Foundational libraries
Like many open source libraries, pandas builds functionality on top of other foundational libraries, letting them manage lower-level details while pandas offers more user-friendly functionality. If you find yourself wanting to dive deeper into technical details beyond what you learn with pandas, these are the libraries you’ll want to focus on.
NumPy
NumPy labels itself as the fundamental package for scientific computing with Python, and it is the library on top of which pandas was originally built. NumPy is actually an n-dimensional library, so you are not limited to two-dimensional data like we get with a pd.DataFrame
(pandas actually used to offer 3-d and 4-d panel structures, but they are now long gone).
Throughout this book, we have shown you how to construct pandas objects from NumPy objects, as you can see in the following pd.DataFrame
constructor:
arr = np.arange(1, 10).reshape(3, -1)
df = pd.DataFrame(arr)
df
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