Python 将JSON行解压缩到数据帧

我正在处理JSON行格式，并试图在单个列表中“解包”字典对象。因为它使用一个列表来保存dictionary对象，所以我以前没有找到任何关于这个问题的帖子。数据如下所示，列表对象中有一堆嵌套字典：

0        [{'created_at': 'Sun Jun 14 20:20:28 +0000 202...
1        [{'created_at': 'Sat Jul 25 22:30:14 +0000 202...
2        [{'created_at': 'Sat May 30 02:22:04 +0000 202...
3        [{'created_at': 'Tue May 05 16:54:05 +0000 202...
4        [{'created_at': 'Sat Jun 20 13:50:23 +0000 202...
                               ...                        
17453    [{'created_at': 'Mon Apr 13 01:01:10 +0000 202...
17454    [{'created_at': 'Fri Jul 17 09:00:50 +0000 202...
17455    [{'created_at': 'Sun Jun 21 00:51:54 +0000 202...
17456    [{'created_at': 'Tue Jun 02 18:23:49 +0000 202...
17457    [{'created_at': 'Thu May 28 00:27:01 +0000 202...

我现在尝试的是：

with open('data') as file:
    lines = file.read().splitlines()
df_inter = pd.DataFrame(lines)
df_inter.columns = ['json_element']

对于嵌套的附加项，我将使用本文提供的

pd.json\u规范化（df\u inter['json\u element'].apply（json.loads））

。但是，如何将多个dictionary对象解压缩到一行中

编辑

由于数据量巨大，我将提供部分单行数据：

[{'created_at': 'Sun Jun 14 20:20:28 +0000 2020', 'id': 1272262651100434433, 'id_str': '1272262651100434433', 'truncated': False, 'display_text_range': [0, 243], 'entities': {'hashtags': [{'text': 'Tenet', 'indices': [82, 88]}], 'symbols': [], 'user_mentions': [], 'urls': [], 'media': [{'id': 1272262640753094656, 'id_str': '1272262640753094656', 'indices': [244, 267], 'media_url': 'http://pbs.twimg.com/media/Eaf8IYsWsAAHVHV.jpg'...}]

---

如果您的

数据

文件如下所示：

[{"created_at": "Sun Jun 14 20:20:28 +0000 2020", "id": 1272262651100434433, "id_str": "1272262651100434433", "truncated": false, "display_text_range": [0, 243], "entities": {"hashtags": [{"text": "Tenet", "indices": [82, 88]}], "symbols": [], "user_mentions": [], "urls": [], "media": [{"id": 1272262640753094656, "id_str": "1272262640753094656", "indices": [244, 267], "media_url": "http://pbs.twimg.com/media/Eaf8IYsWsAAHVHV.jpg"}]}}]
[{"created_at": "Sun Jun 14 20:20:28 +0000 2020", "id": 1272262651100434433, "id_str": "1272262651100434433", "truncated": false, "display_text_range": [0, 243], "entities": {"hashtags": [{"text": "Tenet", "indices": [82, 88]}], "symbols": [], "user_mentions": [], "urls": [], "media": [{"id": 1272262640753094656, "id_str": "1272262640753094656", "indices": [244, 267], "media_url": "http://pbs.twimg.com/media/Eaf8IYsWsAAHVHV.jpg"}]}}]
[{"created_at": "Sun Jun 14 20:20:28 +0000 2020", "id": 1272262651100434433, "id_str": "1272262651100434433", "truncated": false, "display_text_range": [0, 243], "entities": {"hashtags": [{"text": "Tenet", "indices": [82, 88]}], "symbols": [], "user_mentions": [], "urls": [], "media": [{"id": 1272262640753094656, "id_str": "1272262640753094656", "indices": [244, 267], "media_url": "http://pbs.twimg.com/media/Eaf8IYsWsAAHVHV.jpg"}]}}]
[{"created_at": "Sun Jun 14 20:20:28 +0000 2020", "id": 1272262651100434433, "id_str": "1272262651100434433", "truncated": false, "display_text_range": [0, 243], "entities": {"hashtags": [{"text": "Tenet", "indices": [82, 88]}], "symbols": [], "user_mentions": [], "urls": [], "media": [{"id": 1272262640753094656, "id_str": "1272262640753094656", "indices": [244, 267], "media_url": "http://pbs.twimg.com/media/Eaf8IYsWsAAHVHV.jpg"}]}}]
[{"created_at": "Sun Jun 14 20:20:28 +0000 2020", "id": 1272262651100434433, "id_str": "1272262651100434433", "truncated": false, "display_text_range": [0, 243], "entities": {"hashtags": [{"text": "Tenet", "indices": [82, 88]}], "symbols": [], "user_mentions": [], "urls": [], "media": [{"id": 1272262640753094656, "id_str": "1272262640753094656", "indices": [244, 267], "media_url": "http://pbs.twimg.com/media/Eaf8IYsWsAAHVHV.jpg"}]}}]

                       created_at                   id               id_str  truncated display_text_range                                           entities
0  Sun Jun 14 20:20:28 +0000 2020  1272262651100434433  1272262651100434433      False           [0, 243]  {'hashtags': [{'text': 'Tenet', 'indices': [82...
1  Sun Jun 14 20:20:28 +0000 2020  1272262651100434433  1272262651100434433      False           [0, 243]  {'hashtags': [{'text': 'Tenet', 'indices': [82...
2  Sun Jun 14 20:20:28 +0000 2020  1272262651100434433  1272262651100434433      False           [0, 243]  {'hashtags': [{'text': 'Tenet', 'indices': [82...
3  Sun Jun 14 20:20:28 +0000 2020  1272262651100434433  1272262651100434433      False           [0, 243]  {'hashtags': [{'text': 'Tenet', 'indices': [82...
4  Sun Jun 14 20:20:28 +0000 2020  1272262651100434433  1272262651100434433      False           [0, 243]  {'hashtags': [{'text': 'Tenet', 'indices': [82...

