When working with datasets, I often run into issues with missing values, outliers, and inconsistent formats. When planning these cleaning steps, what order should I follow for maximum efficiency? Which steps can be automated, and how do you identify areas that need manual review? Also, what general strategies do you recommend for different data types (numeric, categorical, date)? Guys, what methodology do you follow for this, and which libraries or simple techniques make your work easier? Would love to hear your thoughts.
How can we determine the most effective steps in the data cleaning process?
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Compared to manually scripting data cleaning in Pandas, tools like Trifacta’s visual profiling quickly highlight outliers and formatting errors, whereas Pandas gives you the flexibility to customize each step in code. So, I first use an auto-profiler like Trifacta to identify the priority errors, then fix those selected columns with my Python script in Pandas.