How to Convert a Year Range Column into Separate Columns in Python

Learn how to easily transform a year range column into two distinct columns in Python with a simple data manipulation technique.
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How to Convert a Year Range Column into Separate Columns in Python

When working with data in Python, particularly when analyzing datasets with year ranges, you may encounter scenarios where you need to split a single column into two separate ones for better readability and analysis. In this post, we will focus on how to take a Years Range column containing a string that specifies a start year and an end year, and convert it into two columns: Year Start and Year End.

The Problem

Consider a dataset structured as follows:

NameYears RangeAndy1985 - 1987Bruce2011 - 2018In this example, the Years Range column presents years as a string, and our goal is to extract these years into individual columns for ease of access and further analysis.

The Solution

Step 1: Setup Your Data

First, ensure that you have your data loaded into a pandas DataFrame. Here’s how you can begin:

[[See Video to Reveal this Text or Code Snippet]]

To split the column, you can use:

[[See Video to Reveal this Text or Code Snippet]]

Step 3: Review the Output

After executing the above line, your DataFrame will look like this:

NameYears RangeYear StartYear EndAndy1985 - 198719851987Bruce2011 - 201820112018Important Notes

Data Types: After splitting, the new columns will still be of type string. If you plan to perform numerical operations, consider converting them to int:

[[See Video to Reveal this Text or Code Snippet]]

Error Handling: Ensure your Years Range format is consistent across the dataset to avoid errors during the split.

Conclusion

Transforming a Year Range column into two distinct columns is a straightforward process with the use of the pandas library in Python. This method can make your data much easier to analyze and manipulate, especially when dealing with time-based data. Embrace these powerful data manipulation techniques to enhance your data analysis workflow!

Happy coding! If you have any other questions or need further assistance, feel free to ask.