Kids’ Product Data Collection from FirstCry & Walmart – A Complete Technical Guide

Introduction

Kids’ product eCommerce is one of the fastest-growing retail categories globally. Platforms like FirstCry (India) and Walmart (USA) list thousands of:

• baby care items
• diapers
• toys
• kids’ fashion
• feeding bottles
• strollers & prams
• nursery furniture
• educational toys
• baby skincare
• maternity essentials

For consumer brands, distributors, analytics companies, and pricing teams, collecting this data is critical to:

• understand demand shifts
• track competitor products
• detect price gaps
• monitor discounts
• analyze new launches
• track stock-outs
• create retail intelligence dashboards

This tutorial teaches you exactly how to scrape FirstCry and Walmart using Python, Selenium, Requests, and BeautifulSoup — just like the Grab.com tutorial style.

Let’s build a complete Kids’ Product Data Extraction System.

Step 1: Install the Required Libraries

pip install selenium
pip install requests
pip install beautifulsoup4
pip install pandas
pip install lxml
pip install pillow

You will use:

• Selenium → load dynamic pages
• Requests → fetch HTML/images
• BeautifulSoup → parse data
• Pandas → store dataset
• Pillow → verify/download images

Step 2: Scrape FirstCry (India) Kids’ Products

FirstCry uses dynamic grids + lazy-loading.

Example category:

Baby Diapers:

https://www.firstcry.com/diapers

Start Selenium Browser

from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.common.keys import Keys
from time import sleep
import pandas as pd
import requests
from bs4 import BeautifulSoup

browser = webdriver.Chrome()
browser.get("https://www.firstcry.com/diapers")
sleep(4)

Scroll the Page to Load More Products

for _ in range(8):
    browser.find_element(By.TAG_NAME, "body").send_keys(Keys.END)
    sleep(2)

Extract Product Cards

items = browser.find_elements(By.XPATH, '//div[contains(@class,"listItems")]//li')

Extract Product Details

firstcry_records = []

for item in items:
    try:
        name = item.find_element(By.CLASS_NAME, "li_title").text
    except:
        name = ""

    try:
        price = item.find_element(By.CLASS_NAME, "li_final_price").text
    except:
        price = ""

    try:
        mrp = item.find_element(By.CLASS_NAME, "li_mrp").text
    except:
        mrp = ""

    try:
        url = item.find_element(By.TAG_NAME, "a").get_attribute("href")
    except:
        url = ""

    try:
        img = item.find_element(By.TAG_NAME, "img").get_attribute("src")
    except:
        img = ""

    firstcry_records.append({
        "platform": "FirstCry",
        "name": name,
        "price": price,
        "mrp": mrp,
        "url": url,
        "image_url": img
    })

Step 3: Scrape Walmart Kids’ Products (USA)

Example category:

Kids Toys:

https://www.walmart.com/browse/toys

Open Walmart Page

browser.get("https://www.walmart.com/browse/toys")
sleep(4)

Scroll to Load Dynamic Listings

for _ in range(8):
    browser.find_element(By.TAG_NAME, "body").send_keys(Keys.END)
    sleep(2)

Extract Walmart Product Cards

w_items = browser.find_elements(By.XPATH, '//div[contains(@class,"search-result-gridview-item")]')

Extract Walmart Product Details

walmart_records = []

for item in w_items:
    try:
        name = item.find_element(By.CLASS_NAME, "product-title-link").text
    except:
        name = ""

    try:
        price = item.find_element(By.CLASS_NAME, "price-main").text
    except:
        price = ""

    try:
        url = item.find_element(By.TAG_NAME, "a").get_attribute("href")
    except:
        url = ""

    try:
        img = item.find_element(By.TAG_NAME, "img").get_attribute("src")
    except:
        img = ""

    walmart_records.append({
        "platform": "Walmart",
        "name": name,
        "price": price,
        "mrp": "",
        "url": url,
        "image_url": img
    })

Step 4: Combine FirstCry + Walmart Data

df = pd.DataFrame(firstcry_records + walmart_records)
df.head()

Step 5: Clean Product Titles (Important for Kids Category)

Kids’ products often contain:

• pack sizes
• age ranges
• gender indicators
• quantity variations
• cartoon character names

Let’s normalize titles:

def clean_title(t):
    t = t.lower()
    remove_words = ["buy now", "discount", "pack of", "pcs", "girls", "boys"]
    for w in remove_words:
        t = t.replace(w, "")
    return t.strip()

df["clean_title"] = df["name"].apply(clean_title)

Step 6: Extract Age Group (0–3 months, 3–6 months, 1–3 years, etc.)

