Removing missing values from datasets is an important step in ensuring data analysis and modeling quality and accuracy. It helps to avoid issues with incomplete data, skewed results, poor model performance, and data integrity. But it is important to carefully consider the implications of removing missing values and to choose an appropriate method for handling them, such as imputation or removal, depending on the specific situation.

To remove missing values in Python, you can use the .dropna(), method of the pandas library. This function removes any rows or columns that contain missing values in a dataset.

Here's an example:

It's important to note that removing missing values can result in a loss of information, so it's important to consider the implications of removing them before doing so. In some cases, it may be appropriate to impute missing values instead of removing them.

Also, we want to remind you that replacing missing values with mean values can be used for handling missing data in Python. It is typically used when the missing data is missing at random (MAR), which means that the missing values are not related to the actual value of the missing data.