How to Preserve Integer Data Types in Pandas DataFrames with Missing Values?

ndarray vs DataFrame: Preserving Integer Type with NaNs

For operational scenarios where maintaining the integrity of integer-type columns in a DataFrame is paramount while accommodating missing values, an inherent challenge arises. NumPy arrays, the underlying data structure in Pandas DataFrames, impose restrictions on data types, particularly regarding the coexistence of integer elements and NaN values.

The NaN Dilemma

NumPy's inability to represent NaN within integer arrays stems from a design limitation. This poses a conundrum in scenarios where one wishes to retain the integer data type tout court.

Attempts and Inconsistencies

Efforts to circumvent this limitation have been pursued, such as leveraging the from_records() function with coerce_float=False and experimenting with NumPy masked arrays. However, these approaches consistently convert the column data type to float.

Current Solutions and Limitations

Until advancements are made in NumPy's handling of missing values, there remain limited options. One potential workaround involves replacing NaNs with a sentinel value, such as an arbitrarily chosen large integer that differs from valid data and can be used to identify missing entries during processing.

Alternatively, a workaround adopted in recent versions of pandas (0.24 onwards) is to utilize the Int64 extension dtype (capitalized "Int") instead of the default int64 (lower case). Int64 supports optional integer NA values, providing a workaround for this specific issue.

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