Real-world Examples of Data Cleaning with Pandas #ai #artificialintelligence #machinelearning

@genaiexp To solidify our understanding, let's explore some real-world examples of data cleaning using Pandas. Imagine you're working with a dataset of customer orders, but you notice inconsistencies in date formats, missing customer IDs, and duplicate entries for the same order. First, you'd identify and standardize date formats using to_datetime(), ensuring all dates are uniformly processed. Next, handle missing customer IDs by filling them with placeholder values or using imputation techniques if suitable. Duplicate orders can be identified and removed using duplicated() and drop_duplicates(), ensuring the dataset accurately reflects distinct transactions. By applying the techniques we've discussed, you can transform a messy, unreliable dataset into a structured, actionable resource ready for analysis. These case studies not only reinforce the concepts learned but also demonstrate how these skills are applied in real-world scenarios.