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3 Best Ways to Merge Pandas DataFrames | Infographic/data-science-insights/3-best-ways-to-merge-pandas-dataframes-infographic

3 Best Ways to Merge Pandas DataFrames | Infographic

September 25, 2024

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3 Best Ways to Merge Pandas DataFrames | Infographic

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.

Do you know Panda is a very important tool that has made a huge contribution in the growth of global data analytics market which is expected to reach $129.5 billion by 2027 (as predicted by market.us)?

In the following infographic, we briefly explain three important ways to merge dataframes namely:

  • Concatenation – used to stack dataframes one on top of other
  • Merge – merges the dataframes based on common columns
  • Join – joins dataframes based on index labels.

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.

3 Best Ways to Merge Pandas DataFrames | Infographic

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