Introduction to SEO Research with Python
Discover effective SEO Research with Python and optimization, no premium tools required! π°π€
Why SEO Research with Python?
SEO Research with Python is an incredibly versatile method and utilises a powerful programming language that offers numerous benefits for SEO professionals:
- Open-source and freely available
- Extensive ecosystem of libraries for data analysis, web scraping, and automation
- Easy to learn and write, even for beginners
- Integrates well with various APIs and data sources
Target Audience
This guide is tailored for TheITApprentice volunteers who have a basic understanding of Python and want to leverage it for SEO research and optimization. No prior SEO experience is necessary!
![SEO Research with Python](https://theitapprentice.com/wp-content/uploads/2024/06/DALLΒ·E-2024-06-10-00.35.29-Design-a-modern-logo-for-a-project-titled-SEO-Research-with-Python.-The-logo-should-incorporate-a-minimalist-design-focusing-on-black-and-white-col.webp)
Essential Python Tools for SEO
Python Setup
Before we dive into the SEO techniques, let’s set up our Python environment:
- Install Python from the official website: https://www.python.org/downloads/
- Create a virtual environment to keep your projects isolated:
python -m venv myenv
myenv\Scripts\activate
Key Libraries
We’ll be using the following Python libraries for our SEO tasks:
requests
: For making HTTP requests and retrieving web pagesBeautifulSoup
: For parsing and extracting data from HTML and XML documentspandas
: For data manipulation and analysispytrends
: For accessing Google Trends data- Google APIs: For integrating with various Google services like Analytics and Search Console
Install these libraries using pip
:
pip install requests beautifulsoup4 pandas pytrends google-api-python-client
Python-Powered SEO Techniques
1. Keyword Research Using Python
Discover trending keywords with pytrends
:
from pytrends.request import TrendReq
pytrends = TrendReq(hl='en-US', tz=360)
keywords = ['IT apprenticeship', 'tech apprenticeship', 'IT career', 'IT training', 'technology skills']
pytrends.build_payload(kw_list=keywords, timeframe='today 12-m')
interest_over_time = pytrends.interest_over_time()
print(interest_over_time)
2. Competitor Website Analysis
Scrape competitor websites with BeautifulSoup
and requests
:
import requests
from bs4 import BeautifulSoup
url = 'https://competitor.com'
response = requests.get(url)
soup = BeautifulSoup(response.text, 'html.parser')
# Extract meta tags
meta_tags = soup.find_all('meta')
for tag in meta_tags:
print(tag.get('name'), tag.get('content'))
# Extract headings
headings = soup.find_all(['h1', 'h2', 'h3'])
for heading in headings:
print(heading.name, heading.text.strip())
3. Content Optimization Analysis
Analyze text content using the nltk
library:
import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
nltk.download('punkt')
nltk.download('stopwords')
text = "Your content goes here..."
# Tokenize and remove stop words
tokens = word_tokenize(text)
stop_words = set(stopwords.words('english'))
filtered_tokens = [word for word in tokens if word.casefold() not in stop_words]
# Calculate keyword density
keyword_density = {}
for word in filtered_tokens:
if word not in keyword_density:
keyword_density[word] = 1
else:
keyword_density[word] += 1
total_words = len(filtered_tokens)
for word, count in keyword_density.items():
keyword_density[word] = (count / total_words) * 100
print(keyword_density)
4. SEO Audit with Python
Check site health with requests
:
import requests
urls = ['https://example.com', 'https://example.com/about', 'https://example.com/contact']
for url in urls:
response = requests.get(url)
status_code = response.status_code
if status_code == 200:
print(f"{url} is accessible (status code: {status_code})")
else:
print(f"{url} is not accessible (status code: {status_code})")
5. Backlink Analysis
Find backlinks with a custom web scraper:
import requests
from bs4 import BeautifulSoup
url = 'https://example.com'
response = requests.get(url)
soup = BeautifulSoup(response.text, 'html.parser')
backlinks = []
for link in soup.find_all('a'):
href = link.get('href')
if href and 'example.com' not in href:
backlinks.append(href)
print(backlinks)
6. Tracking SEO Performance
Fetch data from Google Analytics API:
from google.oauth2 import service_account
from googleapiclient.discovery import build
SERVICE_ACCOUNT_FILE = 'path/to/your/service_account.json'
VIEW_ID = 'your_view_id'
credentials = service_account.Credentials.from_service_account_file(SERVICE_ACCOUNT_FILE)
analytics = build('analyticsreporting', 'v4', credentials=credentials)
response = analytics.reports().batchGet(
body={
'reportRequests': [
{
'viewId': VIEW_ID,
'dateRanges': [{'startDate': '7daysAgo', 'endDate': 'today'}],
'metrics': [{'expression': 'ga:sessions'}],
'dimensions': [{'name': 'ga:pagePath'}]
}
]
}
).execute()
for report in response.get('reports', []):
for row in report.get('data', {}).get('rows', []):
print(row['dimensions'][0], row['metrics'][0]['values'][0])
Congradulations for learning the basics of SEO Research with Python
We hope you now enjoy effective SEO research and optimization, no premium tools required!
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