What are the differences between data types in Pandas and NumPy?

What are the differences between data types in Pandas and NumPy?

Pandas and NumPy both handle data types, but they have some key differences. NumPy uses a fixed set of data types, while Pandas offers more flexibility with its data types, including categorical and datetime types. Would you like to know more about specific data types?

Yes, can you explain the data types available in Pandas?

Sure! Pandas primarily uses the following data types: int64, float64, object (for strings), category, and datetime64. Each type serves different purposes, like handling numerical data, text, or dates.

How do these data types affect performance?

Data types can significantly impact performance. For instance, using 'category' for repeated string values can save memory and speed up operations. In contrast, using 'object' for large text data can be less efficient.

What about NumPy data types?

NumPy has a more limited set of data types, including int, float, complex, and bool. It focuses on numerical data and is optimized for performance in mathematical operations.