pandas-dev/pandas · error · TypeError

passing a filterable condition to a non-table indexer [{self

Error message

passing a filterable condition to a non-table indexer [{self}]

What it means

Raised by FilterBinOp.evaluate in pandas.core.computation.pytables when the operator is not '==' or '!=' (i.e. an inequality like <, >, <=, >=, in, not in) AND self.is_in_table is False. is_in_table checks that queryables has a non-None entry for the column. The error is a TypeError and signals that you are trying an inequality filter on a column that is not stored as a data_column (only equality/list membership can sometimes be optimized via the index).

Source

Thrown at pandas/core/computation/pytables.py:347

                self.filter[2],
            )
        return self

    def format(self):
        """return the actual filter format"""
        return [self.filter]

    # error: Signature of "evaluate" incompatible with supertype "BinOp"
    def evaluate(self) -> Self | None:  # type: ignore[override]
        if not self.is_valid:
            raise ValueError(f"query term is not valid [{self}]")

        rhs = self.conform(self.rhs)
        values = list(rhs)

        if self.op not in ["==", "!="]:
            if not self.is_in_table:
                raise TypeError(
                    f"passing a filterable condition to a non-table indexer [{self}]"
                )
            return None

        if self.is_in_table and len(values) <= self._max_selectors:
            return None
        filter_op = self.generate_filter_op()
        self.filter = (self.lhs.value, filter_op, Index(values))
        return self

    def generate_filter_op(self, invert: bool = False):
        if (self.op == "!=" and not invert) or (self.op == "==" and invert):
            return lambda axis, vals: ~axis.isin(vals)
        else:
            return lambda axis, vals: axis.isin(vals)


class JointFilterBinOp(FilterBinOp):

View on GitHub (pinned to 71959b8cb9)

Solutions

1. Declare the column as a data_column when writing: store.put('df', df, format='table', data_columns=['price']).
2. Use equality/list membership if you cannot rewrite: where='price == 100' may still work for indexed columns, but inequalities require data_columns.
3. Filter in pandas after reading: df = store.get('df'); df[df['price'] > 100].
4. For range queries on the index, use the index column name directly (e.g. where='index > 100').

Example fix

# before
store.select('df', where='price > 100')  # TypeError: passing a filterable condition to a non-table indexer

# after
df.to_hdf(path, 'df', format='table', data_columns=['price'])
store.select('df', where='price > 100')
# or:
df = pd.read_hdf(path, 'df')
df[df['price'] > 100]

def supports_inequality(store, key, column) -> bool:
    storer = store.get_storer(key)
    data_cols = list(storer.data_columns or [])
    idx_cols = [getattr(a, 'name', None) for a in (storer.index_axes or [])]
    return column in data_cols or column in idx_cols

# before store.select('df', where=f'{col} > 5'):
if not supports_inequality(store, 'df', col):
    raise TypeError(f'{col!r} must be a data_column for inequality queries')

def is_data_column(store, key, column) -> bool:
    try:
        return column in (store.get_storer(key).data_columns or [])
    except Exception:
        return False

try:
    store.select('df', where=f'{col} > 5')
except TypeError as e:
    if 'filterable condition' in str(e):
        df = store.get('df')
        result = df[df[col] > 5]
    else:
        raise

When it happens

Trigger: store.select('df', where='price > 100') where 'price' is not a data_column; store.select('df', where='date < 20200101') against a non-indexed date column.

Common situations: Default table writes only make the index queryable for inequalities; users assume all columns support range queries; using inequality on a string/categorical column without data_columns=True.

Related errors

• name {self.name!r} is not defined
• arithmetic operations are not supported inside an HDFStore '
• Cannot compare {conv_val} of type {type(conv_val)} to {kind}
• query term is not valid [{self}]
• unable to collapse Joint Filters

AI-assisted analysis of pandas-dev/pandas@71959b8cb9 (2026-08-07). Data as JSON: /api/errors/88ed7c6e4a303932. Report an issue: GitHub.