我想用有点太复杂的数据集来实现机器学习。我想和熊猫一起工作，然后在快速学习中使用一些内置模型。
数据外观在JSON文件中给出，示例如下所示：
lrModel.elasticNetParam我想创建一个考虑这种“嵌套数据”的熊猫数据框，但是我不知道如何构建一个除“单个参数”之外还要考虑“嵌套参数”的数据框
例如，我不知道如何合并包含“单个参数”和症状的“ demo_Profile”，该症状是字典的列表，在某些情况下为单个值，在其他情况下为列表。
有人知道解决这个问题的方法吗？
编辑*********
上面显示的JSON只是一个示例，在其他情况下，列表中值的数量和症状的数量也会有所不同。因此，上面显示的示例并非在每种情况下都是固定的。
答案 0 :(得分：2)
考虑熊猫的json_normalize。但是，由于存在更深的嵌套，请考虑分别处理数据，然后在“归一化”列上进行填充并进行合并。
import json
import pandas as pd
from pandas.io.json import json_normalize
with open('myfile.json', 'r') as f:
data = json.loads(f.read())
final_df = pd.concat([json_normalize(data['demo_Profile']),
json_normalize(data['event']['symptoms']),
json_normalize(data['event']['info_personal']),
json_normalize(data['event']['labs'])], axis=1)
# FLATTEN NESTED LISTS
n_list = ['someinfo1', 'someinfo2', 'someinfo3', 'socrates.associations']
final_df[n_list] = final_df[n_list].apply(lambda col:
col.apply(lambda x: x if pd.isnull(x) else x[0]))
# FILLING FORWARD
norm_list = ['age', 'bmi', 'height', 'weight', 'sex', 'someinfo1', 'someinfo2', 'someinfo3',
'info1', 'info2', 'info3', 'info4', 'name', 'value']
final_df[norm_list] = final_df[norm_list].ffill()
输出
print(final_df)
# age bmi height sex someinfo1 someinfo2 someinfo3 weight name socrates.associations socrates.onsetType socrates.timeCourse info1 info2 info3 info4 name value
# 0 98.0 5.0 160.0 male some_more_info1 some_more_inf2 some_more_info3 139.0 name1 associations1 onsetType1 timeCourse1 219.59 129.18 41.15 94.19 name1 valuelab
# 1 98.0 5.0 160.0 male some_more_info1 some_more_inf2 some_more_info3 139.0 name2 NaN NaN timeCourse2 219.59 129.18 41.15 94.19 name1 valuelab
# 2 98.0 5.0 160.0 male some_more_info1 some_more_inf2 some_more_info3 139.0 name3 NaN onsetType2 NaN 219.59 129.18 41.15 94.19 name1 valuelab
# 3 98.0 5.0 160.0 male some_more_info1 some_more_inf2 some_more_info3 139.0 name4 NaN onsetType3 NaN 219.59 129.18 41.15 94.19 name1 valuelab
# 4 98.0 5.0 160.0 male some_more_info1 some_more_inf2 some_more_info3 139.0 name5 associations2 NaN NaN 219.59 129.18 41.15 94.19 name1 valuelab
答案 1 :(得分：1)
平整json数据的一种快速简便的方法是使用可通过pip安装的flatten_json包
pip install flatten_json
我希望您有许多条目的列表，看起来像您提供的条目。因此，以下代码将为您提供所需的结果：
import pandas as pd
from flatten_json import flatten
json_data = [{...patient1...}, {patient2...}, ...]
flattened = (flatten(entry) for entry in json_data)
df = pd.DataFrame(flattened)
在扁平化的数据中，列表条目带有数字后缀（我在“实验室”列表中添加了另一名患者，并带有附加条目）：
+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| index demo_Profile_age demo_Profile_bmi demo_Profile_height demo_Profile_sex demo_Profile_someinfo1_0 demo_Profile_someinfo2_0 demo_Profile_someinfo3_0 demo_Profile_weight event_info_personal_info1 event_info_personal_info2 event_info_personal_info3 event_info_personal_info4 event_labs_0_name event_labs_0_value event_labs_1_name event_labs_1_value event_symptoms_0_name event_symptoms_0_socrates_associations_0 event_symptoms_0_socrates_onsetType event_symptoms_0_socrates_timeCourse event_symptoms_1_name event_symptoms_1_socrates_timeCourse event_symptoms_2_name event_symptoms_2_socrates_onsetType event_symptoms_3_name event_symptoms_3_socrates_onsetType event_symptoms_4_name event_symptoms_4_socrates_associations_0 |
+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| 0 98 5 160 male some_more_info1 some_more_inf2 some_more_info3 139 219.59 129.18 41.15 94.19 name1 valuelab NaN NaN name1 associations1 onsetType1 timeCourse1 name2 timeCourse2 name3 onsetType2 name4 onsetType3 name5 associations2 |
| 1 98 5 160 male some_more_info1 some_more_inf2 some_more_info3 139 219.59 129.18 41.15 94.19 name1 valuelab name2 valuelabr2 name1 associations1 onsetType1 timeCourse1 name2 timeCourse2 name3 onsetType2 name4 onsetType3 name5 associations2 |
+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
flatten方法包含其他参数，以删除不需要的列或前缀。
注意：虽然此方法可为您提供所需的扁平化DataFrame，但我希望您在将数据集输入到机器学习算法时会遇到其他问题，具体取决于您的预测目标和编码方式。数据作为特征。