我是Python和Pandas的新手.我正在尝试将Pandas Dataframe转换为嵌套的JSON.函数.to_json()没有给我足够的灵活性来实现我的目标.
以下是数据帧的一些数据点(以csv,逗号分隔):
,ID,Location,Country,Latitude,Longitude,timestamp,tide
0,1,BREST,FRA,48.383,-4.495,1807-01-01,6905.0
1,1,BREST,FRA,48.383,-4.495,1807-02-01,6931.0
2,1,BREST,FRA,48.383,-4.495,1807-03-01,6896.0
3,1,BREST,FRA,48.383,-4.495,1807-04-01,6953.0
4,1,BREST,FRA,48.383,-4.495,1807-05-01,7043.0
2508,7,CUXHAVEN 2,DEU,53.867,8.717,1843-01-01,7093.0
2509,7,CUXHAVEN 2,DEU,53.867,8.717,1843-02-01,6688.0
2510,7,CUXHAVEN 2,DEU,53.867,8.717,1843-03-01,6493.0
2511,7,CUXHAVEN 2,DEU,53.867,8.717,1843-04-01,6723.0
2512,7,CUXHAVEN 2,DEU,53.867,8.717,1843-05-01,6533.0
4525,9,MAASSLUIS,NLD,51.918,4.25,1848-02-01,6880.0
4526,9,MAASSLUIS,NLD,51.918,4.25,1848-03-01,6700.0
4527,9,MAASSLUIS,NLD,51.918,4.25,1848-04-01,6775.0
4528,9,MAASSLUIS,NLD,51.918,4.25,1848-05-01,6580.0
4529,9,MAASSLUIS,NLD,51.918,4.25,1848-06-01,6685.0
6540,8,WISMAR 2,DEU,53.898999999999994,11.458,1848-07-01,6957.0
6541,8,WISMAR 2,DEU,53.898999999999994,11.458,1848-08-01,6944.0
6542,8,WISMAR 2,DEU,53.898999999999994,11.458,1848-09-01,7084.0
6543,8,WISMAR 2,DEU,53.898999999999994,11.458,1848-10-01,6898.0
6544,8,WISMAR 2,DEU,53.898999999999994,11.458,1848-11-01,6859.0
8538,10,SAN FRANCISCO,USA,37.806999999999995,-122.465,1854-07-01,6909.0
8539,10,SAN FRANCISCO,USA,37.806999999999995,-122.465,1854-08-01,6940.0
8540,10,SAN FRANCISCO,USA,37.806999999999995,-122.465,1854-09-01,6961.0
8541,10,SAN FRANCISCO,USA,37.806999999999995,-122.465,1854-10-01,6952.0
8542,10,SAN FRANCISCO,USA,37.806999999999995,-122.465,1854-11-01,6952.0
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有很多重复的信息,我想有这样的JSON:
[
{
"ID": 1,
"Location": "BREST",
"Latitude": 48.383,
"Longitude": -4.495,
"Country": "FRA",
"Tide-Data": {
"1807-02-01": 6931,
"1807-03-01": 6896,
"1807-04-01": 6953,
"1807-05-01": 7043
}
},
{
"ID": 5,
"Location": "HOLYHEAD",
"Latitude": 53.31399999999999,
"Longitude": -4.62,
"Country": "GBR", …Run Code Online (Sandbox Code Playgroud) 我有一个熊猫多索引数据框,我想将其输出为嵌套字典。
# create the dataset
data = {'clump_thickness': {(0, 0): 274.0, (0, 1): 19.0, (1, 0): 67.0, (1, 1): 12.0, (2, 0): 83.0, (2, 1): 45.0, (3, 0): 16.0, (3, 1): 40.0, (4, 0): 4.0, (4, 1): 54.0, (5, 0): 0.0, (5, 1): 69.0, (6, 0): 0.0, (6, 1): 0.0, (7, 0): 0.0, (7, 1): 0.0, (8, 0): 0.0, (8, 1): 0.0, (9, 0): 0.0, (9, 1): 0.0}}
df = pd.DataFrame(data)
df.head()
# clump_thickness
# 0 0 274.0
# 1 …Run Code Online (Sandbox Code Playgroud)