The dropna() function allows removing rows or columns with missing values, while fillna() helps replace them with specific values like the mean, median, or a constant. Additionally, interpolate() can be useful for filling gaps in time series data. Choosing the right approach depends on the dataset and the impact of missing values on the analysis.

--- Ultima modifica di piojanaaa in data 2025-03-05 06:02:32 ---

When defining a function in Python using def, it's important to ensure that all parameters are properly passed and not lost due to incorrect argument handling. Common mistakes include missing required arguments, using incorrect default values, or modifying mutable default arguments inside the function.