Pandas json_normalize 会产生令人困惑的“KeyError”消息？
声明：本页面是StackOverFlow热门问题的中英对照翻译，遵循CC BY-SA 4.0协议，如果您需要使用它，必须同样遵循CC BY-SA许可，注明原文地址和作者信息，同时你必须将它归于原作者（不是我）：StackOverFlow
原文地址: http://stackoverflow.com/questions/32291437/
Warning: these are provided under cc-by-sa 4.0 license. You are free to use/share it, But you must attribute it to the original authors (not me):
StackOverFlow
Pandas json_normalize produces confusing `KeyError` message?
提问by themachinist
I'm trying to convert a nested JSON to a Pandas dataframe. I've been using json_normalizewith success until I came across a certain JSON. I've made a smaller version of it to recreate the problem.
我正在尝试将嵌套的 JSON 转换为 Pandas 数据框。我一直在json_normalize成功使用，直到遇到某个 JSON。我制作了一个较小的版本来重现这个问题。
from pandas.io.json import json_normalize
json=[{"events": [{"schedule": {"date": "2015-08-27",
"location": {"building": "BDC", "floor": 5},
"ID": 815},
"group": "A"},
{"schedule": {"date": "2015-08-27",
"location": {"building": "BDC", "floor": 5},
"ID": 816},
"group": "A"}]}]
I then run:
然后我运行：
json_normalize(json[0],'events',[['schedule','date'],['schedule','location','building'],['schedule','location','floor']])
Expecting to see something like this:
期待看到这样的事情：
ID group schedule.date schedule.location.building schedule.location.floor
'815' 'A' '2015-08-27' 'BDC' 5
'816' 'A' '2015-08-27' 'BDC' 5
But instead I get this error:
但是我收到了这个错误：
In [2]: json_normalize(json[0],'events',[['schedule','date'],['schedule','location','building'],['schedule','location','floor']])
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-2-b588a9e3ef1d> in <module>()
----> 1 json_normalize(json[0],'events',[['schedule','date'],['schedule','location','building'],['schedule','location','floor']])
/Users/logan/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pandas/io/json.pyc in json_normalize(data, record_path, meta, meta_prefix, record_prefix)
739 records.extend(recs)
740
--> 741 _recursive_extract(data, record_path, {}, level=0)
742
743 result = DataFrame(records)
/Users/logan/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pandas/io/json.pyc in _recursive_extract(data, path, seen_meta, level)
734 meta_val = seen_meta[key]
735 else:
--> 736 meta_val = _pull_field(obj, val[level:])
737 meta_vals[key].append(meta_val)
738
/Users/logan/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pandas/io/json.pyc in _pull_field(js, spec)
674 if isinstance(spec, list):
675 for field in spec:
--> 676 result = result[field]
677 else:
678 result = result[spec]
KeyError: 'schedule'
采纳答案by chrisb
In this case, I think you'd just use this:
在这种情况下，我认为你只需要使用这个：
In [57]: json_normalize(data[0]['events'])
Out[57]:
group schedule.ID schedule.date schedule.location.building \
0 A 815 2015-08-27 BDC
1 A 816 2015-08-27 BDC
schedule.location.floor
0 5
1 5
The metapaths ([['schedule','date']...]) are for specifying data at the same level of nesting as your records, i.e. at the same level as 'events'. It doesn't look like json_normalizehandles dicts with nested lists particularly well, so you may need to do some manual reshaping if your actual data is much more complicated.
该meta路径（[['schedule','date']...]）是在同一级别为“事件”嵌套为您的记录，即同级别指定数据。它看起来不像json_normalize处理嵌套列表的字典特别好，所以如果你的实际数据要复杂得多，你可能需要做一些手动整形。
回答by Sandesh
I got the KeyError when the structue of the json was not consistent. Meaning, when one of the nested strucutes were missing from the json, I got KeyError.
当 json 的结构不一致时，我得到了 KeyError。意思是，当 json 中缺少嵌套结构之一时，我得到了 KeyError。
https://pandas.pydata.org/pandas-docs/stable/generated/pandas.io.json.json_normalize.html
https://pandas.pydata.org/pandas-docs/stable/generated/pandas.io.json.json_normalize.html
From the examples mentioned on the pandas documentation site, if you make the nested tag (counties) missing on one of the records, you will get a KeyError. To circumvent this, you might have to make sure ignore the missing tag or consider only the records which have nested column/tag populated with data.
从 pandas 文档站点上提到的示例中，如果您在其中一条记录中缺少嵌套标记（县），您将收到 KeyError。为了避免这种情况，您可能必须确保忽略丢失的标签或仅考虑使用数据填充的嵌套列/标签的记录。
回答by Jim Arnold
I had this same problem! This thread helped, especially parachute py's answer.
我有同样的问题！该线程有所帮助，尤其是降落伞 py 的回答。
I found a solution using:
我找到了一个解决方案：
df.dropna(subset = *column(s) with nested data*)
then saving the resultant dfas a new json.
Load the new json and now you'll be able to flatten the nested columns.
然后将结果保存df为新的 json。加载新的 json，现在您将能够展平嵌套的列。
There's probably a more efficient way to get around this, but my solution works.
可能有更有效的方法来解决这个问题，但我的解决方案有效。
edit: forgot to mention, I tried using the *errors = 'ignore'*arg in json.normalize()and it didn't help.
编辑：忘了提及，我尝试使用*errors = 'ignore'*argjson.normalize()并没有帮助。
