我想用JSON接收天气预报数据。
Rep中的'Period' {'SiteRep': {'DV': {'type': 'Obs',
'Location': [{'i': '3002',
'lat': '60.749',
'lon': '-0.854',
'name': 'BALTASOUND',
'Period': [{'Rep': {'$': '1380',
'D': 'SW',
'G': '34',
'H': '79.5',
'P': '1019',
'S': '25',
'T': '7.9',
'V': '13000',
'W': '8',
'Dp': '4.6',
'Pt': 'F'},
'type': 'Day',
'value': '2019-12-31Z'},
{'Rep': [{'$': '0',
'D': 'SW',
'G': '32',
'H': '84.0',
'P': '1018',
'S': '21',
'T': '7.5',
'V': '13000',
'W': '8',
'Dp': '5.0',
'Pt': 'F'},
{'$': '60',
'D': 'SW',
'G': '34',
'H': '81.7',
'P': '1018',
'S': '22',
'T': '7.5',
'V': '12000',
'W': '8',
'Dp': '4.6',
'Pt': 'F'},
{'$': '120',
'D': 'SW',
'G': '36',
'H': '79.9',
'P': '1017',
'S': '24',
'T': '7.9',
'V': '11000',
'W': '8',
'Dp': '4.7',
'Pt': 'F'},
{'$': '180',
'D': 'SW',
'G': '40',
'H': '82.3',
'P': '1016',
'S': '23',
'T': '7.5',
'V': '13000',
'W': '8',
'Dp': '4.7',
'Pt': 'F'},
{'$': '240',
'D': 'SW',
'G': '33',
'H': '84.6',
'P': '1015',
'S': '18',
'T': '8.0',
'V': '12000',
'W': '8',
'Dp': '5.6',
'Pt': 'F'},
{'$': '300',
'D': 'SW',
'G': '33',
'H': '85.3',
'P': '1015',
'S': '24',
'T': '8.3',
'V': '11000',
'W': '8',
'Dp': '6.0',
'Pt': 'F'},
{'$': '360',
'D': 'WSW',
'G': '41',
'H': '89.0',
'P': '1014',
'S': '30',
'T': '8.5',
'V': '8000',
'W': '8',
'Dp': '6.8',
'Pt': 'F'},
{'$': '420',
'D': 'SW',
'G': '43',
'H': '89.6',
'P': '1013',
'S': '28',
'T': '8.7',
'V': '7000',
'W': '7',
'Dp': '7.1',
'Pt': 'F'},
{'$': '480',
'D': 'SW',
'G': '39',
'H': '88.4',
'P': '1013',
'S': '23',
'T': '8.7',
'V': '15000',
'W': '7',
'Dp': '6.9',
'Pt': 'F'},
{'$': '540',
'D': 'SW',
'G': '40',
'H': '84.3',
'P': '1013',
'S': '29',
'T': '9.1',
'V': '19000',
'W': '8',
'Dp': '6.6',
'Pt': 'F'},
{'$': '600',
'D': 'SW',
'G': '41',
'H': '85.4',
'P': '1012',
'S': '24',
'T': '8.9',
'V': '12000',
'W': '8',
'Dp': '6.6',
'Pt': 'F'},
{'$': '660',
'D': 'SW',
'G': '38',
'H': '84.2',
'P': '1012',
'S': '28',
'T': '9.2',
'V': '13000',
'W': '8',
'Dp': '6.7',
'Pt': 'F'},
{'$': '720',
'D': 'SW',
'G': '47',
'H': '83.6',
'P': '1011',
'S': '32',
'T': '9.4',
'V': '12000',
'W': '8',
'Dp': '6.8',
'Pt': 'F'},
{'$': '780',
'D': 'WSW',
'G': '45',
'H': '84.8',
'P': '1011',
'S': '30',
'T': '9.4',
'V': '11000',
'W': '8',
'Dp': '7.0',
'Pt': 'F'},
{'$': '840',
'D': 'SW',
'G': '43',
'H': '86.0',
'P': '1010',
'S': '28',
'T': '9.4',
'V': '11000',
'W': '7',
'Dp': '7.2',
'Pt': 'F'},
{'$': '900',
'D': 'WSW',
'G': '40',
'H': '85.4',
'P': '1009',
'S': '29',
'T': '9.4',
'V': '12000',
'W': '8',
'Dp': '7.1',
'Pt': 'F'},
{'$': '960',
'D': 'SW',
'G': '39',
'H': '86.0',
'P': '1009',
'S': '25',
'T': '9.2',
'V': '11000',
'W': '8',
'Dp': '7.0',
'Pt': 'F'},
{'$': '1020',
'D': 'SW',
'G': '33',
'H': '87.8',
'P': '1009',
'S': '23',
'T': '8.9',
'V': '11000',
'W': '8',
'Dp': '7.0',
'Pt': 'F'},
{'$': '1080',
'D': 'SW',
'G': '36',
'H': '85.5',
'P': '1008',
'S': '23',
'T': '8.9',
'V': '11000',
'W': '8',
'Dp': '6.6',
'Pt': 'F'},
{'$': '1140',
'D': 'SW',
'G': '40',
'H': '86.6',
'P': '1007',
'S': '28',
'T': '8.8',
'V': '14000',
'W': '8',
'Dp': '6.7',
'Pt': 'F'},
{'$': '1200',
'D': 'SSW',
'G': '39',
'H': '84.8',
'P': '1006',
'S': '28',
'T': '8.8',
'V': '13000',
'W': '8',
'Dp': '6.4',
'Pt': 'F'},
{'$': '1260',
'D': 'SSW',
'G': '37',
'H': '87.7',
'P': '1005',
'S': '26',
'T': '8.0',
'V': '15000',
'W': '8',
'Dp': '6.1',
'Pt': 'F'},
{'$': '1320',
'D': 'S',
'G': '37',
'H': '88.4',
'P': '1003',
'S': '24',
'T': '8.0',
'V': '13000',
'W': '8',
'Dp': '6.2',
'Pt': 'F'},
{'$': '1380',
'D': 'S',
'G': '38',
'H': '89.6',
'P': '1002',
'S': '29',
'T': '7.6',
'V': '11000',
'W': '8',
'Dp': '6.0',
'Pt': 'F'}],
'type': 'Day',
'value': '2020-01-01Z'}]}]}}}
JSON的结构如下所示，其中每个期间都有两个报告：
SiteRep - DV - Location - Period (0) - Rep (0)
- Rep(1)
Period (1) - Rep (0)
- Rep(1)
所需的输出将是将“位置”，“期间”和“报告”值展平的表。
| i | lat | lon | name |country | continent| elevation| name |Rep(0)$| Rep(0)D|Rep(0)G|..
