pandas-dev/pandas · error · TypeError

.from_tuples received an invalid item

Error message

{name}.from_tuples received an invalid item, {d}

What it means

Raised in the same from_tuples loop when unpacking `lhs, rhs = d` raises TypeError rather than ValueError — i.e. `d` is not iterable/unpackable at all (an int, None that is not NA-detected, a non-iterable object). NA values are handled before unpacking; anything else that is not a tuple of two triggers this.

Solutions

1. Filter or coerce non-tuple items: keep only tuples of length 2 (or recognised NA).
2. Replace bare scalars with explicit NA: `pd.NA` or `np.nan` so the NA branch handles them.
3. Build with `from_arrays` from two parallel lists derived from your data instead of from_tuples.

Example fix

# before
pd.arrays.IntervalArray.from_tuples([42, (0,1)])

# after - normalise entries first
clean = [x if isinstance(x, tuple) and len(x) == 2 else pd.NA for x in [42, (0,1)]]
pd.arrays.IntervalArray.from_tuples(clean)

import pandas as pd

def coerce_to_pairs(data):
    out = []
    for d in data:
        if isinstance(d, tuple) and len(d) == 2:
            out.append(d)
        elif pd.isna(d):
            out.append(pd.NA)
        else:
            out.append(pd.NA)
    return out

import pandas as pd
from collections.abc import Iterable

def all_unpackable_or_na(data) -> bool:
    for d in data:
        if pd.isna(d):
            continue
        if not (isinstance(d, tuple) and len(d) == 2):
            return False
    return True

try:
    arr = pd.arrays.IntervalArray.from_tuples(data)
except TypeError as e:
    if 'invalid item' in str(e):
        clean = [d if isinstance(d, tuple) and len(d) == 2 else pd.NA for d in data]
        arr = pd.arrays.IntervalArray.from_tuples(clean)
    else:
        raise

When it happens

Trigger: `IntervalArray.from_tuples([42])`; `from_tuples([None])` where None is not treated as NA by `isna`; `from_tuples([object()])`.

Common situations: Dirty data where some rows are scalars instead of pairs; user passes a list of mixed intervals and bare numbers.

Related errors

• category, object, and string subtypes are not supported for…
• (...) must be called with a collection of some kind, was…
• dtype must be an IntervalDtype, got
• ExtensionArray.fillna does not support filling with a dict…
• Left and right arrays must have matching signedness. Got

AI-assisted analysis of pandas-dev/pandas@3b7651241d (2026-08-11). Data as JSON: /api/errors/13b1d7ff26685cc2. Report an issue: GitHub.

Appendix: source

Thrown at pandas/core/arrays/interval.py:589

            left, right = [], []
        else:
            # ensure that empty data keeps input dtype
            left = right = data

        for d in data:
            if not isinstance(d, tuple) and isna(d):
                lhs = rhs = np.nan
            else:
                name = cls.__name__
                try:
                    # need list of length 2 tuples, e.g. [(0, 1), (1, 2), ...]
                    lhs, rhs = d
                except ValueError as err:
                    msg = f"{name}.from_tuples requires tuples of length 2, got {d}"
                    raise ValueError(msg) from err
                except TypeError as err:
                    msg = f"{name}.from_tuples received an invalid item, {d}"
                    raise TypeError(msg) from err
            left.append(lhs)
            right.append(rhs)

        return cls.from_arrays(left, right, closed, copy=False, dtype=dtype)

    @classmethod
    def _validate(cls, left, right, dtype: IntervalDtype) -> None:
        """
        Verify that the IntervalArray is valid.

        Checks that

        * dtype is correct
        * left and right match lengths
        * left and right have the same missing values
        * left is always below right
        """
        if not isinstance(dtype, IntervalDtype):

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