How to Efficiently Convert Variable-Length Python Sequences to Dense NumPy Arrays?

Efficiently Converting Variable-Length Python Sequences to Dense NumPy Arrays

Converting Python sequences into NumPy arrays is straightforward. However, when dealing with variable-length lists, the implicit conversion results in arrays of type object, which may not be optimal. Moreover, enforcing a specific data type can lead to exceptions.

One efficient solution to this problem is to use the itertools.zip_longest function. By utilizing zip_longest, one can easily create a sequence of tuples with missing values filled using a placeholder value. By transposing the resulting list, a dense NumPy array of the desired data type can be obtained.

For example, consider the sequence v = [[1], [1, 2]].

<code class="python">import itertools
np.array(list(itertools.zip_longest(*v, fillvalue=0))).T
Out:
array([[1, 0],
       [1, 2]])</code>

Here, the fillvalue of 0 is used to fill the missing values in the shorter list.

For Python 2 compatibility, use itertools.izip_longest instead. This approach is efficient and provides a simple way to convert variable-length Python sequences into dense NumPy arrays, ensuring type safety and optimal performance.

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