Grouping and Summarizing Keyword Data
Grouping keywords by topic or intent is a powerful technique for organizing your SEO strategy. When you group keywords, you can better understand which topics are driving the most traffic and where to focus your optimization efforts. This approach not only reveals gaps in your content but also helps prioritize actions based on search volume or user intent.
1234567891011121314151617181920212223242526272829303132333435363738import pandas as pd # Sample keyword data data = { "keyword": [ "best running shoes", "marathon training plan", "trail running tips", "running shoes for flat feet", "marathon nutrition", "trail running gear" ], "category": [ "Shoes", "Marathon", "Trail", "Shoes", "Marathon", "Trail" ], "search_volume": [ 12000, 5000, 3000, 8000, 4000, 2500 ] } df = pd.DataFrame(data) # Group by category grouped = df.groupby("category") for name, group in grouped: print(f"Category: {name}") print(group) print()
The groupby method in pandas allows you to split your data into groups based on the values in one or more columns. After grouping, you can apply aggregation functions to summarize data within each group. For keyword data, this is especially useful for calculating metrics like the total or average search volume per category, helping you quickly identify which topics are most valuable.
123# Calculate total and average search volume per category summary = df.groupby("category")["search_volume"].agg(["sum", "mean"]) print(summary)
1. What pandas method is used to group data by a column?
2. Why is grouping keywords by category useful?
3. Fill in the blank: To calculate the mean of a group, use _ _ _ .
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Grouping keywords by topic or intent is a powerful technique for organizing your SEO strategy. When you group keywords, you can better understand which topics are driving the most traffic and where to focus your optimization efforts. This approach not only reveals gaps in your content but also helps prioritize actions based on search volume or user intent.
1234567891011121314151617181920212223242526272829303132333435363738import pandas as pd # Sample keyword data data = { "keyword": [ "best running shoes", "marathon training plan", "trail running tips", "running shoes for flat feet", "marathon nutrition", "trail running gear" ], "category": [ "Shoes", "Marathon", "Trail", "Shoes", "Marathon", "Trail" ], "search_volume": [ 12000, 5000, 3000, 8000, 4000, 2500 ] } df = pd.DataFrame(data) # Group by category grouped = df.groupby("category") for name, group in grouped: print(f"Category: {name}") print(group) print()
The groupby method in pandas allows you to split your data into groups based on the values in one or more columns. After grouping, you can apply aggregation functions to summarize data within each group. For keyword data, this is especially useful for calculating metrics like the total or average search volume per category, helping you quickly identify which topics are most valuable.
123# Calculate total and average search volume per category summary = df.groupby("category")["search_volume"].agg(["sum", "mean"]) print(summary)
1. What pandas method is used to group data by a column?
2. Why is grouping keywords by category useful?
3. Fill in the blank: To calculate the mean of a group, use _ _ _ .
¡Gracias por tus comentarios!
