In Pandas, merging dataframes is a fundamental operation that refers to combining data from multiple sources depending upon their common columns. This is necessary and is an important step for data analysis and manipulation, especially when data scientists and data engineers are working with datasets having overlapping information.
The real-world data is often messy, incomplete, and inconsistent and Pandas is a powerful Python library and effective data science tool that is used to merge these dataframes. There are 3 different ways to do that and knowing these techniques can significantly boost your data science career.
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In the following infographic, we briefly explain three important ways to merge dataframes namely:
Check out the infographic below and understand their purpose, syntax, and examples.
Pandas are a popular data science tool and merging dataframes is an important function. Top data science certification programs can help students and professionals master this technique to boost their data science career.
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