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

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

NumPy and Pandas both handle data types, but they do so in different ways. NumPy focuses on homogeneous data types, meaning all elements in an array must be of the same type, which allows for efficient computation. In contrast, Pandas allows for heterogeneous data types within a DataFrame, meaning each column can have a different data type.

Can you give examples of data types in both libraries?

Sure! In NumPy, common data types include int, float, and bool. In Pandas, you can have int, float, object (for strings), and datetime types, among others.

Which library is better for handling large datasets?

It depends on the use case. NumPy is generally faster for numerical computations on large datasets due to its homogeneous nature. However, if you need to handle mixed data types or perform complex data manipulations, Pandas is more suitable.

How do I convert data types in Pandas?

You can use the astype() method in Pandas to convert data types. For example, df['column_name'] = df['column_name'].astype('int') will convert the specified column to integers.