将包含多行 JSON 的文件加载到 Pandas 中

Question

我正在尝试将 JSON 文件读入 Python pandas (0.14.0) 数据框。这是 JSON 文件的第一行：

{"votes": {"funny": 0, "useful": 0, "cool": 0}, "user_id": "P_Mk0ygOilLJo4_WEvabAA", "review_id": "OeT5kgUOe3vcN7H6ImVmZQ", "stars": 3, "date": "2005-08-26", "text": "This is a pretty typical cafe.  The sandwiches and wraps are good but a little overpriced and the food items are the same.  The chicken caesar salad wrap is my favorite here but everything else is pretty much par for the course.", "type": "review", "business_id": "Jp9svt7sRT4zwdbzQ8KQmw"}

我正在尝试执行以下操作：df = pd.read_json(path)。

我收到以下错误（带有完整回溯）：

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/Users/d/anaconda/lib/python2.7/site-packages/pandas/io/json.py", line 198, in read_json
    date_unit).parse()
  File "/Users/d/anaconda/lib/python2.7/site-packages/pandas/io/json.py", line 266, in parse
    self._parse_no_numpy()
  File "/Users/d/anaconda/lib/python2.7/site-packages/pandas/io/json.py", line 483, in _parse_no_numpy
    loads(json, precise_float=self.precise_float), dtype=None)
ValueError: Trailing data

Trailing data 错误是什么？如何将其读入数据框？

根据一些建议，以下是 .json 文件的几行：

{"votes": {"funny": 0, "useful": 0, "cool": 0}, "user_id": "P_Mk0ygOilLJo4_WEvabAA", "review_id": "OeT5kgUOe3vcN7H6ImVmZQ", "stars": 3, "date": "2005-08-26", "text": "This is a pretty typical cafe.  The sandwiches and wraps are good but a little overpriced and the food items are the same.  The chicken caesar salad wrap is my favorite here but everything else is pretty much par for the course.", "type": "review", "business_id": "Jp9svt7sRT4zwdbzQ8KQmw"}
{"votes": {"funny": 0, "useful": 0, "cool": 0}, "user_id": "TNJRTBrl0yjtpAACr1Bthg", "review_id": "qq3zF2dDUh3EjMDuKBqhEA", "stars": 3, "date": "2005-11-23", "text": "I agree with other reviewers - this is a pretty typical financial district cafe.  However, they have fantastic pies.  I ordered three pies for an office event (apple, pumpkin cheesecake, and pecan) - all were delicious, particularly the cheesecake.  The sucker weighed in about 4 pounds - no joke.\n\nNo surprises on the cafe side - great pies and cakes from the catering business.", "type": "review", "business_id": "Jp9svt7sRT4zwdbzQ8KQmw"}
{"votes": {"funny": 0, "useful": 0, "cool": 0}, "user_id": "H_mngeK3DmjlOu595zZMsA", "review_id": "i3eQTINJXe3WUmyIpvhE9w", "stars": 3, "date": "2005-11-23", "text": "Decent enough food, but very overpriced. Just a large soup is almost $5. Their specials are $6.50, and with an overpriced soda or juice, it's approaching $10. A bit much for a cafe lunch!", "type": "review", "business_id": "Jp9svt7sRT4zwdbzQ8KQmw"}

我正在使用的这个 .json 文件根据规范在每一行中包含一个 JSON 对象。

我按照建议尝试了jsonlint.com 网站，但出现以下错误：

Parse error on line 14:
...t7sRT4zwdbzQ8KQmw"}{    "votes": {
----------------------^
Expecting 'EOF', '}', ',', ']'

Answer 1

我也遇到了同样的问题。当您的数据被写入以 '\n' 之类的结尾分隔的行时，就会发生这种情况；您需要先逐行读取它们，然后将每一行转换为 python 内置类型。 我是这样解决的：

with open("/path/to/file") as f:
    content = f.readlines()

data = [eval(c) for c in content]
data = pd.DataFrame(data)

祝你好运！

Answer 2

以下代码帮助我将JSON 内容加载到dataframe：

import json
import pandas as pd

with open('Appointment.json', encoding="utf8") as f:
    data = f.readlines()
    data = [json.loads(line) for line in data] #convert string to dict format
df = pd.read_json(data) # Load into dataframe

Answer 3

我也遇到过类似的问题。

原来pd.read_json(myfile.json) 会自动在父文件夹中搜索，但如果您与文件不在同一个文件夹中，则会返回此“尾随数据”错误。

我想通了，因为当我尝试使用 open('myfile.json', 'r') 执行此操作时，我收到了 FileNotFound 错误，所以我检查了路径。

我未能将 myfile.json 移动到与我的笔记本相同的文件夹中。

将其更改为 pd.read_json('../myfile.json') 即可。

Answer 4

从 Pandas 0.19.0 版本开始，您可以使用 lines 参数，如下所示：

import pandas as pd

data = pd.read_json('/path/to/file.json', lines=True)

Answer 5

你必须逐行阅读。例如，您可以在reddit上使用ryptophan提供的以下代码：

import pandas as pd

# read the entire file into a python array
with open('your.json', 'rb') as f:
    data = f.readlines()

# remove the trailing "\n" from each line
data = map(lambda x: x.rstrip(), data)

# each element of 'data' is an individual JSON object.
# i want to convert it into an *array* of JSON objects
# which, in and of itself, is one large JSON object
# basically... add square brackets to the beginning
# and end, and have all the individual business JSON objects
# separated by a comma
data_json_str = "[" + ','.join(data) + "]"

# now, load it into pandas
data_df = pd.read_json(data_json_str)