How can I create a dense NumPy array with a specific data type and filled-in missing values from a sequence of variable-length lists?

Filling Missing Values in NumPy Array Conversion

When converting a Python sequence of variable-length lists to a NumPy array, the resulting array often assumes an object data type due to the varying lengths of the lists. Enforcing a specific data type, such as int32, can lead to errors.

To efficiently obtain a dense NumPy array with a specific data type and filled-in missing values, the itertools module can be employed. Specifically, the zip_longest function can be used to pad the lists with a placeholder value.

For example, given a sequence v:

To create a dense int32 array with zeros as the placeholder:

This code produces:

where the missing values in the original sequence are filled with zeros. Notably, for Python 2, the function to use is itertools.izip_longest.

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