轉換 dataframe 列中的整數 arrays 中的字符串數組

Question

我正在嘗試轉換 arrays 中的字符串數組，這些整數在 dataframe 列中關聯其 ID。

那是因為我需要 map 每個 id 的家庭房間列表，如下所示：

那就是 JSON 我必須 map：

[ 
   {
     "id": 1,
     "name": "dining room",
   }, 
   {
      "id": 2,
      "name": "living room",
   },
   {
      "id": 3,
      "name": "guest room",
   },
   {
      "id": 4,
      "name": "bathroom",
   },
   {
      "id": 5,
      "name": "game room",
   },
   {
      "id": 6,
      "name": "kitchen",
   },
   {
      "id": 7,
      "name": "storage room",
   },
   {
      "id": 8,
      "name": "bedroom",
   },
   {
      "id": 9,
      "name": "family room",
   }
]

那就是 dataframe 我有：

index     home_rooms             
0         [dining room, living room, bathroom]                     
1         [guest room, kitchen, game room] 
2         [storage room, family room, bedroom] 
3         [dining room, living room, bathroom] 
4         [guest room, kitchen, game room]
5         [storage room, family room, bedroom] 
6         [dining room, living room, bathroom] 
7         [guest room, kitchen, game room]
8         [storage room, family room, bedroom]

這就是我需要的 dataframe：

index     home_rooms             
0         [1, 2, 4]                     
1         [3, 6, 5] 
2         [7, 9, 8] 
3         [1, 2, 4]
4         [3, 6, 5]
5         [7, 9, 8] 
6         [1, 2, 4] 
7         [3, 6, 5]
8         [7, 9, 8]

有什么解決辦法嗎？

提前致謝。

Answer 1

我們將 json 字符串稱為l_str 。 將其加載到 dataframe 作為df_map 。 從df_map構造字典d中的結構name: id 。 使用itemgetter和列表理解來構造每個index的id列表

from operator import itemgetter

df_map = pd.read_json(l_str)
d = dict(zip(df_map.name, df_map.id))
df['home_rooms'] = [list(itemgetter(*x)(d)) for x in df.home_rooms]

Out[415]:
   index home_rooms
0      0  [1, 2, 4]
1      1  [3, 6, 5]
2      2  [7, 9, 8]
3      3  [1, 2, 4]
4      4  [3, 6, 5]
5      5  [7, 9, 8]
6      6  [1, 2, 4]
7      7  [3, 6, 5]
8      8  [7, 9, 8]

Answer 2

嘗試：

mapper = pd.read_json(jsonstr).set_index('name')['id']
df_out = df.explode('home_rooms').replace('dinig room', 'dining room') #fix typo with replace
df_out['home_rooms'] = df_out['home_rooms'].map(mapper)
df_out.groupby('index').agg(list).reset_index()

Output：

   index home_rooms
0      0  [1, 2, 4]
1      1  [3, 6, 5]
2      2  [7, 9, 8]
3      3  [1, 2, 4]
4      4  [3, 6, 5]
5      5  [7, 9, 8]
6      6  [1, 2, 4]
7      7  [3, 6, 5]
8      8  [7, 9, 8]