我想用一个有点过于复杂的数据集来实现机器学习。我想使用 Pandas,然后使用 skit-learn 中的一些内置模型。
JSON 文件中给出了数据外观,示例如下所示:
{
"demo_Profile": {
"sex": "male",
"age": 98,
"height": 160,
"weight": 139,
"bmi": 5,
"someinfo1": [
"some_more_info1"
],
"someinfo2": [
"some_more_inf2"
],
"someinfo3": [
"some_more_info3"
],
},
"event": {
"info_personal": {
"info1": 219.59,
"info2": 129.18,
"info3": 41.15,
"info4": 94.19,
},
"symptoms": [
{
"name": "name1",
"socrates": {
"associations": [
"associations1"
],
"onsetType": "onsetType1",
"timeCourse": "timeCourse1"
}
},
{
"name": "name2",
"socrates": {
"timeCourse": "timeCourse2"
}
},
{
"name": "name3",
"socrates": {
"onsetType": "onsetType2"
}
},
{
"name": "name4",
"socrates": {
"onsetType": "onsetType3"
}
},
{
"name": "name5",
"socrates": {
"associations": [
"associations2"
]
}
}
],
"labs": [
{
"name": "name1 ",
"value": "valuelab"
}
]
}
}
我想创建一个考虑这种“嵌套数据”的熊猫数据框,但我不知道如何构建一个除了“单个参数”之外还考虑“嵌套参数”的数据框
例如,我不知道如何将包含“单个参数”的“demo_Profile”与症状合并,症状是字典列表,在相同情况下为单个值,在其他情况下为列表。
有人知道处理这个问题的任何方法吗?
编辑*********
上面显示的 JSON 只是一个示例,在其他情况下,列表中的值数量以及症状数量会有所不同。因此,上面显示的示例并非适用于所有情况。
扁平化 json 数据的一种快速简便的方法是使用 flatten_json 包,该包可以通过 pip 安装
pip install flatten_json
我希望您有一个包含许多条目的列表,这些条目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,列表条目以数字为后缀(我在“实验室”列表中添加了另一名患者,并添加了一个附加条目):
+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| 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 方法包含附加参数以删除不需要的列或前缀。
注意:虽然此方法可以根据需要为您提供扁平化的 DataFrame,但我希望您在将数据集输入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是什么以及您希望如何将数据编码为特征.
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