Introduction to Keyword Data Analysis
Keyword research is the backbone of effective SEO, helping you discover what your audience is searching for and how competitive those terms are. It enables you to target the right queries, attract relevant traffic, and outperform competitors. The main types of keyword data you will encounter include the keyword itself, its search volume (how often it is searched), and its difficulty (how hard it is to rank for). Python is a powerful tool for analyzing keyword datasets, allowing you to efficiently filter, sort, and extract insights from large collections of keyword data.
1234567891011import pandas as pd # Sample keyword data data = { "keyword": ["python seo", "seo tools", "keyword analysis", "best backlinks", "on-page optimization"], "search_volume": [1200, 5400, 800, 1500, 600], "difficulty": [35, 60, 40, 55, 30] } df = pd.DataFrame(data) print(df)
This DataFrame consists of three columns: keyword (the search phrase), search_volume (average monthly searches), and difficulty (a score representing how competitive the keyword is). Each row represents a different keyword entry. You can access a column for analysis by referencing it with its name, such as df['search_volume'] to work with the search volumes, or df['keyword'] to access all keywords.
123# Filter keywords with search volume above 1000 high_volume = df[df["search_volume"] > 1000] print(high_volume)
1. What is a common metric used in keyword research?
2. Which pandas method is used to filter rows based on a condition?
3. Fill in the blank: To access the 'keyword' column in a DataFrame df, use ____.
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How can I filter keywords by difficulty instead of search volume?
Can you show me how to sort the keywords by search volume or difficulty?
What other types of analysis can I perform on this keyword data?
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Keyword research is the backbone of effective SEO, helping you discover what your audience is searching for and how competitive those terms are. It enables you to target the right queries, attract relevant traffic, and outperform competitors. The main types of keyword data you will encounter include the keyword itself, its search volume (how often it is searched), and its difficulty (how hard it is to rank for). Python is a powerful tool for analyzing keyword datasets, allowing you to efficiently filter, sort, and extract insights from large collections of keyword data.
1234567891011import pandas as pd # Sample keyword data data = { "keyword": ["python seo", "seo tools", "keyword analysis", "best backlinks", "on-page optimization"], "search_volume": [1200, 5400, 800, 1500, 600], "difficulty": [35, 60, 40, 55, 30] } df = pd.DataFrame(data) print(df)
This DataFrame consists of three columns: keyword (the search phrase), search_volume (average monthly searches), and difficulty (a score representing how competitive the keyword is). Each row represents a different keyword entry. You can access a column for analysis by referencing it with its name, such as df['search_volume'] to work with the search volumes, or df['keyword'] to access all keywords.
123# Filter keywords with search volume above 1000 high_volume = df[df["search_volume"] > 1000] print(high_volume)
1. What is a common metric used in keyword research?
2. Which pandas method is used to filter rows based on a condition?
3. Fill in the blank: To access the 'keyword' column in a DataFrame df, use ____.
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