NumPy vs. Pandas: How Can I Store NaN Values in an Integer Array?

Keeping Array Type as Integer with NaN Values: NumPy vs. Pandas

When working with data structures that contain both integer and NaN values, it is crucial to maintain the intended data type while handling missing information. NumPy and Pandas, popular data analysis libraries in Python, offer different approaches for this task.

In NumPy, it is not possible to directly store NaN values in an integer array. This limitation stems from the fact that NaN is a floating-point concept that aligns with the float data type. You mentioned that using masked arrays did not solve the issue, as it also resulted in the data type being converted to float.

Pandas, on the other hand, has historically lacked support for integer NA values, causing columns containing both integer and NaN values to be casted as float. However, this has changed with the introduction of an extension dtype, Int64 (capitalized), in version 0.24 of Pandas. To utilize this feature, you can specify the dtype as "Int64[NA]" when creating your DataFrame. Note that this extension dtype must be used instead of the default int64 (lower case).

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