Stop Writing Messy Boolean Masks: 10 Elegant Ways to Filter Pandas DataFrames

Filtering data in Pandas DataFrames can be achieved through various techniques, moving from simple boolean masks to more complex logic. Multiple conditions can be combined using logical operators like `&` (AND) and `|` (OR) to create specific data subsets. More elegant and efficient methods are also available, including `.isin()` for checking against a list of values and `.query()` for a more readable, SQL-like syntax. The guide also covers string matching, date-based filtering, and handling missing values to provide a comprehensive toolkit for data selection.