numpy/numpy · error · ValueError

Can only create a chararray from string data.

Error message

Can only create a chararray from string data.

What it means

Raised in chararray.__array_finalize__ when the array's dtype kind is not one of V/S/U/b/c (void, bytes, unicode, bytes-native, character). chararray is a string-only subclass; numpy refuses to finalize it over numeric or object data. Guard at defchararray.py:592.

Source

Thrown at numpy/_core/defchararray.py:593

                                   offset=offset, strides=strides,
                                   order=order)
        if filler is not None:
            self[...] = filler

        return self

    def __array_wrap__(self, arr, context=None, return_scalar=False):
        # When calling a ufunc (and some other functions), we return a
        # chararray if the ufunc output is a string-like array,
        # or an ndarray otherwise
        if arr.dtype.char in "SUbc":
            return arr.view(type(self))
        return arr

    def __array_finalize__(self, obj):
        # The b is a special case because it is used for reconstructing.
        if self.dtype.char not in 'VSUbc':
            raise ValueError("Can only create a chararray from string data.")

    def __getitem__(self, obj):
        val = ndarray.__getitem__(self, obj)
        if isinstance(val, character):
            return val.rstrip()
        return val

    # IMPLEMENTATION NOTE: Most of the methods of this class are
    # direct delegations to the free functions in this module.
    # However, those that return an array of strings should instead
    # return a chararray, so some extra wrapping is required.

    def __eq__(self, other):
        """
        Return (self == other) element-wise.

        See Also
        --------

View on GitHub (pinned to e117b3ca4e)

Solutions

1. Convert the data to a string dtype first, then view as chararray: arr.astype('U').view(np.char.chararray).
2. Use np.char.asarray(arr) which handles dtype conversion explicitly instead of bare .view.
3. Drop the chararray subclass and operate on a plain ndarray of str_/bytes_ dtype, which is the modern recommendation.

Example fix

// before
np.array([1, 2, 3]).view(np.char.chararray)
// after
np.array([1, 2, 3]).astype('U').view(np.char.chararray)

import numpy as np

def to_chararray(arr):
    a = np.asarray(arr)
    if a.dtype.char not in 'VSUbc':
        a = a.astype('U')
    return a.view(np.char.chararray)

def is_string_dtype(arr) -> bool:
    return np.asarray(arr).dtype.char in 'VSUbc'

When it happens

Trigger: Viewing or slicing a non-string ndarray as a chararray (e.g. arr.view(np.char.chararray) on an int/float array), or constructing/reshaping that triggers __array_finalize__ with a non-string dtype.

Common situations: Migrating legacy np.char usage onto numeric arrays; using .astype after .view in the wrong order; copy/slicing operations that propagate a chararray type onto data of a different dtype.

Related errors

• Can only multiply by integers
• min_digits must be less than or equal to precision
• precision must be greater than 0 if fractional=False
• No input operands
• Character {s} is not a valid symbol.

AI-assisted analysis of numpy/numpy@e117b3ca4e (2026-08-07). Data as JSON: /api/errors/0f0a1feff77dee64. Report an issue: GitHub.