Python Web Scraping and Data Analysis: A Simple Case Study

Python, with its extensive library support, has become a popular choice for web scraping and data analysis tasks. In this article, we will walk through a simple case study that demonstrates how to scrape data from a website and analyze it using Python. This example will focus on scraping product information from an online retail store and performing basic data analysis.

Step 1: Setting Up the Environment

First, ensure you have Python installed on your machine. Next, you’ll need to install a few libraries that will make the scraping and analysis processes easier. Open your terminal or command prompt and install the following libraries:

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pip install requests beautifulsoup4 pandas

• requests is used to fetch the web page content.
• beautifulsoup4 is used to parse the HTML content.
• pandas is used for data analysis.

Step 2: Scraping the Data

In this step, we will scrape product data from an example website. For demonstration purposes, let’s say we are scraping product names and prices.

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import requests
from bs4 import BeautifulSoup

# Target URL
url = 'https://example.com/products'

# Fetch the content
response = requests.get(url)
html_content = response.text

# Parse the HTML content
soup = BeautifulSoup(html_content, 'html.parser')

# Extract the data
products = []
for product in soup.find_all('div', class_='product'):
    name = product.find('h3').text.strip()
    price = product.find('span', class_='price').text.strip()
    products.append((name, price))

# Display the scraped data
for name, price in products:
    print(f'Name: {name}, Price: {price}')

Step 3: Analyzing the Data

Now that we have the data, let’s analyze it using pandas. We will create a DataFrame and perform some basic operations.

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import pandas as pd

# Convert the list of tuples into a DataFrame
df = pd.DataFrame(products, columns=['Name', 'Price'])

# Convert the price column to a numeric type for analysis
df['Price'] = df['Price'].str.replace('$', '').astype(float)

# Display the DataFrame
print(df)

# Basic analysis: finding the average price
average_price = df['Price'].mean()
print(f'Average Price: {average_price}')

Conclusion

This simple case study demonstrates how Python can be used for web scraping and basic data analysis. With a few lines of code, we were able to scrape product information from a website and perform simple data analysis, such as calculating the average price. Python’s simplicity and powerful libraries make it an excellent tool for such tasks.

[tags]
Python, Web Scraping, Data Analysis, BeautifulSoup, Pandas, Requests