使用嵌套的字典和列表解析 JSON

Question

I'm pretty new to Python and using Libraries effectively. I'm currently stuck parsing a JSON to a Dataframe.

This is my dict :

 data = { "event": { "id": 323725, "code": 981, "sport": "Football", "tournament": "Bresil D1", "name": "Sao Paulo - Fortaleza Ce", "homeTeam": "Sao Paulo", "awayTeam": "Fortaleza Ce", "markets": [ { "marketName": "R\ésultat Final", "status": "active", "antepost": false, "marketID": "0", "marketFixedID": "285", "columnType": 0, "marketGroupID": [ "g_s-441_11111111", "g_s-441_261", "g_s-441_11111111", "g_s-441_11111111" ], "marketOrder": 1, "odds": [ { "id": "323725_0_400", "name": "SAO PAULO", "short": "SAO PAULO", "clean": "1", "status": "active", "odd": 1.85, "handicap": 0 }, { "id": "323725_0_401", "name": "X", "short": "X", "clean": "X", "status": "active", "odd": 2.65, "handicap": 0 }, { "id": "323725_0_402", "name": "FORTALEZA CE", "short": "FORTALEZA CE", "clean": "2", "status": "active", "odd": 3.5, "handicap": 0 } ], "minimumRestriction": 1 }, { "marketName": "R\ésultat Final avec Handicap (0:1)", "status": "active", "antepost": false, "marketID": "10", "marketFixedID": "295", "columnType": 0, "marketGroupID": [ "g_s-441_11111111", "g_s-441_265", "g_s-441_11111111", "g_s-441_11111111" ], "marketOrder": 2, "isHandicap": true, "var0": -1, "odds": [ { "id": "323725_10_445", "name": "1", "short": "1", "clean": "1", "status": "active", "odd": 2.6, "handicap": 0 }, { "id": "323725_10_446", "name": "X", "short": "X", "clean": "X", "status": "active", "odd": 2.9, "handicap": 0 }, { "id": "323725_10_447", "name": "2", "short": "2", "clean": "2", "status": "active", "odd": 1.75, "handicap": 0 } ], "minimumRestriction": 1 }, { "marketName": "Moins\\/Plus 1,5", "status": "active", "antepost": false, "marketID": "19", "marketFixedID": "304", "columnType": 2, "marketGroupID": [ "g_s-441_11111111", "g_s-441_11111111", "g_s-441_11111111", "g_s-441_11111111" ], "marketOrder": 4, "isHandicap": false, "var0": 1.5, "odds": [ { "id": "323725_19_469", "name": "Moins ", "short": "Moins ", "clean": "Moins ", "status": "active", "odd": 2.65, "handicap": 0 }, { "id": "323725_19_470", "name": "Plus", "short": "Plus", "clean": "Plus", "status": "active", "odd": 1.15, "handicap": 0 } ], "minimumRestriction": 1 }, }

My goal is to parse this dict into a Dataframe containing "marketName" and each individual "odd" & it's "name", inside it.

First I tried to pull wanted data like this :

 markets = data['event']['markets'] for m in markets: marketName= m['marketName'] odds = m['odds']

From here I don't know how to deal with this data and put it correctly inside a coerent Dataframe

Answer 1

Try using a dictionary comprehension and pd.DataFrame.join :

markets = pd.DataFrame({k: v for i in data['event']['markets'] for k, v in i.items() if k in ['marketName', 'odds']})
markets = markets[['marketName']].join(pd.DataFrame(markets['odds'].tolist()).add_prefix('odds_'))

And now:

print(markets)

Outputs:

        marketName odds_clean  odds_handicap        odds_id odds_name  \
0  Moins\/Plus 1,5     Moins               0  323725_19_469    Moins    
1  Moins\/Plus 1,5       Plus              0  323725_19_470      Plus   

   odds_odd odds_short odds_status  
0      2.65     Moins       active  
1      1.15       Plus      active