我正在尝试使用 pandas 读取 JSON 文件：
import pandas as pd
df = pd.read_json('https://data.gov.in/node/305681/datastore/export/json')
我收到ValueError: arrays must all be same length
其他一些 JSON 页面显示此错误：
ValueError: Mixing dicts with non-Series may lead to ambiguous ordering.
我如何以某种方式读取这些值？我并不特别关注数据有效性。
【问题讨论】：
查看 json 是有效的，但它嵌套了数据和字段：
import json
import requests
In [11]: d = json.loads(requests.get('https://data.gov.in/node/305681/datastore/export/json').text)
In [12]: list(d.keys())
Out[12]: ['data', 'fields']
您希望数据作为内容，字段作为列名：
In [13]: pd.DataFrame(d["data"], columns=[x["label"] for x in d["fields"]])
Out[13]:
S. No. States/UTs 2008-09 2009-10 2010-11 2011-12 2012-13
0 1 Andhra Pradesh 183446.36 193958.45 201277.09 212103.27 222973.83
1 2 Arunachal Pradesh 360.5 380.15 407.42 419 438.69
2 3 Assam 4658.93 4671.22 4707.31 4705 4709.58
3 4 Bihar 10740.43 11001.77 7446.08 7552 8371.86
4 5 Chhattisgarh 9737.92 10520.01 12454.34 12984.44 13704.06
5 6 Goa 148.61 148 149 149.45 457.87
6 7 Gujarat 12675.35 12761.98 13269.23 14269.19 14558.39
7 8 Haryana 38149.81 38453.06 39644.17 41141.91 42342.66
8 9 Himachal Pradesh 977.3 1000.26 1020.62 1049.66 1069.39
9 10 Jammu and Kashmir 7208.26 7242.01 7725.19 6519.8 6715.41
10 11 Jharkhand 3994.77 3924.73 4153.16 4313.22 4238.95
11 12 Karnataka 23687.61 29094.3 30674.18 34698.77 36773.33
12 13 Kerala 15094.54 16329.52 16856.02 17048.89 22375.28
13 14 Madhya Pradesh 6712.6 7075.48 7577.23 7971.53 8710.78
14 15 Maharashtra 35502.28 38640.12 42245.1 43860.99 45661.07
15 16 Manipur 1105.25 1119 1137.05 1149.17 1162.19
16 17 Meghalaya 994.52 999.47 1010.77 1021.14 1028.18
17 18 Mizoram 411.14 370.92 387.32 349.33 352.02
18 19 Nagaland 831.92 833.5 802.03 703.65 617.98
19 20 Odisha 19940.15 23193.01 23570.78 23006.87 23229.84
20 21 Punjab 36789.7 32828.13 35449.01 36030 37911.01
21 22 Rajasthan 6449.17 6713.38 6696.92 9605.43 10334.9
22 23 Sikkim 136.51 136.07 139.83 146.24 146
23 24 Tamil Nadu 88097.59 108475.73 115137.14 118518.45 119333.55
24 25 Tripura 1388.41 1442.39 1569.45 1650 1565.17
25 26 Uttar Pradesh 10139.8 10596.17 10990.72 16075.42 17073.67
26 27 Uttarakhand 1961.81 2535.77 2613.81 2711.96 3079.14
27 28 West Bengal 33055.7 36977.96 39939.32 43432.71 47114.91
28 29 Andaman and Nicobar Islands 617.58 657.44 671.78 780 741.32
29 30 Chandigarh 272.88 248.53 180.06 180.56 170.27
30 31 Dadra and Nagar Haveli 70.66 70.71 70.28 73 73
31 32 Daman and Diu 18.83 18.9 18.81 19.67 20
32 33 Delhi 1.17 1.17 1.17 1.23 NA
33 34 Lakshadweep 134.64 138.22 137.98 139.86 139.99
34 35 Puducherry 111.69 112.84 113.53 116 112.89
另请参阅json_normalize 了解更复杂的 json DataFrame 提取。
【讨论】：
以下为我列出了键值对：
from urllib.request import urlopen
import json
from pandas.io.json import json_normalize
import pandas as pd
import requests
df = json.loads(requests.get('https://api.github.com/repos/akkhil2012/MachineLearning').text)
data = pd.DataFrame.from_dict(df, orient='index')
print(data)
【讨论】：
MachineLearning.json?