我认为，以产生重复列名的方式组织数据只会在以后给您带来麻烦。更好的方法是恕我直言创造每一个列pivots，interval_id和p_value。将数据加载到熊猫中后，这将使查询数据变得非常容易。
jq 帮助这里
import sh
jq = sh.jq.bake('-M') # disable colorizing
json_data = "from above"
rule = """[{pivots: .intervals[].pivots,
interval_id: .intervals[].series[].interval_id,
p_value: .intervals[].series[].p_value}]"""
out = jq(rule, _in=json_data).stdout
res = pd.DataFrame(json.loads(out))
interval_id p_value pivots
32 2 2.867501e-06 Jane Smith
33 2 1.000000e+00 Jane Smith
34 2 1.116279e-08 Jane Smith
35 2 2.867501e-06 Jane Smith
36 0 1.000000e+00 Bob Smith
37 0 1.116279e-08 Bob Smith
38 0 2.867501e-06 Bob Smith
39 0 1.000000e+00 Bob Smith
40 0 1.116279e-08 Bob Smith
41 0 2.867501e-06 Bob Smith
42 1 1.000000e+00 Bob Smith
43 1 1.116279e-08 Bob Smith
改编自此评论
当然，您始终可以调用res.drop_duplicates()以删除重复的行。这给
In [175]: res.drop_duplicates()
Out[175]:
interval_id p_value pivots
0 0 1.000000e+00 Jane Smith
1 0 1.116279e-08 Jane Smith
2 0 2.867501e-06 Jane Smith
6 1 1.000000e+00 Jane Smith
7 1 1.116279e-08 Jane Smith
8 1 2.867501e-06 Jane Smith
12 2 1.000000e+00 Jane Smith
13 2 1.116279e-08 Jane Smith
14 2 2.867501e-06 Jane Smith
36 0 1.000000e+00 Bob Smith
37 0 1.116279e-08 Bob Smith
38 0 2.867501e-06 Bob Smith
42 1 1.000000e+00 Bob Smith
43 1 1.116279e-08 Bob Smith
44 1 2.867501e-06 Bob Smith
48 2 1.000000e+00 Bob Smith
49 2 1.116279e-08 Bob Smith
50 2 2.867501e-06 Bob Smith
[18 rows x 3 columns]
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