How do you merge datasets using Pandas?
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How Do You Merge Datasets Using Pandas?
In real-world data science, information often comes from multiple sources—sales reports, customer data, and social media insights, for example. To uncover meaningful patterns, these datasets must be combined. According to a 2023 Kaggle survey, over 72% of data professionals use Pandas as their primary data manipulation tool [Kaggle, 2023].
In Python, Pandas offers the merge() function, which works similarly to SQL joins. Common merge types include:
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Inner Join: Keeps only matching rows from both datasets.
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Left Join: Keeps all rows from the left dataset, matching from the right where possible.
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Right Join: The opposite of a left join.
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Outer Join: Keeps all rows from both datasets, filling missing values with NaN.
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Merging data effectively is a vital skill for any aspiring data scientist—because clean, connected data tells the most accurate story. So, are you ready to start merging your way to data mastery?
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