簡體   English   中英

將python嵌套的類似JSON的數據轉換為dataframe

[英]Convert python nested JSON-like data to dataframe

我的記錄如下所示,我需要將其寫入一個csv文件中:

my_data={"data":[{"id":"xyz","type":"book","attributes":{"doc_type":"article","action":"cut"}}]}

看起來像json,但下一條記錄以"data"而不是"data1"開頭,這迫使我分別讀取每條記錄。 然后,我使用eval()將其轉換為dict,以迭代鍵和值的某個路徑以獲取所需的值。 然后,我根據需要的鍵生成鍵和值的列表。 然后, pd.dataframe()將該列表轉換為我知道如何轉換為csv的數據pd.dataframe() 我的有效代碼如下。 但我相信,有更好的方法可以做到這一點。 地雷的伸縮性很差。 謝謝。

counter=1
k=[]
v=[]
res=[]
m=0
for line in f2:
    jline=eval(line)
counter +=1
for items in jline:
    k.append(jline[u'data'][0].keys())
    v.append(jline[u'data'][0].values())
print 'keys are:', k
i=0
j=0
while i <3 :
    while j <3:
        if k[i][j]==u'id':
            res.append(v[i][j])
        j += 1    
    i += 1
#res is my result set
del k[:]
del v[:]

將my_data更改為:

my_data = [{"id":"xyz","type":"book","attributes":{"doc_type":"article","action":"cut"}}, # Data One
{"id":"xyz2","type":"book","attributes":{"doc_type":"article","action":"cut"}}, # Data Two
{"id":"xyz3","type":"book","attributes":{"doc_type":"article","action":"cut"}}] # Data Three

您可以這樣將其直接轉儲到數據幀中:

mydf = pd.DataFrame(my_data)

尚不清楚您的數據路徑是什么,但是如果您要查找idtype等的特定組合,則可以顯式搜索

def find_my_way(data, pattern):

    # pattern = {'id':'someid', 'type':'sometype'...}
    res = []
    for row in data:
        if row.get('id') == pattern.get('id'):
            res.append(row)
    return row


mydf = pd.DataFrame(find_my_way(mydata, pattern))

編輯:

在不討論api的工作原理的情況下,您將需要執行以下偽代碼:

my_objects = []
calls = 0
while calls < maximum:

    my_data = call_the_api(params)

    data = my_data.get('data')

    if not data:
        calls+=1
        continue

    # Api calls to single objects usually return a dictionary, to group objects they return lists. This handles both cases
    if isinstance(data, list):
        my_objects = [*data, *my_objects]

    elif isinstance(data, {}):
        my_objects = [{**data}, *my_objects]

# This will unpack the data response into a list that you can then load into a DataFrame with the attributes from the api as the columns

df = pd.DataFrame(my_objects)

假設您從api獲得的數據如下所示:

"""
 {
 "links": {},
 "meta": {},
 "data": {
    "type": "FactivaOrganizationsProfile",
    "id": "Goog",
    "attributes": {
      "key_executives": {
        "source_provider": [
          {
            "code": "FACSET",
            "descriptor": "FactSet Research Systems Inc.",
            "primary": true
          }
        ]
      }
    },
    "relationships": {
      "people": {
        "data": {
            "type": "people",
            "id": "39961704"
          }
      }
    }
  },
 "included": {}
 }
 """

根據文檔,這就是為什么我使用my_data.get('data')

那應該使您所有的數據(未經過濾)進入DataFrame

DataFrame保存為最后一點對內存更友好

暫無
暫無

聲明:本站的技術帖子網頁,遵循CC BY-SA 4.0協議,如果您需要轉載,請注明本站網址或者原文地址。任何問題請咨詢:yoyou2525@163.com.

 
粵ICP備18138465號  © 2020-2024 STACKOOM.COM