!

[rabbi1516]

Write professional Amazon Product Listing Services

[commentsense888]

Product description... by ahotko https://www.reddit.com/r/ProgrammerHumor/comments/dbp8r6/product_description/?utm_source=ifttt

[mrmomoinfo]

Tznzxm Case for ZTE ZMax 10, ZTE Z6250 Back Case,Consumer Cellular ZMax 10 Case, Owl Painting Design Flexible Soft TPU Scratch Resistant Non-Slip Protective Cover Slim Phone Case for ZTE Z6250

Tznzxm Case for ZTE ZMax 10, ZTE Z6250 Back Case,Consumer Cellular ZMax 10 Case, Owl Painting Design Flexible Soft TPU Scratch Resistant Non-Slip Protective Cover Slim Phone Case for ZTE Z6250

Price: (as of – Details) product_description: Design:Slim Flexible TPU Painting Owl Back Case Compatible Model:Consumer Cellular ZMax 10 /ZTE ZMax 10 / ZTE Z6250 6.49″ Tznzxm is a professional 3c accessories retail store, provides a variety of stylish 3C accessories for electronic products, with high quality and good customer service. Features: Material: TPU Access to all controls and functions…

[onlinedealsmart]

ZOOKKI Camera Accessories Kit for Gopro Hero 7 6 5 4 3, Sports Accessories Kit for SJ4000/SJ5000/AKASO EK5000 EK7000/Xiaomi Yi 4K/WiMiUS Black Silver

ZOOKKI Camera Accessories Kit for Gopro Hero 7 6 5 4 3, Sports Accessories Kit for SJ4000/SJ5000/AKASO EK5000 EK7000/Xiaomi Yi 4K/WiMiUS Black Silver

Price: (as of – Details) ZOOKKI, car, floating, three, tripod, surface, adapters, flat, adhesive] in product_description: [‘Attention: the camera and controller showed in the picture are not included in the kit. This is an essential accessories kit for all the lovers. It will comfortable with all the sizes of cameras includes hero 4/3+/3/2/1. SJ4000 SJ5000 SJ6000 cameras. Packing list: 1 x…

[a2zwebinfotech-blog-com]

Available Professional Content Writer at a Cost-Effective Price.

For more information click here: https://www.a2zwebinfotech.com/services/content-service

[prevajconsultants]

SM Product Gallery - Responsive Magento Module (Magento Extensions)

SM Product Gallery is very simple but effective module to display products. With SM Product Gallery, your products’ photo will be well arranged as a gallery with grid layout. Each photo will be displayed on the right side with a large image and short description. This flexible Magento module is totally worth for your website.

In addition, the friendly user Admin interface is really easy for you to control any parameter we provided. You can configures it as your own ideas and place it on any position on your site. Lets see the Demo to feel more.

Let’s access this module’s demo to have the best overview!

<tbody> # Main Features 1. Support Magento 1.7.x, Magento 1.8.x, Magento 1.9.x 2. Fully compatible with IE8+, Firefox 2+, Flock 0.7+, Netscape, Safari, Opera 9.5 and Chrome 3. Support fully responsive layout 4. Allow to set the number of columns for devices that have different screen widths 5. Allow to select a main category or more categories to be shown 6. Allow to include or exclude Child categories 7. Support to control showing number of products 8. Support to show or hide Featured Products 9. Allow to change max length of title/description of item 10. Allow to display/hide Item Title/Description/Price/Reviews Summary/Add to Cart/Add Wishlish/Add Compare 11. Allow to order to get image with options such as product_image, product_description 12. Allow to change width/height/background of images 13. Allow to set autoplay 14. Support to increase or decrease the speed of each transition effect 15. Support to open link in Same Window, New Window and Popup Window 16. Support pre- and post- text with each instance 17. Support to cache the content of this module. </tbody>

from CodeCanyon new items https://ift.tt/2HFNUG1 via IFTTT https://goo.gl/zxKHwc

[smartgadgetsbd-blog]

#Xiaomi_WiFi_Amplifier_Pro 300Mbps Amplificador Wi-Fi Repeater Wifi Signal Cover Extender Repeater 2.4G Mi Wireless Router ---- #PRICE_1090tk ---- #Product_Description: Xiaomi Mi WiFi Repeater Pro is designed to extended WiFi coverage and strength the signal by repeating the signal from your home or office wireless router and redistributing. With up to 300Mbps speed and built in dual antennas, it eliminates WiFi dead zones and make you enjoy seamless HD streaming, online gaming everywhere. It works with any standard WiFi router. ---- #Order_Process: For order just send a message our inbox with your Name, Full Address, Product name & Phone number with quantity. You may call our Hotline 01610311621 for direct order. (24/7 Online) ---- #Delivery_Process: #Home_Delivery available inside DHAKA. Product will be Deliver #within_24_Hours. For outside of DHAKA, Courier charge applicable and You have to pay Advance 100tk, just for confirmation. https://www.instagram.com/p/But6axIBvAm/?utm_source=ig_tumblr_share&igshid=bdfz0bsw3yi3

[waggingonline-blog]

Cozy Double Sided Fleece Dogs Bed $50.67 https://goo.gl/zFM9bQ [short_description] High sided elegant dogs bed with free delivery. Buy this cozy double sided fleece dog bed and signup to our news letter for exclusive deals. Wagging Online pet store and more. Brand Name: KIMHOME PET Wash Style: Mechanical Wash Feature: Breathable Weight: 0.97 Pattern: Dot Item Type: Beds & Sofas Model Number: PC150 Size: S/M/L Filler: High-grade sponge Applicable Dog Breed: dog & cat S Size: 45*37*17cm M Size: 65*50*18cm L Size: 80*70*22cm [/short_description][product_description] Brand Name: KIMHOME PET Wash Style: Mechanical Wash Feature: Breathable Weight: 0.97 Pattern: Dot Item Type: Beds & Sofas Model Number: PC150 Size: S/M/L Filler: High-grade sponge Applicable Dog Breed: dog & cat S Size: 45*37*17cm M Size: 65*50*18cm L Size: 80*70*22cm Wagging Online Pet Store strive to provide accurate information so you can make an informed decision, this includes but not limited to; sizing, colours, dimensions, ingredients and packaging. When changes do occur there can be a delay with this information being displayed online and products may vary slightly from the displayed product photos. Wagging Online provide free delivery with every order and are the link between you and your four legged friend. [/product_description] https://waggingonline.com/