What are the differences between using Pandas and NumPy?

What are the differences between using Pandas and NumPy?

Pandas is primarily used for data manipulation and analysis, while NumPy is focused on numerical computations. Pandas provides data structures like DataFrames, which are great for handling tabular data, whereas NumPy offers powerful array objects for efficient numerical operations.

When should I use Pandas over NumPy?

You should use Pandas when you need to work with structured data, like tables or time series, where you require features like indexing, grouping, and merging. NumPy is better for performing mathematical operations on large datasets.

Can I use both libraries together?

Absolutely! In fact, they complement each other well. You can use NumPy for numerical operations and then use Pandas to manipulate and analyze the results in a more structured way.

What are some common use cases for each library?

Common use cases for Pandas include data cleaning, data transformation, and exploratory data analysis. For NumPy, typical use cases involve mathematical computations, linear algebra, and working with large arrays.