pandas-dev/pandas · error · ValueError

closed keyword does not match dtype.closed

Error message

closed keyword does not match dtype.closed

What it means

Raised in `_ensure_simple_new_inputs` when both an explicit `closed` keyword and an explicit `IntervalDtype` with a non-None `.closed` are passed, and they disagree (e.g. `closed='left'` with `dtype='interval[int64, right]'`). The closed attribute lives on the dtype, so pandas refuses a contradictory override.

Solutions

1. Make closed consistent: specify it in only one place — either the keyword or the dtype.
2. If the dtype came from an old pickle (closed is None), the keyword is used to fill it; do not pass a closed keyword when the dtype already carries one.
3. Construct the dtype with matching closed: `IntervalDtype('int64', 'left')` together with `closed='left'`.

Example fix

# before
IntervalArray.from_arrays(l, r, closed='left', dtype=IntervalDtype('int64', 'right'))

# after - choose one source of truth
IntervalArray.from_arrays(l, r, closed='left')
# or
IntervalArray.from_arrays(l, r, dtype=IntervalDtype('int64', 'left'))

from pandas import IntervalDtype

def reconcile_closed(dtype, closed):
    if isinstance(dtype, IntervalDtype) and dtype.closed is not None:
        if closed is not None and closed != dtype.closed:
            raise ValueError(f'closed={closed} conflicts with dtype.closed={dtype.closed}')
        return dtype.closed
    return closed

from pandas import IntervalDtype

def closed_is_consistent(dtype, closed) -> bool:
    if isinstance(dtype, IntervalDtype) and dtype.closed is not None and closed is not None:
        return dtype.closed == closed
    return True

try:
    arr = IntervalArray.from_arrays(l, r, closed=closed, dtype=dtype)
except ValueError as e:
    if 'closed keyword does not match' in str(e):
        arr = IntervalArray.from_arrays(l, r, dtype=dtype)  # let dtype win
    else:
        raise

When it happens

Trigger: Passing `IntervalArray.from_arrays(left, right, closed='left', dtype=IntervalDtype('int64', 'right'))`; loading an old pickle whose dtype carries closed='right' while constructing with closed='left'.

Common situations: Building intervals interactively and toggling closed; serialised dtype carrying closed state recombined with a different keyword; refactoring where closed was moved into the dtype string.

Related errors

• Cannot modify read-only array
• invalid dtype
• left and right must have the same length
• left and right must have the same time zone, got
• left side of interval must be <= right side

AI-assisted analysis of pandas-dev/pandas@3b7651241d (2026-08-11). Data as JSON: /api/errors/04c547944298f1e2. Report an issue: GitHub.

Appendix: source

Thrown at pandas/core/arrays/interval.py:310

        closed = closed or "right"

        if dtype is not None:
            # GH 19262: dtype must be an IntervalDtype to override inferred
            dtype = pandas_dtype(dtype)
            if isinstance(dtype, IntervalDtype):
                if dtype.subtype is not None:
                    left = left.astype(dtype.subtype)
                    right = right.astype(dtype.subtype)
            else:
                msg = f"dtype must be an IntervalDtype, got {dtype}"
                raise TypeError(msg)

            if dtype.closed is None:
                # possibly loading an old pickle
                dtype = IntervalDtype(dtype.subtype, closed)
            elif closed != dtype.closed:
                raise ValueError("closed keyword does not match dtype.closed")

        # coerce dtypes to match if needed
        if is_float_dtype(left.dtype) and is_integer_dtype(right.dtype):
            right = right.astype(left.dtype)
        elif is_float_dtype(right.dtype) and is_integer_dtype(left.dtype):
            left = left.astype(right.dtype)

        if type(left) != type(right):
            msg = (
                f"must not have differing left [{type(left).__name__}] and "
                f"right [{type(right).__name__}] types"
            )
            raise ValueError(msg)
        if (
            isinstance(left.dtype, CategoricalDtype)
            or is_string_dtype(left.dtype)
            or is_string_dtype(right.dtype)
        ):

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