NumPy is a scientific computing library for Python. It offers high-level mathematical functions and a multi-dimensional structure (know as
ndarray) for manipulating large data sets.
While NumPy on its own offers limited functions for data analysis, many other libraries that are key to analysis—such as SciPy, matplotlib, and pandas are heavily dependent on NumPy. SciPy, for instance, offers advanced mathematical functions built on top of NumPy’s array data structure,
NumPy, along with the libraries mentioned above, is a part of the core SciPy stack—a group of tools for scientific computing in Python.
The NumPy array
NumPy’s array (or
ndarray) is a Python object used for storing data. The main advantage of NumPy over other Python data structures, such as Python’s
lists or pandas’
Series, is speed at scale. It’s most useful when you’re creating large matrices with billions of data points.
You don’t need a deep understanding of NumPy’s array for most analytical tasks—it’s more often used for programming—but there are times when it’s more efficient than other Python data structures. If you’re interested in diving deeper into this issue, check out these resources:
- Computing the eigenvalues of a matrix
- Manipulating linear matrices