Python 如何在django中返回此JSON而不转义？

在我的Django项目中，我试图返回一个

JsonResponse

，但是返回的数据正在“某处”转义。当我通过Jupyter笔记本运行代码时，我没有问题。我的数据帧结构是：

---

我的Django响应读取我的数据帧pickle并按如下方式处理：

def API_FTEs_month(request, storeCode):
    df1=pd.read_pickle(storeCode+'.pickle')
    result=(df1.groupby(['Date','Job Role'], as_index=False)
        .apply(lambda i: i[['Department', 'Team', 'Days']].to_dict('r'))
         .reset_index()
         .rename(columns={0: 'Assignments'})
         .to_json(orient='records'))
return JsonResponse(result, safe=False)

"[{\"Date\":\"2017-12-31\",\"Job Role\":\"Junior\",\"Assignments\":[{\"Department\":\"Electronics\",\"Team\":\"A\",\"Days\":12.8311126233},{\"Department\":\"Electronics\",\"Team\":\"B\",\"Days\":9.7797036952},{\"Department\":\"Electronics\",\"Team\":\"C\",\"Days\":12.4532628859},{\"Department\":\"Household\",\"Team\":\"A\",\"Days\":13.2005991473},{\"Department\":\"Household\",\"Team\":\"B\",\"Days\":11.2217690247},{\"Department\":\"Household\",\"Team\":\"C\",\"Days\":9.9799650502}]},{\"Date\":\"2017-12-31\",\"Job Role\":\"Senior\",\"Assignments\":[{\"Department\":\"Electronics\",\"Team\":\"A\",\"Days\":12.3088204188},{\"Department\":\"Electronics\",\"Team\":\"B\",\"Days\":11.6027520428},{\"Department\":\"Electronics\",\"Team\":\"C\",\"Days\":8.4242249342},{\"Department\":\"Household\",\"Team\":\"A\",\"Days\":10.2680664459},{\"Department\":\"Household\",\"Team\":\"B\",\"Days\":10.7355819544},{\"Department\":\"Household\",\"Team\":\"C\",\"Days\":14.4751405746}]},{\"Date\":\"2018-01-31\",\"Job Role\":\"Junior\",\"Assignments\":[{\"Department\":\"Electronics\",\"Team\":\"A\",\"Days\":9.8390990646},{\"Department\":\"Electronics\",\"Team\":\"B\",\"Days\":7.8840336082},{\"Department\":\"Electronics\",\"Team\":\"C\",\"Days\":7.4098884623},{\"Department\":\"Household\",\"Team\":\"A\",\"Days\":6.5804561812},{\"Department\":\"Household\",\"Team\":\"B\",\"Days\":7.9109739164},{\"Department\":\"Household\",\"Team\":\"C\",\"Days\":7.6766255979}]},{\"Date\":\"2018-01-31\",\"Job Role\":\"Senior\",\"Assignments\":[{\"Department\":\"Electronics\",\"Team\":\"A\",\"Days\":5.9779944185},{\"Department\":\"Electronics\",\"Team\":\"B\",\"Days\":7.8300778676},{\"Department\":\"Electronics\",\"Team\":\"C\",\"Days\":7.9050436379},{\"Department\":\"Household\",\"Team\":\"A\",\"Days\":6.9225874658},{\"Department\":\"Household\",\"Team\":\"B\",\"Days\":7.6001780124},{\"Department\":\"Household\",\"Team\":\"C\",\"Days\":6.5897367619}]}]"

我不知道为什么，但反应如下：

def API_FTEs_month(request, storeCode):
    df1=pd.read_pickle(storeCode+'.pickle')
    result=(df1.groupby(['Date','Job Role'], as_index=False)
        .apply(lambda i: i[['Department', 'Team', 'Days']].to_dict('r'))
         .reset_index()
         .rename(columns={0: 'Assignments'})
         .to_json(orient='records'))
return JsonResponse(result, safe=False)

