Numpy arrays are the most basic way of storing data. Numpy arrays are very limited in what they can contain. A single n-dimensional array can contain data of a single data type. For many real-world cases, you need a library that can handle more than one data type in a single dataset. For example, you will find many datasets online where the label column is a string while the rest of the columns in the dataset contain floating point numbers.
The pandas library makes it easier to work with these kinds of datasets and is used by many developers and data scientists. It's a library that allows you to load datasets from disk stored in many different formats as DataFrames. For example, you can read DataFrames stored ...
