3 Pandas Tricks for Data Cleaning & Preparation

Data cleaning and preparation are estimated to occupy up to 80% of a data scientist's daily workflow. Because Pandas is the standard data manipulation library in Python, the efficiency of your operations directly dictates how quickly you can move from raw, dirty datasets to model-ready features. And there is good reason to want to increase your cleaning and preparation time: it translates directly to more time available to spend on modeling, analysis, and communicating insights. However, many developers write Pandas code that mimics standard Python looping structures or uses imperative, state-mutating updates. These approaches suffer from several issues: they can trigger the confusing SettingWithCopyWarning, bloat RAM usage with redundant copies, and drag execution…