"[{\"Date\":\"2017-12-31\",\"Job Role\":\"Junior\",\"Assignments\":[{\"Department\":\"Electronics\",\"Team\":\"A\",\"Days\":12.8311126233},{\"Department\":\"Electronics\",\"Team\":\"B\",\"Days\":9.7797036952},{\"Department\":\"Electronics\",\"Team\":\"C\",\"Days\":12.4532628859},{\"Department\":\"Household\",\"Team\":\"A\",\"Days\":13.2005991473},{\"Department\":\"Household\",\"Team\":\"B\",\"Days\":11.2217690247},{\"Department\":\"Household\",\"Team\":\"C\",\"Days\":9.9799650502}]},{\"Date\":\"2017-12-31\",\"Job Role\":\"Senior\",\"Assignments\":[{\"Department\":\"Electronics\",\"Team\":\"A\",\"Days\":12.3088204188},{\"Department\":\"Electronics\",\"Team\":\"B\",\"Days\":11.6027520428},{\"Department\":\"Electronics\",\"Team\":\"C\",\"Days\":8.4242249342},{\"Department\":\"Household\",\"Team\":\"A\",\"Days\":10.2680664459},{\"Department\":\"Household\",\"Team\":\"B\",\"Days\":10.7355819544},{\"Department\":\"Household\",\"Team\":\"C\",\"Days\":14.4751405746}]},{\"Date\":\"2018-01-31\",\"Job Role\":\"Junior\",\"Assignments\":[{\"Department\":\"Electronics\",\"Team\":\"A\",\"Days\":9.8390990646},{\"Department\":\"Electronics\",\"Team\":\"B\",\"Days\":7.8840336082},{\"Department\":\"Electronics\",\"Team\":\"C\",\"Days\":7.4098884623},{\"Department\":\"Household\",\"Team\":\"A\",\"Days\":6.5804561812},{\"Department\":\"Household\",\"Team\":\"B\",\"Days\":7.9109739164},{\"Department\":\"Household\",\"Team\":\"C\",\"Days\":7.6766255979}]},{\"Date\":\"2018-01-31\",\"Job Role\":\"Senior\",\"Assignments\":[{\"Department\":\"Electronics\",\"Team\":\"A\",\"Days\":5.9779944185},{\"Department\":\"Electronics\",\"Team\":\"B\",\"Days\":7.8300778676},{\"Department\":\"Electronics\",\"Team\":\"C\",\"Days\":7.9050436379},{\"Department\":\"Household\",\"Team\":\"A\",\"Days\":6.9225874658},{\"Department\":\"Household\",\"Team\":\"B\",\"Days\":7.6001780124},{\"Department\":\"Household\",\"Team\":\"C\",\"Days\":6.5897367619}]}]"

---

在Jupyter Notebook中重新创建尝试我似乎得到了有效的JSON:

笔记本电脑：

我假设问题出在我的

to_json（）

或

JsonResponse

中，但我尝试将其他json尝试插入到我的

JsonResponse

中，例如以下内容，它在不转义的情况下为我提供了有效的json（但不是所需的结构）：

def nested_dict():
    return collections.defaultdict(nested_dict)
result=nested_dict()

for row in df4.itertuples():
    result[row.Index[0]][row.Index[1]][row[1]][row[2]]['sales'] = row.Days

json.dumps(result)

您需要更改：

.to_json(orient='records')

致：

为了防止双重编码的json

这在以下评论中得到了更好的解释：

JsonResponse需要一个Python对象，然后对该对象进行JSON编码。您已经传入了JSON，导致JSON编码JSON

---

如果将

更改为_json

更改为

更改为_dict

，它是如何工作的？

JsonResponse

需要一个Python对象，然后该对象将被json编码。您已经在使用JSON了，结果是JSON编码的JSON.D'oh！谢谢你们两位！