keras-team/keras · error · TypeError

If class_mode=" ", y_col must be a list. Received .

Error message

If class_mode="{}", y_col must be a list. Received {}.

What it means

Raised by `DataFrameIterator` when `class_mode` requires labels but `y_col` is not a list. When using categorical/binary/etc. class modes with a pandas DataFrame, `y_col` must be a list of column names. Wrap the column name(s) in a list.

Solutions

1. Pass y_col as a list: y_col=['price','category']
2. Ensure each named column exists in the DataFrame

Example fix

// before
y_col='price'
// after
y_col=['price', 'category']

if class_mode == 'multi_output': assert isinstance(y_col, list)

def is_list(v): return isinstance(v, list)

try: flow_from_dataframe(..., y_col=y_col)
except TypeError as e: if 'must be a list' in str(e): y_col = [y_col]

When it happens

Trigger: flow_from_dataframe(..., class_mode='multi_output', y_col='price').

Common situations: Migrating from single-output code and forgetting to wrap y_col in a list.

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

• All values in column x_col=
• Column weight_col= must be numeric.
• Expect string, list or tuple but found
• If class_mode=" ", y_col=" " column values must be strings.
• If class_mode=" ", y_col=" " column values must be type…

AI-assisted analysis of keras-team/keras@7a34a03db6 (2026-08-25). Data as JSON: /api/errors/86d485fbb036eecb. Report an issue: GitHub.

Appendix: source

Thrown at keras/src/legacy/preprocessing/image.py:805

                f"belonging to {num_classes} classes."
            )
        self._filepaths = [
            os.path.join(self.directory, fname) for fname in self.filenames
        ]
        super().__init__(self.samples, batch_size, shuffle, seed)

    def _check_params(self, df, x_col, y_col, weight_col, classes):
        # check class mode is one of the currently supported
        if self.class_mode not in self.allowed_class_modes:
            raise ValueError(
                "Invalid class_mode: {}; expected one of: {}".format(
                    self.class_mode, self.allowed_class_modes
                )
            )
        # check that y_col has several column names if class_mode is
        # multi_output
        if (self.class_mode == "multi_output") and not isinstance(y_col, list):
            raise TypeError(
                'If class_mode="{}", y_col must be a list. Received {}.'.format(
                    self.class_mode, type(y_col).__name__
                )
            )
        # check that filenames/filepaths column values are all strings
        if not all(df[x_col].apply(lambda x: isinstance(x, str))):
            raise TypeError(
                f"All values in column x_col={x_col} must be strings."
            )
        # check labels are string if class_mode is binary or sparse
        if self.class_mode in {"binary", "sparse"}:
            if not all(df[y_col].apply(lambda x: isinstance(x, str))):
                raise TypeError(
                    'If class_mode="{}", y_col="{}" column '
                    "values must be strings.".format(self.class_mode, y_col)
                )
        # check that if binary there are only 2 different classes
        if self.class_mode == "binary":

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