apache/beam · error · WontImplementError

str.repeat(repeats=) repeats must be an int or a…

Error message

str.repeat(repeats=) repeats must be an int or a DeferredSeries. Lists are not supported because they make this operation sensitive to the order of the data.

What it means

DeferredStringMethods.str.repeat raises WontImplementError when `repeats` is a Python list. A per-element repeats list must line up positionally with the series rows, which depends on row order, so Beam only accepts an int (same repeat count for all) or a DeferredSeries (aligned by index).

Solutions

Use a scalar int if all rows repeat the same number of times: s.str.repeat(3).
Convert the counts into a deferred Beam Series (aligned on the same index) and pass that as `repeats`.
Compute repetition with an expression using .str * counts on Series pairs if a Series of counts exists.
Fall back to plain pandas for this step outside the Beam pipeline.

Example fix

// before
result = s.str.repeat([1, 2, 3])

// after
counts = make_deferred_series([1, 2, 3], index=s.index)  # aligned deferred series
result = s.str.repeat(counts)

Defensive patterns

Strategy: type-guard

Validate before calling

if isinstance(repeats, list):
    raise ValueError("str.repeat list counts unsupported in Beam; use int or DeferredSeries")

Type guard

def valid_repeat_arg(repeats):
    return isinstance(repeats, int) and not isinstance(repeats, bool) or isinstance(repeats, frame_base.DeferredFrame)

Try / catch

try:
    out = s.str.repeat(repeats)
except apachebeam.WontImplementError:
    out = s.str.repeat(int(np.mean(repeats)))  # or build a DeferredSeries

Prevention

Use a scalar int when the repeat count is uniform.
Materialize per-row counts as a deferred Series aligned on the same index.
Avoid list arguments in any Beam dataframe string API.

When it happens

Trigger: s.str.repeat([1, 2, 3]) or any list of per-row repeat counts on a deferred Beam Series; also note the parallel TypeError branch for any other non-int, non-Series value.

Common situations: Porting pandas str.repeat examples that use list counts; generating per-row padded strings with varying lengths; mixing plain lists with deferred pipelines.

Understand the failure class

Background: "Must be a positive integer", "Invalid value", "Unsupported": the invalid-argument-value error family, when a library rejects the value you pass — this error's family across 35 libraries.

Related errors

AI-assisted analysis of apache/beam@12126d8942 (2026-09-13). Data as JSON: /api/errors/a6f0295dbcbffd16. Report an issue: GitHub.

Appendix: source

Thrown at sdks/python/apache_beam/dataframe/frames.py:5084

              # pandas to compute this proxy. Currently it incorrectly infers
              # dtype bool, may require upstream fix.
              proxy=self._expr.proxy(),
              requires_partition_by=partitionings.Arbitrary(),
              preserves_partition_by=partitionings.Arbitrary()))
    elif isinstance(repeats, frame_base.DeferredBase):
      return frame_base.DeferredFrame.wrap(
          expressions.ComputedExpression(
              'repeat',
              lambda series, repeats_series: series.str.repeat(repeats_series),
              [self._expr, repeats._expr],
              # TODO(https://github.com/apache/beam/issues/20573): Defer to
              # pandas to compute this proxy. Currently it incorrectly infers
              # dtype bool, may require upstream fix.
              proxy=self._expr.proxy(),
              requires_partition_by=partitionings.Index(),
              preserves_partition_by=partitionings.Arbitrary()))
    elif isinstance(repeats, list):
      raise frame_base.WontImplementError(
          "str.repeat(repeats=) repeats must be an int or a DeferredSeries. "
          "Lists are not supported because they make this operation sensitive "
          "to the order of the data.", reason="order-sensitive")
    else:
      raise TypeError("str.repeat(repeats=) value must be an int or a "
                      f"DeferredSeries (encountered {type(repeats)}).")

  @frame_base.with_docs_from(pd.Series.str)
  @frame_base.args_to_kwargs(pd.Series.str)
  def get_dummies(self, **kwargs):
    """
    Series must be categorical dtype. Please cast to ``CategoricalDtype``
    to ensure correct categories.
    """
    dtype = self._expr.proxy().dtype
    if not isinstance(dtype, pd.CategoricalDtype):
      raise frame_base.WontImplementError(
          "get_dummies() of non-categorical type is not supported because "

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