Yeah, so in order to clean a data set with missing values and inconsistent formats using Parleton, I'll just use Pandas to identify, handle, and transform the data. Firstly, I'll identify the missing values using .isNull and .sum, then choose a method to fill them, for example, fillMA or fFill or bFill, or any more advanced techniques if I have. Next, I'll handle the inconsistent data types using toNumber or other conversion functions and address inconsistencies in data values using techniques like standardization and data validation. Finally, I'll remove all these duplicates with .duplicated and handle outliers with methods like standard deviation or box-maps.
