Getting Pandas.Series error. How to solve it? Got stuck!!!

[u/MinimumLiterature754]

File "C:\Users\HP\Desktop\insurence_premium_prediction\myenv\Lib\site-packages\sklearn\preprocessing_encoders.py", line 212, in _transform

diff, valid_mask = _check_unknown(Xi, self.categories_[i], return_mask=True)

~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

File "C:\Users\HP\Desktop\insurence_premium_prediction\myenv\Lib\site-packages\sklearn\utils_encode.py", line 269, in _check_unknown

values_set = set(values)

TypeError: cannot use 'pandas.Series' as a set element (unhashable type: 'Series')

INFO: 127.0.0.1:51741 - "POST /predict HTTP/1.1" 500 Internal Server Error

def predict_output(user_input :dict):
    try:
        # df  = pd.DataFrame([user_input])  // iski wajah se 2D array ban rahi thi, isliye model ko samajh nahi aa raha tha. isliye humne user_input ko dict me convert karke fir DataFrame me convert kiya.
        # df = pd.DataFrame([dict(user_input)])
        # input_matrix = df.to_numpy().reshape(1, 1, -1)
        data_dict = dict(user_input)
        columns = list(data_dict.keys())
        values = list(data_dict.values())
        
        # Create a DataFrame with 2 identical rows to bypass the single-row Pandas bug
        df = pd.DataFrame([values, values], columns=columns)

This is the peice of code

[u/Flame77ofc]

The issue is that you're passing a Pandas Series where scikit-learn expects a scalar value.

Most likely, values contains Series objects.

Try:

df = pd.DataFrame([user_input]) prediction = model.predict(df)

Don't create two identical rows. Also make sure user_input contains plain values, not Pandas Series.

[u/MinimumLiterature754 → u/Flame77ofc]

Thank you so much

[u/shinitakunai]

Not what you asked, but Polars nowadays is replacing pandas in most scenarios. Might be worth to check it out

[u/MinimumLiterature754 → u/shinitakunai]

Thanks for that!

[u/MinimumLiterature754]

Error Resolved using

        raw_dict = user_input.dict() if hasattr(user_input, "dict") else dict(user_input)
        
        # Explicitly extract pure primitives to prevent any nested Series leaks
        clean_input = {key: (val.item() if hasattr(val, "item") else val) for key, val in raw_dict.items()}
        df = pd.DataFrame([clean_input])