Dropping null values and duplicates is important in data cleaning and preprocessing in Python because it ensures the integrity and accuracy of the data being analyzed. Null values can lead to errors and bias in the analysis, and duplicates can skew the results and make them unreliable.

Removing null values and duplicates can also improve the performance of the analysis and reduce the amount of storage space required. It is also important to understand that dropping null values and duplicates also depends on data analysis, sometimes, it is required to keep null values and duplicates in the dataset to get meaningful information.