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

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

NumPy primarily uses homogeneous data types, meaning all elements in an array must be of the same type. In contrast, Pandas allows for heterogeneous data types, enabling different types in a single DataFrame.

Can you give examples of data types in both libraries?

Sure! In NumPy, you might use np.int32 or np.float64. In Pandas, you can have int, float, object, and even datetime64 types.

Which library is better for handling large datasets?

Pandas is generally better for handling large datasets due to its optimized data structures and built-in functions for data manipulation. However, NumPy can be faster for numerical computations.

Are there any specific scenarios where one is preferred over the other?

Yes, if you're performing complex mathematical operations, NumPy is preferred. For data analysis and manipulation, Pandas is usually the better choice.