- updated _metadata_to_df function

[liesenf]

Bug description:
write_csv encounters TypeError when metadata is provided in dict format.

Solution:
edited write_csv function that features press_element" function that loops over each element and handles lists, dicts, and strings, or "return as is".
Closes #69

Error message:

TypeError Traceback (most recent call last)
in <cell line: 2>()
1 # Save the corpus as a .csv file locally
----> 2 Dutch_corpus.write_csv(path = "Dutch_corpus.csv")

8 frames
/usr/local/lib/python3.10/dist-packages/sktalk/corpus/write/writer.py in (x)
52 norm = pd.json_normalize(data=metadata, sep="_")
53 df = pd.DataFrame(norm)
---> 54 df[:] = np.vectorize(lambda x: ', '.join(
55 x) if isinstance(x, list) else x)(df)
56 return df

TypeError: sequence item 0: expected str instance, dict found

Key Changes:
Added process_element function: This function handles three cases:
List: Joins the elements with ', '.
Dictionary: Converts the dictionary to a JSON string using json.dumps. Alternatively, you could convert the dictionary to a custom string format, e.g., by joining key-value pairs with a colon.
Other types: Returns the value as-is.
Replaced lambda with process_element: The np.vectorize now applies this more robust function to each element in the DataFrame.
This approach should resolve the TypeError by correctly handling cases where elements in the DataFrame are dictionaries.

[sonarqubecloud]

Quality Gate passed

Issues
0 New issues
0 Accepted issues

Measures
0 Security Hotspots
81.8% Coverage on New Code
0.0% Duplication on New Code

See analysis details on SonarCloud

[mdingemanse]

looks like a simple enough change — but your branch is out of date with main. Want to merge/rebase?

/edit I have reviewed & approved, feel free to merge this PR

[bvreede]

Great quick fix here!

The addition of a playground/ folder with a copied notebook is less standard though — consider removing this...

[liesenf]

@bvreede Thanks for spotting - deleted

I was having trouble to remember the setup you introduced back in the days with a notebook for developing that loads the package from a branch.

Was that in the private repo scikit-talk_benchmarking?