如何使用Python從JSONAPI中選擇、映射和統計資料？

我是 Python 新手，正在努力為以下問題找到正確的方法：

我有 2 個 API 回應，一個是設備串列，另一個是組織串列。每臺設備都通過一個組織 ID 鏈接到一個組織。

organizations = [
                 {
                  'name': 'Aperture Science Inc.', 
                  'description': 'Just a corporation!', 
                  'id': 1
                 }, 
                 {
                  'name': 'Software Development Inc', 
                  'description': "Making the world's next best app!", 
                  'id': 2
                 }
                ]

devices = [
           {
            'id': 1, 
            'organizationId': 2, 
            'nodeClass': 'WINDOWS_WORKSTATION', 
            'displayName': 'DESKTOP_01'
            },{
            'id': 2, 
            'organizationId': 2, 
            'nodeClass': 'WINDOWS_SERVER', 
            'displayName': 'SERVER_01'
            },{
            'id': 3, 
            'organizationId': 1, 
            'nodeClass': 'WINDOWS_WORSTATION', 
            'displayName': 'DESKTOP_0123'
            }
           ]

設備中的 OrganizationID = 組織中的 ID。我想分別獲得每個組織的服務器和作業站數量的結果，如下所示：

results = [
           { 
            'Organization Name' : 'Aperture Science Inc.', 
            'Number of Workstations': 1, 
            'Number of Servers': 0,
            'Total devices': 1
           }, 
           { 
            'Organization Name' : 'Software Development Inc', 
            'Number of Workstations': 1, 
            'Number of Servers': 1,
            'Total devices': 2
           }

我從這個開始

wks_sum = sum(d.nodeClass == "WINDOWS_WORKSTATION" for d in devices)
print(wks_sum)

但我收到此錯誤：

AttributeError: 'dict' 物件沒有屬性 'nodeClass'

最后我轉換并保存在一個 csv 檔案中：

df = pd.DataFrame(results)
df.to_csv('results.csv', index=False)

我正在努力計算每種設備型別并將設備映射到正確的組織名稱，非常感謝一些幫助:)

編輯：

感謝@Vincent，我可以想出：

    for device in devices:
    for organization in organizations:
        organization["workstations"] = organization.get("workstations", [])
        organization["servers"] = organization.get("servers", [])
        if device["organizationId"] != organization["id"]:
            continue
        if device["nodeClass"].__eq__("WINDOWS_SERVER"):
            organization["servers"].append(device["nodeClass"])
        elif device["nodeClass"].__eq__("WINDOWS_WORKSTATION"):
            organization["workstations"].append(device["nodeClass"])
        break

results = [
    {
        "Organization Name": organization["name"],
        "Number of Workstations": len(organization["workstations"]),
        "Number of Servers": len(organization["servers"]),
        "Total devices": len(organization["workstations"]   organization["servers"]),
    } for organization in organizations
]


# print(f"{results = }")
print(results)


# convert and save in a csv file

df = pd.DataFrame(results)
df.to_csv('results.csv', index=False)

uj5u.com熱心網友回復：

此代碼將實作您的目標：

organizations = [
     {
      'name': 'Aperture Science Inc.', 
      'description': 'Just a corporation!', 
      'id': 1
     }, 
     {
      'name': 'Software Development Inc', 
      'description': "Making the world's next best app!", 
      'id': 2
     }
]

devices = [
    {
    'id': 1, 
    'organizationId': 2, 
    'nodeClass': 'WINDOWS_WORKSTATION', 
    'displayName': 'DESKTOP_01'
    },{
    'id': 2, 
    'organizationId': 2, 
    'nodeClass': 'WINDOWS_SERVER', 
    'displayName': 'SERVER_01'
    },{
    'id': 3, 
    'organizationId': 1, 
    'nodeClass': 'WINDOWS_WORSTATION', 
    'displayName': 'DESKTOP_0123'
    }
]

for device in devices:
    for organization in organizations:
        organization["workstations"] = organization.get("workstations", [])
        organization["servers"] = organization.get("servers", [])
        if device["organizationId"] != organization["id"]:
            continue
        if device["displayName"].startswith("SERVER_"):
            organization["servers"].append(device["nodeClass"])
        elif device["displayName"].startswith("DESKTOP_"):
            organization["workstations"].append(device["nodeClass"])
        break
        
results = [
    {
        "Organization Name": organization["name"],
        "Number of Workstations": len(organization["workstations"]),
        "Number of Servers": len(organization["servers"]),
        "Total devices": len(organization["workstations"]   organization["servers"]),
    } for organization in organizations
]


print(f"{results = }")

結果：

[{'Organization Name': 'Aperture Science Inc.', 'Number of Workstations': 1, 'Number of Servers': 0, 'Total devices': 1}, {'Organization Name': 'Software Development Inc', 'Number of Workstations': 1, 'Number of Servers': 1, 'Total devices': 2}]

確實，您可以使用諸如 Pandas 之類的晦澀庫來完成，但我認為像這樣的慢速代碼更好地了解已完成的操作，并且在需要時更易于修改。

處理海量資料，例如使用sqlite3轉儲到兩個sql表中，然后處理SQL。

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