# SEO Content Analysis
``` python
from seo_rat.article import Article
# | export
from seo_rat.content_parser import extract_headers, remove_metadata, get_page_content
from seo_rat.gsc_client import get_date_range
```
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source
### calculate_keyword_density
``` python
def calculate_keyword_density(
content:str, # Page content
keyword:str, # Keyword to search for
)->dict:
```
*Calculate keyword density using word-level matching.*
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source
### find_cannibalized
``` python
def find_cannibalized(
session, # Active database session
website_id:int, # Website ID
site_url:str, # GSC property URL
start:str, # Start date (YYYY-MM-DD)
end:str, # End date (YYYY-MM-DD)
)->dict:
```
*Find keyword cannibalization via exact focus keyword matches and GSC
ranking data.*
## Future Update
- Add DSPY to filter htre nosie from the cannibalization
- Input: query string + website description/niche
- Output: is_relevant: bool + reason: str
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source
### check_h1_count
``` python
def check_h1_count(
headers:list, # Headers from [`extract_headers`](https://abdelkareemkobo.github.io/seo_rat/content_parser.html#extract_headers)
title:str | None=None, # Frontmatter title (for Quarto)
title_is_h1:bool=False, # Whether title counts as H1
)->dict:
```
*Check H1 count — for Quarto, the title frontmatter field acts as H1.*
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source
### keyword_in_first_section
``` python
def keyword_in_first_section(
content:str, # Page content
keyword:str, # Keyword to search for
percent:int=10, # Percentage of content to check
)->bool:
```
*Check if keyword appears in the first X% of content.*
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source
### check_paragraph_length
``` python
def check_paragraph_length(
content:str, # Page content
)->dict:
```
*Check average number of sentences per paragraph.*
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source
### keyword_in_metadata
``` python
def keyword_in_metadata(
metadata:dict, # Parsed frontmatter dict
keyword:str, # Keyword to search for
desc_field:str='description', # Frontmatter description field
)->dict:
```
*Check if keyword appears in title and description metadata fields.*
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source
### keyword_in_alt_texts
``` python
def keyword_in_alt_texts(
images:list, # Images from [`extract_images`](https://abdelkareemkobo.github.io/seo_rat/content_parser.html#extract_images)
keyword:str, # Keyword to search for
)->bool:
```
*Check if keyword appears in any image alt text.*
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source
### analyze_header_distribution
``` python
def analyze_header_distribution(
headers:list, # Headers from [`extract_headers`](https://abdelkareemkobo.github.io/seo_rat/content_parser.html#extract_headers)
)->dict:
```
*Analyze header type distribution as counts and percentages.*
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source
### check_keyword_placement
``` python
def check_keyword_placement(
keyword:str | None, # Focus keyword
metadata:dict, # Parsed frontmatter dict
headers:list, # Headers from [`extract_headers`](https://abdelkareemkobo.github.io/seo_rat/content_parser.html#extract_headers)
content:str, # Page content
url:str, # Page URL
desc_field:str='description', # Frontmatter description field
title_is_h1:bool=False, # Whether title counts as H1
)->dict:
```
*Check where the focus keyword appears across page elements.*
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source
### content_freshness
``` python
def content_freshness(
last_updated:str, # Last updated date (YYYY-MM-DD)
days:int=180, # Max days to consider content fresh
)->dict:
```
*Check content freshness based on last updated date.*