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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?
- Susan SarandonOriginal
- 2024-11-04 03:58:01683browse
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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