Using regex:

import re

def extract_age(t):
    match = re.search(r"(\d+\s?(months|years|year|month))", t)
    return match.group(1) if match else None

df["age_group"] = df["name"].apply(extract_age)

Step 7: Extract Pack Size (Diapers, Wipes, Feeding Items)

def extract_pack_size(t):
    match = re.search(r"(\d+)\s?(pcs|pieces|count)", t.lower())
    return match.group(1) if match else None

df["pack_size"] = df["name"].apply(extract_pack_size)

Step 8: Detect Product Type (Feeding, Toys, Apparel, Diapers)

def classify_type(t):
    t = t.lower()
    if "diaper" in t:
        return "Diapers"
    if "toy" in t or "lego" in t or "puzzle" in t:
        return "Toys"
    if "bottle" in t or "sipper" in t:
        return "Feeding"
    if "dress" in t or "shirt" in t:
        return "Kids Apparel"
    return "Other"

df["category"] = df["clean_title"].apply(classify_type)

Step 9: Download Product Images (Optional)

import os
from PIL import Image
from io import BytesIO

os.makedirs("kid_images", exist_ok=True)

def download_image(url, name):
    if url:
        response = requests.get(url)
        img = Image.open(BytesIO(response.content))
        img.save(f"kid_images/{name}.jpg")

for i, row in df.head(20).iterrows():   # download 20 sample images
    download_image(row["image_url"], f"product_{i}")

Step 10: Export the Final Kids’ Product Dataset

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

Sample Final Output

{
 "platform": "FirstCry",
 "name": "Pampers New Baby Diapers - Pack of 72",
 "price": "₹899",
 "mrp": "₹1099",
 "url": "https://www.firstcry.com/...",
 "image_url": "https://cdn.firstcry.com/...",
 "clean_title": "pampers new baby diapers 72",
 "age_group": "new baby",
 "pack_size": "72",
 "category": "Diapers"
}

And Walmart example:

{
 "platform": "Walmart",
 "name": "LEGO City Police Car Toy for 5+ Years",
 "price": "$9.97",
 "mrp": "",
 "url": "https://www.walmart.com/...",
 "image_url": "https://i5.walmartimages.com/...jpg",
 "clean_title": "lego city police car toy 5 years",
 "age_group": "5 years",
 "pack_size": null,
 "category": "Toys"
}

What Are the Limitations of This Kids’ Scraper?

1. FirstCry layout changes frequently

XPath updates may break code.

2. Walmart uses anti-bot systems

High-speed scraping will get blocked.

3. Age extraction is non-standard

Brands write age groups in different formats.

4. Product variants require deeper parsing

Color/size/shade variations require clicking deeper.

5. Toys have complex naming

NLP-based categorization works better than rule-based.

When Should You Use Actowiz Solutions?

This tutorial is great for:

• small datasets
• demo projects
• college assignments
• proof-of-concepts

But when you need:

• 50,000+ Kids products
• Daily or hourly data refresh
• Multi-country kids’ product comparison
• Image extraction at scale
• AI classification for product types
• SKU linking across FirstCry / Walmart / Amazon / Target
• Market analytics dashboards
• Lead indicators for kids’ retail trends

Then Actowiz Solutions is the right partner.

We already support:

• Baby care analytics
• Toys & kids fashion data
• Cross-border pricing
• Stock monitoring
• Competitive intelligence
• Retail dashboards

Built for India, USA, Europe, UAE, KSA, and more.

Conclusion

Kids' product scraping is essential for:

• price comparison
• SKU mapping
• category intelligence
• retailer benchmarking
• trend forecasting
• brand performance tracking

With this tutorial, you now know how to:

• scrape FirstCry (India)
• scrape Walmart (USA)
• extract product metadata
• normalize titles
• detect age groups
• classify product types
• export datasets

For scalable, production-grade kids’ product intelligence, Actowiz Solutions provides end-to-end automated pipelines.

You can also reach us for all your mobile app scraping, data collection, web scraping, and instant data scraper service requirements!