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10 Ways to Initialize a Numpy Array (How to create numpy arrays)

In Python, matrix-like data structures are most commonly represented with numpy arrays. The numpy Python package has been well-developed for efficient computation of matrices. The first step to using numpy arrays is to initialize, or create, an array. In Python, there are many ways to create a numpy array. In this article, I’ll demonstrate how to create numpy arrays in ten different ways.

Ten common ways to initialize (or create) numpy arrays are:

  1. From values (numpy.array([value, value, value]))
  2. From a Python list or tuple (numpy.asarray(list))
  3. Empty array (numpy.empty(shape))
  4. Array of ones (numpy.ones(shape))
  5. Array of zeros (numpy.zeros(shape))
  6. Array of any value (numpy.full(value))
  7. Copy an array (numpy.copy(array))
  8. Sequential or evenly spaced values (numpy.arange, numpy.linspace, numpy.geomspace)
  9. Array of random values (numpy.random)
  10. Array of repeaded values (numpy.repeat)

At first, numpy will have a steep learning curve, but stick with it. Learning numpy is a skill that will greatly improve your Python programming.

How to Create Numpy Arrays in 10 Different Ways


Final Thoughts

There are many different ways to create numpy arrays. This article has demonstrated what I think are the most common and useful ways to initialize numpy arrays. If one of these methods doesn’t meet your needs, consult the numpy documentation for more array creation options.

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