Python:解析json列表

Python:解析json列表,json,Json,大家好,我基本上想解析这个json数据 [ { "id": 417862, "name": "octokit.rb", "full_name": "octokit/octokit.rb", "owner": { "login": "octokit", "id": 3430433, "avatar_url": "https://avatars0.githu

大家好,我基本上想解析这个json数据

[
    {
        "id": 417862,
        "name": "octokit.rb",
        "full_name": "octokit/octokit.rb",
        "owner": {
            "login": "octokit",
            "id": 3430433,
            "avatar_url": "https://avatars0.githubusercontent.com/u/3430433?v=4",
            "gravatar_id": "",
            "url": "https://api.github.com/users/octokit",
            "html_url": "https://github.com/octokit",
            "followers_url": "https://api.github.com/users/octokit/followers",
            "following_url": "https://api.github.com/users/octokit/following{/other_user}",
            "gists_url": "https://api.github.com/users/octokit/gists{/gist_id}",
            "starred_url": "https://api.github.com/users/octokit/starred{/owner}{/repo}",
            "subscriptions_url": 
        } ]
我的问题是如何从json获取
“id”:417862
的值

谢谢。

那么:

mylist = [{
  "id": 417862,
  "name": "octokit.rb",
  "full_name": "octokit/octokit.rb",
  "owner": {
    "login": "octokit",
    "id": 3430433,
    "avatar_url": "https://avatars0.githubusercontent.com/u/3430433?v=4",
    "gravatar_id": "",
    "url": "https://api.github.com/users/octokit",
    "html_url": "https://github.com/octokit",
    "followers_url": "https://api.github.com/users/octokit/followers",
    "following_url": "https://api.github.com/users/octokit/following{/other_user}",
    "gists_url": "https://api.github.com/users/octokit/gists{/gist_id}",
    "starred_url": "https://api.github.com/users/octokit/starred{/owner}{/repo}",
    "subscriptions_url": "cats"
  }
}]

print mylist[0]['id']

这将返回打印
mylist
的第0个(即第一个)成员的
id
属性,该属性等于
417862

,假设您正在使用熊猫: 生成一个函数并返回列表的第一个值

def get_json_data(mylist):
    return mylist[0]
创建新列mn并应用该函数

df['new_col']=df.apply(lambda df: get_json_data(df['old_col']),axis=1)

一旦你拿到了gata,就把它正常化

pd.io.json.json_normalize(df['new_col'])


希望这有帮助:)

你在使用什么编程语言/库?我在使用python!!可能重复的