pandas-dev/pandas · error · TypeError

values must be a 1D list-like

Error message

values must be a 1D list-like

What it means

Raised by pandas.core.arrays.numeric._coerce_to_data_and_mask when the input values, after being coerced to a NumPy array, have more than one dimension (values.ndim != 1). IntegerArray/FloatingArray are 1-D extension arrays backed by a single data ndarray plus a boolean mask, so multi-dimensional data is rejected at construction. The check runs after dtype coercion, so even a valid numeric 2-D array (e.g. a matrix) triggers it.

Solutions

  1. Flatten the input before construction, e.g. np.ravel(values) or values.reshape(-1).
  2. Operate column-by-column on a DataFrame (each Series is 1-D) instead of passing the whole frame to a masked array.
  3. Use pd.DataFrame for genuinely 2-D data rather than a 1-D extension array.

Example fix

# before
pd.array([[1, 2], [3, 4]], dtype='Int64')  # raises

# after
pd.array(np.array([[1, 2], [3, 4]]).ravel(), dtype='Int64')
Defensive patterns

Strategy: validation

Validate before calling

import numpy as np

def to_masked_numeric(values, dtype):
    arr = np.asarray(values)
    if arr.ndim != 1:
        raise ValueError(f'expected 1-D input, got ndim={arr.ndim}')
    return pd.array(arr, dtype=dtype)

Type guard

def is_1d_arraylike(obj) -> bool:
    import numpy as np
    return hasattr(obj, 'ndim') and np.asarray(obj).ndim == 1

Try / catch

try:
    pd.array(values, dtype='Int64')
except TypeError as e:
    if '1D list-like' in str(e):
        values = np.asarray(values).ravel()
        pd.array(values, dtype='Int64')
    else:
        raise

Prevention

When it happens

Trigger: Calling pd.array([[1, 2], [3, 4]], dtype='Int64'), constructing IntegerArray/FloatingArray from a 2-D numpy array, or passing a DataFrame column block (2-D) to a masked-numeric constructor. Also reachable via astype('Int64') on a DataFrame (per-column is fine, but internal 2-D blocks route through here).

Common situations: Passing a matrix/list-of-lists where a flat vector was intended; reshaping data upstream and forgetting to flatten; converting a wide DataFrame slice instead of a single Series.

Related errors

AI-assisted analysis of pandas-dev/pandas@3b7651241d (2026-08-11). Data as JSON: /api/errors/11fba0d7f7c57064. Report an issue: GitHub.

Appendix: source

Thrown at pandas/core/arrays/numeric.py:188

        if inferred_type == "boolean" and dtype is None:
            # object dtype array of bools
            name = dtype_cls.__name__.strip("_")
            raise TypeError(f"{values.dtype} cannot be converted to {name}")

    elif values.dtype.kind == "b" and checker(dtype):
        # fastpath
        mask = np.zeros(len(values), dtype=np.bool_)
        if not copy:
            values = np.asarray(values, dtype=default_dtype)
        else:
            values = np.array(values, dtype=default_dtype, copy=copy)

    elif values.dtype.kind not in "iuf":
        name = dtype_cls.__name__.strip("_")
        raise TypeError(f"{values.dtype} cannot be converted to {name}")

    if values.ndim != 1:
        raise TypeError("values must be a 1D list-like")

    if mask is None:
        if values.dtype.kind in "iu":
            # fastpath
            mask = np.zeros(len(values), dtype=np.bool_)
        elif values.dtype.kind == "f":
            # np.isnan is faster than is_numeric_na() for floats
            # github issue: #60066
            if is_nan_na():
                mask = np.isnan(values)
            else:
                mask = np.zeros(len(values), dtype=np.bool_)
                if dtype_cls.__name__.strip("_").startswith(("I", "U")):
                    wrong = np.isnan(values)
                    if wrong.any():
                        raise ValueError("Cannot cast NaN value to Integer dtype.")
        elif is_nan_na():
            mask = libmissing.is_numeric_na(values)

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