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# What’s new in 2.0.3 (June 28, 2023)

These are the changes in pandas 2.0.3. See Release notes for a full changelog including other versions of pandas.

## Fixed regressions

- Bug in `Timestamp.weekday`()` was returning incorrect results before `'0000-02-29'` (GH 53738)

- Fixed performance regression in merging on datetime-like columns (GH 53231)

- Fixed regression when `DataFrame.to_string()` creates extra space for string dtypes (GH 52690)

## Bug fixes

- Bug in `DataFrame.convert_dtype()` and `Series.convert_dtype()` when trying to convert `ArrowDtype` with `dtype_backend="nullable_numpy"` (GH 53648)

- Bug in `RangeIndex.union()` when using `sort=True` with another `RangeIndex` (GH 53490)

- Bug in `Series.reindex()` when expanding a non-nanosecond datetime or timedelta `Series` would not fill with `NaT` correctly (GH 53497)

- Bug in `read_csv()` when defining `dtype` with `bool[pyarrow]` for the `"c"` and `"python"` engines (GH 53390)

- Bug in `Series.str.split()` and `Series.str.rsplit()` with `expand=True` for `ArrowDtype` with `pyarrow.string` (GH 53532)

- Bug in indexing methods (e.g. `DataFrame.__getitem__()`) where taking the entire `DataFrame`/`Series` would raise an `OverflowError` when Copy on Write was enabled and the length of the array was over the maximum size a 32-bit integer can hold (GH 53616)

- Bug when constructing a `DataFrame` with columns of an `ArrowDtype` with a `pyarrow.dictionary` type that reindexes the data (GH 53617)

- Bug when indexing a `DataFrame` or `Series` with an `Index` with a timestamp `ArrowDtype` would raise an `AttributeError` (GH 53644)

## Other

## Contributors

For contributors, please see /usr/share/doc/contributors_list.txt or https://github.com/pandas-dev/pandas/graphs/contributors