Python For Data Science: The Difference Between Merge, Join, And Concat - Issue 124
Ways to join and merge datasets in Python Pandas. How to know which method to pick for which use case.
Throughout the year, I covered most steps of data work in Python including its packages, data ingestion, cleaning, EDA, graphs, and more. One important piece of workflow in data science and analytics that I haven’t touched yet is data processing. Specifically: merging multiple datasets in Python Pandas.
This step was somehow the hardest for me to figure out, and I had my share of mistakes made using the wrong approach that delayed my analysis (or even worse, brought me to the wrong output leading to getting the wrong data).
This step is the most tricky one because it’s done early in the process and sets the baseline for your analysis. What this means is if you initially merged the datasets wrong, every next step will get you further from the truth. In this issue, I’ll walk you through the methods of merging multiple datasets into one and describe the difference between MERGE(), JOIN(), and CONCAT() in Python, and give you pointers to follow to figure out which approach you should use for which case.
Python Pandas is particularly great for any use cases in data analysis. I can see how it can be daunting to search for documentation to figure out what is the right or best way to perform a particular task, especially when you don’t know what you’re searching for. While I encourage you to start with reading documentation, I also hope to point you to the right method and save you some time.
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