Lernen Analyzing Keyword Difficulty and Opportunity | Keyword Research and Analysis with Python

Understanding keyword difficulty is crucial for SEO specialists aiming to identify high-opportunity keywords. Keyword difficulty measures how challenging it is to rank for a particular keyword, typically based on the strength and number of competing pages. However, a keyword's value is not determined by difficulty alone; search volume—the number of times a keyword is searched for within a given period—is equally important. By combining these two metrics, you can reveal keywords that strike a balance: those with substantial search volume but relatively low competition. These are the keywords most likely to offer the best return on your optimization efforts.


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import pandas as pd

# Sample keyword data
data = {
    "keyword": ["python tutorial", "seo tools", "best laptops", "keyword research", "learn python"],
    "search_volume": [50000, 12000, 80000, 9000, 40000],
    "difficulty": [75, 60, 85, 40, 55]
}

df = pd.DataFrame(data)

# Calculate 'opportunity_score': higher volume and lower difficulty is better
df["opportunity_score"] = df["search_volume"] / (df["difficulty"] + 1)

print(df[["keyword", "search_volume", "difficulty", "opportunity_score"]])

The formula used for calculating the opportunity score divides the search_volume by the difficulty plus one. Adding one to the difficulty prevents division by zero and slightly reduces the score for the easiest keywords, making the ranking more stable. This score rewards keywords that have high search volume and low difficulty. Once the score is calculated for each keyword, you can sort your DataFrame by this value to quickly identify which keywords present the best opportunity for targeting.


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# Sort keywords by opportunity_score in descending order
top_keywords = df.sort_values(by="opportunity_score", ascending=False)

print(top_keywords[["keyword", "search_volume", "difficulty", "opportunity_score"]].head())

1. Why is it important to consider both search volume and difficulty?

2. What pandas method sorts a DataFrame by a column?

3. Fill in the blank: To create a new column in a DataFrame, use ____.

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Suggested prompts:

Can you explain how to interpret the opportunity score values?

What other factors should I consider besides search volume and difficulty?

How can I apply this method to my own keyword list?

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