更新：
j = (df.groupby(['ID','Location','Country','Latitude','Longitude'], as_index=False) .apply(lambda x: x[['timestamp','tide']].to_dict('r')) .reset_index() .rename(columns={0:'Tide-Data'}) .to_json(orient='records'))结果（格式化）：
In [103]: print(json.dumps(json.loads(j), indent=2, sort_keys=True))[ { "Country": "FRA", "ID": 1, "Latitude": 48.383, "Location": "BREST", "Longitude": -4.495, "Tide-Data": [ { "tide": 6905.0, "timestamp": "1807-01-01" }, { "tide": 6931.0, "timestamp": "1807-02-01" }, { "tide": 6896.0, "timestamp": "1807-03-01" }, { "tide": 6953.0, "timestamp": "1807-04-01" }, { "tide": 7043.0, "timestamp": "1807-05-01" } ] }, { "Country": "DEU", "ID": 7, "Latitude": 53.867, "Location": "CUXHAVEN 2", "Longitude": 8.717, "Tide-Data": [ { "tide": 7093.0, "timestamp": "1843-01-01" }, { "tide": 6688.0, "timestamp": "1843-02-01" }, { "tide": 6493.0, "timestamp": "1843-03-01" }, { "tide": 6723.0, "timestamp": "1843-04-01" }, { "tide": 6533.0, "timestamp": "1843-05-01" } ] }, { "Country": "DEU", "ID": 8, "Latitude": 53.899, "Location": "WISMAR 2", "Longitude": 11.458, "Tide-Data": [ { "tide": 6957.0, "timestamp": "1848-07-01" }, { "tide": 6944.0, "timestamp": "1848-08-01" }, { "tide": 7084.0, "timestamp": "1848-09-01" }, { "tide": 6898.0, "timestamp": "1848-10-01" }, { "tide": 6859.0, "timestamp": "1848-11-01" } ] }, { "Country": "NLD", "ID": 9, "Latitude": 51.918, "Location": "MAASSLUIS", "Longitude": 4.25, "Tide-Data": [ { "tide": 6880.0, "timestamp": "1848-02-01" }, { "tide": 6700.0, "timestamp": "1848-03-01" }, { "tide": 6775.0, "timestamp": "1848-04-01" }, { "tide": 6580.0, "timestamp": "1848-05-01" }, { "tide": 6685.0, "timestamp": "1848-06-01" } ] }, { "Country": "USA", "ID": 10, "Latitude": 37.807, "Location": "SAN FRANCISCO", "Longitude": -122.465, "Tide-Data": [ { "tide": 6909.0, "timestamp": "1854-07-01" }, { "tide": 6940.0, "timestamp": "1854-08-01" }, { "tide": 6961.0, "timestamp": "1854-09-01" }, { "tide": 6952.0, "timestamp": "1854-10-01" }, { "tide": 6952.0, "timestamp": "1854-11-01" } ] }]旧答案：
你可以用它做的
groupby()，
apply()和
to_json()方法：
j = (df.groupby(['ID','Location','Country','Latitude','Longitude'], as_index=False) .apply(lambda x: dict(zip(x.timestamp,x.tide))) .reset_index() .rename(columns={0:'Tide-Data'}) .to_json(orient='records'))输出：
In [112]: print(json.dumps(json.loads(j), indent=2, sort_keys=True))[ { "Country": "FRA", "ID": 1, "Latitude": 48.383, "Location": "BREST", "Longitude": -4.495, "Tide-Data": { "1807-01-01": 6905.0, "1807-02-01": 6931.0, "1807-03-01": 6896.0, "1807-04-01": 6953.0, "1807-05-01": 7043.0 } }, { "Country": "DEU", "ID": 7, "Latitude": 53.867, "Location": "CUXHAVEN 2", "Longitude": 8.717, "Tide-Data": { "1843-01-01": 7093.0, "1843-02-01": 6688.0, "1843-03-01": 6493.0, "1843-04-01": 6723.0, "1843-05-01": 6533.0 } }, { "Country": "DEU", "ID": 8, "Latitude": 53.899, "Location": "WISMAR 2", "Longitude": 11.458, "Tide-Data": { "1848-07-01": 6957.0, "1848-08-01": 6944.0, "1848-09-01": 7084.0, "1848-10-01": 6898.0, "1848-11-01": 6859.0 } }, { "Country": "NLD", "ID": 9, "Latitude": 51.918, "Location": "MAASSLUIS", "Longitude": 4.25, "Tide-Data": { "1848-02-01": 6880.0, "1848-03-01": 6700.0, "1848-04-01": 6775.0, "1848-05-01": 6580.0, "1848-06-01": 6685.0 } }, { "Country": "USA", "ID": 10, "Latitude": 37.807, "Location": "SAN FRANCISCO", "Longitude": -122.465, "Tide-Data": { "1854-07-01": 6909.0, "1854-08-01": 6940.0, "1854-09-01": 6961.0, "1854-10-01": 6952.0, "1854-11-01": 6952.0 } }]PS，如果您不关心标识，则可以直接写入JSON文件：
(df.groupby(['ID','Location','Country','Latitude','Longitude'], as_index=False) .apply(lambda x: dict(zip(x.timestamp,x.tide))) .reset_index() .rename(columns={0:'Tide-Data'}) .to_json('/path/to/file_name.json', orient='records'))
