Working with messy data sets is a common challenge.
Simon Rosner
10 replies
What are your favorite tools or techniques for cleaning and transforming data to make it analysis-ready?
Replies
Umaru@umaru_sanda
I use R with the dplyr package. It’s great for data manipulation and cleaning tasks.
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I used to do it on excel.
I often use Python with pandas. It’s great for handling large datasets and has many useful functions for cleaning data.
Did you use OpenRefine? It's perfect for cleaning messy data and transforming it.
I use Python tools like Pandas for cleaning data. They make it easy to fix problems and get the data ready for analysis.
OpenRefine is really helpful for cleaning data. It can remove duplicates and fix errors
I use SQL to clean data. It helps me filter and organize data quickly.
I use R with packages like dplyr for cleaning data. It helps me transform data into the right format for analysis.
Cleaning messy data is like untangling headphone wires - time-consuming but necessary! I rely on the dynamic duo of Python libraries like Pandas and Numpy to whip data into shape for smooth analysis. How do you tackle your data cleaning dilemmas?