您可以使用以下代码在jsonl文件中每行获取一个dataframe行

导入json
作为pd进口熊猫
以“打开”（“数据”）作为f：
df=pd.DataFrame（对于f中的行，json.load（行）[0]

您的df将如下所示：

[{"created_at": "Sun Jun 14 20:20:28 +0000 2020", "id": 1272262651100434433, "id_str": "1272262651100434433", "truncated": false, "display_text_range": [0, 243], "entities": {"hashtags": [{"text": "Tenet", "indices": [82, 88]}], "symbols": [], "user_mentions": [], "urls": [], "media": [{"id": 1272262640753094656, "id_str": "1272262640753094656", "indices": [244, 267], "media_url": "http://pbs.twimg.com/media/Eaf8IYsWsAAHVHV.jpg"}]}}]
[{"created_at": "Sun Jun 14 20:20:28 +0000 2020", "id": 1272262651100434433, "id_str": "1272262651100434433", "truncated": false, "display_text_range": [0, 243], "entities": {"hashtags": [{"text": "Tenet", "indices": [82, 88]}], "symbols": [], "user_mentions": [], "urls": [], "media": [{"id": 1272262640753094656, "id_str": "1272262640753094656", "indices": [244, 267], "media_url": "http://pbs.twimg.com/media/Eaf8IYsWsAAHVHV.jpg"}]}}]
[{"created_at": "Sun Jun 14 20:20:28 +0000 2020", "id": 1272262651100434433, "id_str": "1272262651100434433", "truncated": false, "display_text_range": [0, 243], "entities": {"hashtags": [{"text": "Tenet", "indices": [82, 88]}], "symbols": [], "user_mentions": [], "urls": [], "media": [{"id": 1272262640753094656, "id_str": "1272262640753094656", "indices": [244, 267], "media_url": "http://pbs.twimg.com/media/Eaf8IYsWsAAHVHV.jpg"}]}}]
[{"created_at": "Sun Jun 14 20:20:28 +0000 2020", "id": 1272262651100434433, "id_str": "1272262651100434433", "truncated": false, "display_text_range": [0, 243], "entities": {"hashtags": [{"text": "Tenet", "indices": [82, 88]}], "symbols": [], "user_mentions": [], "urls": [], "media": [{"id": 1272262640753094656, "id_str": "1272262640753094656", "indices": [244, 267], "media_url": "http://pbs.twimg.com/media/Eaf8IYsWsAAHVHV.jpg"}]}}]
[{"created_at": "Sun Jun 14 20:20:28 +0000 2020", "id": 1272262651100434433, "id_str": "1272262651100434433", "truncated": false, "display_text_range": [0, 243], "entities": {"hashtags": [{"text": "Tenet", "indices": [82, 88]}], "symbols": [], "user_mentions": [], "urls": [], "media": [{"id": 1272262640753094656, "id_str": "1272262640753094656", "indices": [244, 267], "media_url": "http://pbs.twimg.com/media/Eaf8IYsWsAAHVHV.jpg"}]}}]

                       created_at                   id               id_str  truncated display_text_range                                           entities
0  Sun Jun 14 20:20:28 +0000 2020  1272262651100434433  1272262651100434433      False           [0, 243]  {'hashtags': [{'text': 'Tenet', 'indices': [82...
1  Sun Jun 14 20:20:28 +0000 2020  1272262651100434433  1272262651100434433      False           [0, 243]  {'hashtags': [{'text': 'Tenet', 'indices': [82...
2  Sun Jun 14 20:20:28 +0000 2020  1272262651100434433  1272262651100434433      False           [0, 243]  {'hashtags': [{'text': 'Tenet', 'indices': [82...
3  Sun Jun 14 20:20:28 +0000 2020  1272262651100434433  1272262651100434433      False           [0, 243]  {'hashtags': [{'text': 'Tenet', 'indices': [82...
4  Sun Jun 14 20:20:28 +0000 2020  1272262651100434433  1272262651100434433      False           [0, 243]  {'hashtags': [{'text': 'Tenet', 'indices': [82...

>>df.info（）
范围索引：5个条目，0到4
数据列（共6列）：
#列非空计数数据类型
---  ------              --------------  -----
0在5个非空对象上创建了\u
1 id 5非空int64
2 id_str 5非空对象
3个截断的5个非空布尔
4显示\u文本\u范围5非空对象
5个实体5个非空对象
数据类型：bool（1）、int64（1）、object（4）
内存使用：333.0+字节

这取决于词典的结构、列表中的每个元素是否具有相同的结构以及有多少嵌套词典。提供更全面的示例数据谢谢，我在帖子中提供了一个示例数据。@Kapocsi你说得对。我已经编辑了这篇文章。谢谢你，先生。这是一段神奇而简单的代码，无需解包列表并将其转换为数据帧。