How do i handle missing data in a large dataset using Python? I’m working on a large dataset using Python and I noticed that some columns have missing values (NaN). I tried using df.dropna() and df.fillna() from pandas, but I’m not sure which method is best for different scenarios. I expected that filling missing values would improve my analysis, but I’m not confident if I did it correctly or if it affected the data negatively. What are the best practices for handling missing data, and when should I choose to drop or fill values Continue reading...
