Via https://www.mongodb.com/community/forums/t/polars-and-pymongoarrow-with-embedded-documents/315534

 

I have an aggregation that produces data that contains embedded documents as:

 

{
  "_id":

{     "$oid": "65816a666771a1d4de33eb68"   }

,
  "diet_included": [
   

{       "label": "banana",       "_id": "62af112dbe66bc92f8ceabcb",       "type": "foodLabel",       "labelId": 46,       "optionCategoryIdsForConflicts": [         13200,         90020,         91020       ],       "foodLabel": "banana",       "categoriesNames": []     }

,
   

{       "label": "carrot",       "_id": "62af113cbe66bc92f8ceac20",       "type": "foodLabel",       "labelId": 131,       "optionCategoryIdsForConflicts": [         18170,         91010       ],       "foodLabel": "carrot",       "categoriesNames": []     }

,
   

{       "label": "eggplant",       "_id": "62af115cbe66bc92f8ceacd4",       "type": "foodLabel",       "labelId": 311,       "optionCategoryIdsForConflicts": [         18080,         61000,         90020       ],       "foodLabel": "eggplant",       "categoriesNames": []     }

  ],
  "matchedLabels": {
    "matchBased": [
     

{         "name": "None"       }

,
     

{         "name": "Pescatarian"       }

,
     

{         "name": "L_Vit. A"       }

    ],
    "noMatchedMicro": [],
    "userIncludedFoodLabels": [
      "banana",
      "carrot",
      "eggplant"
    ]
  }
}

I can successfully extract the data using the pandas library 
result = col.aggrerate(agg)
df = pandas.DataFrame(result)

However this doesnt work with polars.DataFrame or with pymongoarrow aggregate_polars_all.    

This is more of an irritant than a show stopper, but it raises the concern that  perhaps polars a) isn't compatible with 3.11 or that pymongo is the cause

So anyone gone too far on the bleeding edge?