keras-team/keras · error · TypeError

Expect string, list or tuple but found {} in {} column

Error message

Expect string, list or tuple but found {} in {} column 

What it means

When filtering classes (classes=... argument), each y_col value must be a string, list, or tuple so membership in the class set can be tested. Any other Python type raises TypeError.

Source

Thrown at keras/src/legacy/preprocessing/image.py:882

        for label in df[y_col]:
            if isinstance(label, (list, tuple)):
                labels.append([self.class_indices[lbl] for lbl in label])
            else:
                labels.append(self.class_indices[label])
        return labels

    @staticmethod
    def _filter_classes(df, y_col, classes):
        df = df.copy()

        def remove_classes(labels, classes):
            if isinstance(labels, (list, tuple)):
                labels = [cls for cls in labels if cls in classes]
                return labels or None
            elif isinstance(labels, str):
                return labels if labels in classes else None
            else:
                raise TypeError(
                    "Expect string, list or tuple "
                    "but found {} in {} column ".format(type(labels), y_col)
                )

        if classes:
            # prepare for membership lookup
            classes = list(collections.OrderedDict.fromkeys(classes).keys())
            df[y_col] = df[y_col].apply(lambda x: remove_classes(x, classes))
        else:
            classes = set()
            for v in df[y_col]:
                if isinstance(v, (list, tuple)):
                    classes.update(v)
                else:
                    classes.add(v)
            classes = sorted(classes)
        return df.dropna(subset=[y_col]), classes

View on GitHub (pinned to 7a34a03db6)

Solutions

1. Make all y_col values strings (or lists of strings)
2. Convert numerics: df[y_col] = df[y_col].astype(str)

Example fix

// before
classes=['0','1']; df['label'] numeric
// after
df['label'] = df['label'].astype(str)

assert df[y_col].map(lambda v: isinstance(v, (str, list, tuple))).all()

def label_ok(v): return isinstance(v, (str, list, tuple))

try: flow_from_dataframe(..., classes=classes)
except TypeError as e: if 'Expect string, list or tuple' in str(e): df[y_col] = df[y_col].astype(str)

When it happens

Trigger: flow_from_dataframe(..., classes=[...]) with numeric or NaN label values in y_col.

Common situations: Passing classes for the first time after previously running with numeric labels.

Understand the failure class

Background: Invalid argument type errors: "must be of type string", "expected X, got Y", and ERR_INVALID_ARG_TYPE explained — this error's family across 15 libraries.

Related errors

• All values in column x_col={x_col} must be strings.
• If class_mode="{}", y_col="{}" column values must be strings
• If class_mode="{}", y_col="{}" column values must be type st
• Received an invalid value for `units`, expected a positive i
• adapt() expects an iterable that yields arrays or tensors wi

AI-assisted analysis of keras-team/keras@7a34a03db6 (2026-08-25). Data as JSON: /api/errors/0d9174b983c77610. Report an issue: GitHub.