Creating Pandas DataFrame on Python From a MongoDB Document containing Embedded documents

I am working on a machine learning algorithm on python's scikit.learn, but this time the data are in Mongodb documents format. I would like to pull my data into a dataframe. Here is an exemple of the documents:

{
    "_id" : ObjectId("552b9525359c6a09f061cb53"),
    "Interrupt" : true,
    "Url" : "Coco_mademoiselle.jpg",
    "Target" : {
        "FemaleInPercent" : 100,
        "MaleInPercent" : 0,
        "AgeProperties" : 6
    },
    "MaxDisplayTime" : 7,
    "MinDisplayTime" : 2,
    "MediaType" : 0,
    "IsLocked" : false,
    "FaceTagged" : [ 
        {
            "FaceId" : 36,
            "GenderConfidence" : -0.1731295609721586,
            "Age" : 23,
            "TotalAttention" : 14.92099999999997,
            "AttentionInsideThisContent" : 2.273999999999992,
            "Gender" : "Unknown",
            "AngleYaw" : [ 
                0
            ],
            "XPos" : [ 
                0.07704142996903575
            ],
            "YPos" : [ 
                0.7182761555157026
            ],
            "Distance" : [ 
                0.7223960254002589
            ]
        }, 
        {
            "FaceId" : 37,
            "GenderConfidence" : 0.3932732620245187,
            "Age" : 51,
            "TotalAttention" : 14.92099999999997,
            "AttentionInsideThisContent" : 2.273999999999992,
            "Gender" : "Female",
            "AngleYaw" : [ 
                0
            ],
            "XPos" : [ 
                0.9852976840852283
            ],
            "YPos" : [ 
                -0.9149562017596122
            ],
            "Distance" : [ 
                1.344602683844596
            ]
        }
    ],
    "PanelId" : "PANEL_1",
    "ScenarioId" : "Scenario-1",
    "StartTime" : ISODate("2015-04-13T10:06:22.622Z"),
    "EndTime" : ISODate("2015-04-13T10:06:29.640Z")
}

I used this function to put my data into a pandas dataframe but I have some issues with my embedded documents and array of documents:

def read_mongo(db, collection, query={}, host='localhost', port=27017, username=None, password=None, no_id=True):
    """ Read from Mongo and Store into DataFrame """

    # Make a query to the specific DB and Collection
    cursor = collection.find(query)

    # Expand the cursor and construct the DataFrame
    df =  pd.DataFrame(list(cursor))

    # Delete the _id
    if no_id:
        del df['_id']

    return df

Finally, i get a Dataframe with one column containing the FaceTagged informations gathered all together :

data.FaceTagged.to_frame()
                                           FaceTagged
0   [{u'Distance': [0.871754460354], u'XPos': [0.7...
1   [{u'Distance': [0.845591660012], u'XPos': [0.6...
2   [{u'Distance': [1.01813052012], u'XPos': [-0.7...

each line contain all the fields from only one document besides the fact FaceTagged is an array of documents, and each document contains severals fields.

Anyone can relate to this?