Python 将dict转换为数据帧

我的数据如下所示：

{u'"57e01311817bc367c030b390"': u'{"ad_since": 2016, "indoor_swimming_pool": "No", "seaside": "No", "handicapped_access": "Yes"}', u'"57e01311817bc367c030b3a8"': u'{"ad_since": 2012, "indoor_swimming_pool": "No", "seaside": "No", "handicapped_access": "Yes"}'}

我想把它转换成熊猫数据帧。但是当我尝试的时候

df = pd.DataFrame(response.items())

我得到一个包含两列的数据帧，第一列包含第一个键，第二列包含键的值：

                            0                       1 
0  "57e01311817bc367c030b390"   {"ad_since": 2016, "indoor_swimming_pool": "No...
1  "57e01311817bc367c030b3a8"   {"ad_since": 2012, "indoor_swimming_pool": "No... 

---

如何为每个键获取一列：

“ad\U自”

，

“室内游泳池”

，

“室内游泳池”

？并保留第一列，或获取id作为索引。

您需要通过

将
类型的列
str
转换为
dict
。apply（literal\u eval）
或
。apply（json.loads）
，然后使用：

由于值是字符串，因此可以使用和列表：

In [20]: d =     {u'"57e01311817bc367c030b390"': u'{"ad_since": 2016, "indoor_swimming_pool": "No", "seaside": "No", "handicapped_access": "Yes"}', u'"57e01311817bc367c030b3a8"': u'{"ad_since": 2012, "indoor_swimming_pool": "No", "seaside": "No", "handicapped_access": "Yes"}'}

In [21]: import json

In [22]: pd.DataFrame(dict([(k, [json.loads(e)[k] for e in d.values()]) for k in json.loads(d.values()[0])]), index=d.keys())Out[22]: 
                            ad_since handicapped_access indoor_swimming_pool  \
"57e01311817bc367c030b390"      2016                Yes                   No   
"57e01311817bc367c030b3a8"      2012                Yes                   No   

                       seaside  
"57e01311817bc367c030b390"      No  
"57e01311817bc367c030b3a8"      No  

Try read_json您是否使用
pd.DataFrame（response.items（））
尝试示例数据？对我来说，它不起作用。@jezrael谢谢你的评论，我编辑了我的post@RichardRublev我试过了，但出现了错误
TypeError:Expected String或Unicode
@mitsi-谢谢。但我认为两条记录很好，但现在只有一条记录——数据帧中的第二行丢失了。你能添加一些json或json列表吗？使用第一个方法（使用
literal\u eval
）和整个数据集，我得到了错误
ValueError:malformed string
可能是因为特殊字符。但是它与第二种方法的json.loads
json.loads完美结合，谢谢你，我很高兴能帮助你。
import pandas as pd
import json

response = {u'"57e01311817bc367c030b390"': u'{"ad_since": 2016, "indoor_swimming_pool": "No", "seaside": "No", "handicapped_access": "Yes"}', 
           u'"57e01311817bc367c030b3a8"': u'{"ad_since": 2012, "indoor_swimming_pool": "No", "seaside": "No", "handicapped_access": "Yes"}'}


df = pd.DataFrame.from_dict(response, orient='index')
df.iloc[:,0] = df.iloc[:,0].apply(json.loads)


print (pd.DataFrame.from_records(df.iloc[:,0].values.tolist(), index=df.index))
                            ad_since handicapped_access indoor_swimming_pool  \
"57e01311817bc367c030b3a8"      2012                Yes                   No   
"57e01311817bc367c030b390"      2016                Yes                   No   

                           seaside  
"57e01311817bc367c030b3a8"      No  
"57e01311817bc367c030b390"      No  

In [20]: d =     {u'"57e01311817bc367c030b390"': u'{"ad_since": 2016, "indoor_swimming_pool": "No", "seaside": "No", "handicapped_access": "Yes"}', u'"57e01311817bc367c030b3a8"': u'{"ad_since": 2012, "indoor_swimming_pool": "No", "seaside": "No", "handicapped_access": "Yes"}'}

In [21]: import json

In [22]: pd.DataFrame(dict([(k, [json.loads(e)[k] for e in d.values()]) for k in json.loads(d.values()[0])]), index=d.keys())Out[22]: 
                            ad_since handicapped_access indoor_swimming_pool  \
"57e01311817bc367c030b390"      2016                Yes                   No   
"57e01311817bc367c030b3a8"      2012                Yes                   No   

                       seaside  
"57e01311817bc367c030b390"      No  
"57e01311817bc367c030b3a8"      No