numpy/numpy · error · TypeError

clip() missing 1 required positional argument: 'a_min'

Error message

clip() missing 1 required positional argument: 'a_min'

What it means

Raised by np.clip when a_max (and/or its alias max) is provided but a_min is missing (left at the np._NoValue sentinel). clip requires both bounds to be resolvable; when only one positional/keyword bound is given, numpy cannot determine a_min and raises this TypeError. Note: passing only min= (the array-API alias) without a_min/a_max is allowed and does not trigger this.

Source

Thrown at numpy/_core/fromnumeric.py:2473

    array([1, 1, 2, 3, 4, 5, 6, 7, 8, 8])
    >>> np.clip(a, 8, 1)
    array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1])
    >>> np.clip(a, 3, 6, out=a)
    array([3, 3, 3, 3, 4, 5, 6, 6, 6, 6])
    >>> a
    array([3, 3, 3, 3, 4, 5, 6, 6, 6, 6])
    >>> a = np.arange(10)
    >>> a
    array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
    >>> np.clip(a, [3, 4, 1, 1, 1, 4, 4, 4, 4, 4], 8)
    array([3, 4, 2, 3, 4, 5, 6, 7, 8, 8])

    """
    if a_min is np._NoValue and a_max is np._NoValue:
        a_min = None if min is np._NoValue else min
        a_max = None if max is np._NoValue else max
    elif a_min is np._NoValue:
        raise TypeError("clip() missing 1 required positional "
                        "argument: 'a_min'")
    elif a_max is np._NoValue:
        raise TypeError("clip() missing 1 required positional "
                        "argument: 'a_max'")
    elif min is not np._NoValue or max is not np._NoValue:
        raise ValueError("Passing `min` or `max` keyword argument when "
                         "`a_min` and `a_max` are provided is forbidden.")

    return _wrapfunc(a, 'clip', a_min, a_max, out=out, **kwargs)


def _sum_dispatcher(a, axis=None, dtype=None, out=None, keepdims=None,
                    initial=None, where=None):
    return (a, out)


# reduction= enables the C fast path for exact-ndarray reductions.
# _ReductionKind selects the appropriate argument signature to use.

View on GitHub (pinned to e117b3ca4e)

Solutions

1. Pass both bounds: np.clip(a, lo, hi).
2. If you only want a one-sided clip, pass None for the unused bound: np.clip(a, None, hi) or np.clip(a, lo, None).
3. Remember positional order is (a, a_min, a_max).

Example fix

// before
np.clip(a, 8)        # 8 becomes a_min; a_max missing
// after
np.clip(a, None, 8)  # one-sided upper clip

def safe_clip(a, lo=None, hi=None):
    # ensure both bounds are explicit (None allowed)
    import numpy as np
    if lo is None and hi is None:
        raise TypeError('clip needs at least one of a_min/a_max')
    return np.clip(a, lo, hi)

null

null

When it happens

Trigger: np.clip(a, a_max=8); np.clip(a, None is not passed) i.e. calling with a single bound; np.clip(a, max=8) alone is fine (aliases), but np.clip(a, 8) positional treats 8 as a_min and then a_max is missing.

Common situations: Calling clip with a single positional value intending it as the upper bound (it's actually a_min); forgetting the second bound; partial refactor mixing positional and keyword bounds.

Related errors

• clip() missing 1 required positional argument: 'a_max'
• Passing `min` or `max` keyword argument when `a_min` and `a_
• min_digits must be less than or equal to precision
• precision must be greater than 0 if fractional=False
• Can only multiply by integers

AI-assisted analysis of numpy/numpy@e117b3ca4e (2026-08-07). Data as JSON: /api/errors/709cd0ad52c89d24. Report an issue: GitHub.