Preserve Complex Data Types for to_csv
This Pull Request solves the issue outlined in:
https://github.com/pandas-dev/pandas/issues/60895

Complex data types like numpy arrays can now be stored in csv format and later recovered. 
A new parameter, preserve_complex, is introduces, and when it is set to true in your to_csv function call, the complex data types will be preserved and can be recovered from the csv.

The way this works is by serializing Numpy arrays into JSON format for preserve_complex=True. To get them from the csv, we can set the same parameter in read_csv, and the original Numpy array will be returned.

Please refer to tests in scripts/tests/test_csv.py to see how this is used.

Please refer to the original issue for more information on the problem definition.


@Jaspvr is there an existing issue that this PR addresses? If so, could you list it in the description? If not, please create an issue describing the bug or proposed enhancement so it can be reviewed by a team member. > @Jaspvr is there an existing issue that this PR addresses? If so, could you list it in the description? If not, please create an issue describing the bug or proposed enhancement so it can be reviewed by a team member.


Hey, just updated the description. This is in relation to https://github.com/pandas-dev/pandas/issues/60895 This PR introduces a useful feature for preserving complex data types like NumPy arrays during CSV serialization/deserialization via the `preserve_complex` flag. The implementation integrates well with the existing I/O functions, and the new tests seem cover the basic functionality (I haven't run them).

Two minor points for consideration:

**Serialization Logic (`pandas/io/formats/csvs.py`):** The current logic checks only the *first* value in an object column to decide if it contains arrays/lists needing serialization. This might be fragile if the first row has NaN or a different type. Would checking the first *non-NaN* value or using `pd.api.types.infer_dtype` be more robust, balancing robustness vs. performance?
**Deserialization Logic (`pandas/io/parsers/readers.py`):** The `_restore_complex_arrays` function uses a heuristic (`startswith("[")` / `endswith("]")`) combined with checking `all()` non-null values. This seems safer than just checking brackets, but could still potentially misidentify columns if they contain a mix of valid JSON strings and other strings. Perhaps a `try-except json.JSONDecodeError` within the `apply` could be an alternative? (though likely slower if many non-JSON strings exist) Thanks for the pull request, but it appears to have gone stale. Additionally it appears that there needs more discussion whether we want to support this type in exporting CSV. So going to close for now