pandas json_normalize和JSON中的空值 [英] Pandas json_normalize and null values in JSON
本文介绍了 pandas json_normalize和JSON中的空值的处理方法,对大家解决问题具有一定的参考价值,需要的朋友们下面随着小编来一起学习吧!
问题描述
我有这个样本JSON
I have this sample JSON
{
"name":"John",
"age":30,
"cars": [
{ "name":"Ford", "models":[ "Fiesta", "Focus", "Mustang" ] },
{ "name":"BMW", "models":[ "320", "X3", "X5" ] },
{ "name":"Fiat", "models":[ "500", "Panda" ] }
]
}
当我需要将JSON转换为pandas DataFrame时,我使用以下代码
When I need to convert JSON to pandas DataFrame I use following code
import json
from pandas.io.json import json_normalize
from pprint import pprint
with open('example.json', encoding="utf8") as data_file:
data = json.load(data_file)
normalized = json_normalize(data['cars'])
此代码效果很好,但是在一些空车(空值)的情况下,我无法进行normalize_json.
This code works well but in the case of some empty cars (null values) I'm not possible to normalize_json.
json的示例
{
"name":"John",
"age":30,
"cars": [
{ "name":"Ford", "models":[ "Fiesta", "Focus", "Mustang" ] },
null,
{ "name":"Fiat", "models":[ "500", "Panda" ] }
]
}
引发的错误
AttributeError: 'NoneType' object has no attribute 'keys'
我试图忽略json_normalize中的错误,但没有帮助
I tried to ignore errors in json_normalize, but didn't help
normalized = json_normalize(data['cars'], errors='ignore')
我应该如何处理JSON中的空值?
How should I handle null values in JSON?
推荐答案
您可以用空指令填充cars
,以防止出现此错误
You can fill cars
with empty dicts to prevent this error
data['cars'] = data['cars'].apply(lambda x: {} if pd.isna(x) else x)
这篇关于 pandas json_normalize和JSON中的空值的文章就介绍到这了,希望我们推荐的答案对大家有所帮助,也希望大家多多支持IT屋!
查看全文