apache/beam · error · WontImplementError

fillna(limit={method!r}, axis={axis!r}) is not supported bec

Error message

fillna(limit={method!r}, axis={axis!r}) is not supported because it is order-sensitive. Only fillna(limit=None) is supported with axis={axis!r}.

What it means

Same order-sensitivity restriction as fillna(method=...), but for the limit parameter: limit caps how many consecutive NaNs ffill/bfill will fill, which requires knowing row order. Beam raises WontImplementError whenever limit is not None with axis=0/'index'. Note the message contains a small bug — it interpolates method!r where limit is meant.

Source

Thrown at sdks/python/apache_beam/dataframe/frames.py:284

  @frame_base.with_docs_from(pd.DataFrame)
  @frame_base.args_to_kwargs(pd.DataFrame)
  @frame_base.populate_defaults(pd.DataFrame)
  @frame_base.maybe_inplace
  def fillna(self, value, method, axis, limit, **kwargs):
    """When ``axis="index"``, both ``method`` and ``limit`` must be ``None``.
    otherwise this operation is order-sensitive."""
    # Default value is None, but is overriden with index.
    axis = axis or 'index'

    if axis in (0, 'index'):
      if method is not None:
        raise frame_base.WontImplementError(
            f"fillna(method={method!r}, axis={axis!r}) is not supported "
            "because it is order-sensitive. Only fillna(method=None) is "
            f"supported with axis={axis!r}.",
            reason="order-sensitive")
      if limit is not None:
        raise frame_base.WontImplementError(
            f"fillna(limit={method!r}, axis={axis!r}) is not supported because "
            "it is order-sensitive. Only fillna(limit=None) is supported with "
            f"axis={axis!r}.",
            reason="order-sensitive")

    if isinstance(self, DeferredDataFrame) and isinstance(value,
                                                          DeferredSeries):
      # If self is a DataFrame and value is a Series we want to broadcast value
      # to all partitions of self.
      # This is OK, as its index must be the same size as the columns set of
      # self, so cannot be too large.
      class AsScalar(object):
        def __init__(self, value):
          self.value = value

      with expressions.allow_non_parallel_operations():
        value_expr = expressions.ComputedExpression(
            'as_scalar', lambda df: AsScalar(df), [value._expr],

View on GitHub (pinned to 12126d8942)

Solutions

1. Remove the limit parameter and fill all NaNs with a fixed value (limit=None).
2. Do the limited fill in pandas after to_pandas() collection.
3. Compute the fill explicitly (e.g. per-key aggregates) to stay order-independent.
4. If limit semantics are essential, keep that stage outside the Beam DataFrame API.

Example fix

// before
df = df.fillna(value=0, limit=1)
// after
df = df.fillna(value=0)

if kwargs.get('limit') is not None:
    raise ValueError('fillna(limit=...) is unsupported in Beam; drop limit')

def fillna_is_deferrable(kwargs) -> bool:
    return kwargs.get('method', None) is None and kwargs.get('limit', None) is None

try:
    df = df.fillna(value=0, limit=1)
except frame_base.WontImplementError:
    df = df.fillna(value=0)

When it happens

Trigger: df.fillna(value=x, limit=5) or any fillna call with a non-None limit and axis=0/'index' (the default) on a deferred frame.

Common situations: Porting pandas code that partially fills runs of NaNs (limit=1, limit=2) to Beam; time-series cleanup pipelines written against pandas semantics.

Understand the failure class

Background: UnsupportedOperationException and "is not supported" errors: when a library deliberately refuses a call — this error's family across 30 libraries.

Related errors

• fillna(method={method!r}, axis={axis!r}) is not supported be
• Grouping by a concrete ndarray is order sensitive.
• replace(method={method!r}) is not supported because it is or
• sort_values(axis=index) is not supported because it imposes
• align(method={method!r}) is not supported because it is orde

AI-assisted analysis of apache/beam@12126d8942 (2026-09-13). Data as JSON: /api/errors/fbe2d31ed0b3ba72. Report an issue: GitHub.