Error converting python DataFrame to Table

Hi @David, Thanks for writing in — you've actually already diagnosed this correctly, so let me just confirm it and get you moving. The table() conversion (available since R2024a) only handles one level of DataFrame nesting. Those 1×N object columns in your file that carry their own sub-fields push past that limit, and that's exactly what's throwing the dimension mismatch. Your flat numeric columns are completely fine — it's only the nested ones tripping it up. Before anything else, just run this to see what you're working with: fid = py.open("data.pkl", "rb"); data = py.pickle.load(fid); py.print(data.dtypes); py.print(data.head(int32(3))); For your flat columns, pull them out directly: flat_cols = {"col1", "col2", "col3"}; arrays = cellfun(@(c) double(data{c}.values), flat_cols,'UniformOutput', false); T = array2table(cell2mat(arrays), 'VariableNames', flat_cols); For the nested ones, you don't need a separate Python script. Call pandas.json_normalize inline from MATLAB — it flattens nested fields into dot-separated columns (e.g. sensor.value becomes a normal flat column), and after that table() will convert without issue: records = data.to_dict("records"); flat_data = py.pandas.json_normalize(records); T = table(flat_data); If that still gives you trouble, take a look at the PandasToMatlab utility on File Exchange (https://www.mathworks.com/matlabcentral/fileexchange/111770-pandastomatlab). The df2t() function there handles more edge cases than the built-in path and works entirely in memory. Full type-conversion details are in the docs here if you want to check what maps to what: https://www.mathworks.com/help/matlab/matlab_external/python-pandas-dataframes.html Hope this helps!