The “scientific Python stack” is not a single product but a layered collection of independent open-source libraries that agree on a common foundation and so compose into a coherent environment. At the base sits NumPy and its ndarray; above it SciPy adds scientific algorithms, pandas adds labeled tabular data, Matplotlib adds plotting, scikit-learn adds machine learning, and Jupyter (with IPython) provides the interactive notebook in which the others are used. Each is maintained by a different community, yet they fit together because they all speak the same array language.
The keystone is interoperability through NumPy. The NumPy documentation notes that its array is “widely used in the [scientific] Python community,” and the 2020 Nature review “Array programming with NumPy” (Harris, Millman, van der Walt et al., listed as the project’s official citation) makes the architectural point explicit: NumPy’s array became the shared substrate that downstream libraries build on and exchange data through. Because a pandas column, a SciPy routine’s input, and a scikit-learn feature matrix are all (or convert cleanly to) NumPy arrays, data flows from one library to the next without copying or glue code.
This is a deliberately different model from a monolithic environment like MATLAB, where plotting, numerics, statistics, and the interactive shell ship as one vendor-controlled product. The Python stack reaches comparable breadth by federation: many small, focused, BSD-licensed libraries that each do one thing and rely on the others for the rest. The cost is coordination across projects; the benefit is that any piece can be replaced or extended without permission from a central owner.
The displacement of MATLAB was driven by that openness as much as by features. The stack was free, scriptable in a general-purpose language, embeddable in real software systems, and reproducible without per-seat licensing, which mattered enormously as data analysis moved out of the lab and into industry. By making vectorized numerics, dataframes, plotting, and notebooks all available in one free language, the stack let a single skill set carry an analyst from exploration to production.
By the time deep learning arrived, the stack was already the default scientific environment, so frameworks such as the major tensor libraries adopted NumPy-style array semantics and slotted in alongside pandas and Jupyter rather than replacing them. The result is that the same handful of interoperable libraries, plus a notebook, underpin most modern data-science and machine-learning work in Python.