|---|-----|------|-------|--------|----------|----------|------|-------|--------|-------|..
| | | | | | | | | | | |
我设法弄平了位置
normalised_data = pd.json_normalize(df['observations'], record_path=['SiteRep','DV','Location'])
所以现在我的数据看起来像
i lat lon name Period country continent elevation
0 3002 60.749 -0.854 BALTASOUND [{'Rep': {'$': '1380', 'D': 'SW', 'G': '34', 'H': '79.5', 'P': '1019', 'S': '25', 'T': '7.9', 'V': '13000', 'W': '8', 'Dp': '4.6', 'Pt': 'F'}, 'type': 'Day', 'value': '2019-12-31Z'}, {'Rep': [{'$': '0', 'D': 'SW', 'G': '32', 'H': '84.0', 'P': '1018', 'S': '21', 'T': '7.5', 'V': '13000', 'W': '8', 'Dp': '5.0', 'Pt': 'F'}, {'$': '60', 'D': 'SW', 'G': '34', 'H': '81.7', 'P': '1018', 'S': '22', 'T': '7.5', 'V': '12000', 'W': '8', 'Dp': '4.6', 'Pt': 'F'}, {'$': '120', 'D': 'SW', 'G': '36', 'H': '79.9', 'P': '1017', 'S': '24', 'T': '7.9', 'V': '11000', 'W': '8', 'Dp': '4.7', 'Pt': 'F'}, {'$': '180', 'D': 'SW', 'G': '40', 'H': '82.3', 'P': '1016', 'S': '23', 'T': '7.5', 'V': '13000', 'W': '8', 'Dp': '4.7', 'Pt': 'F'}, {'$': '240', 'D': 'SW', 'G': '33', 'H': '84.6', 'P': '1015', 'S': '18', 'T': '8.0', 'V': '12000', 'W': '8', 'Dp': '5.6', 'Pt': 'F'}, {'$': '300', 'D': 'SW', 'G': '33', 'H': '85.3', 'P': '1015', 'S': '24', 'T': '8.3', 'V': '11000', 'W': '8', 'Dp': '6.0', 'Pt': 'F'}, {'$': '360', 'D': 'WSW', 'G': '41', 'H': '89.0', 'P': '1014', 'S': '30', 'T': '8.5', 'V': '8000', 'W': '8', 'Dp': '6.8', 'Pt': 'F'}, {'$': '420', 'D': 'SW', 'G': '43', 'H': '89.6', 'P': '1013', 'S': '28', 'T': '8.7', 'V': '7000', 'W': '7', 'Dp': '7.1', 'Pt': 'F'}, {'$': '480', 'D': 'SW', 'G': '39', 'H': '88.4', 'P': '1013', 'S': '23', 'T': '8.7', 'V': '15000', 'W': '7', 'Dp': '6.9', 'Pt': 'F'}, {'$': '540', 'D': 'SW', 'G': '40', 'H': '84.3', 'P': '1013', 'S': '29', 'T': '9.1', 'V': '19000', 'W': '8', 'Dp': '6.6', 'Pt': 'F'}, {'$': '600', 'D': 'SW', 'G': '41', 'H': '85.4', 'P': '1012', 'S': '24', 'T': '8.9', 'V': '12000', 'W': '8', 'Dp': '6.6', 'Pt': 'F'}, {'$': '660', 'D': 'SW', 'G': '38', 'H': '84.2', 'P': '1012', 'S': '28', 'T': '9.2', 'V': '13000', 'W': '8', 'Dp': '6.7', 'Pt': 'F'}, {'$': '720', 'D': 'SW', 'G': '47', 'H': '83.6', 'P': '1011', 'S': '32', 'T': '9.4', 'V': '12000', 'W': '8', 'Dp': '6.8', 'Pt': 'F'}, {'$': '780', 'D': 'WSW', 'G': '45', 'H': '84.8', 'P': '1011', 'S': '30', 'T': '9.4', 'V': '11000', 'W': '8', 'Dp': '7.0', 'Pt': 'F'}, {'$': '840', 'D': 'SW', 'G': '43', 'H': '86.0', 'P': '1010', 'S': '28', 'T': '9.4', 'V': '11000', 'W': '7', 'Dp': '7.2', 'Pt': 'F'}, {'$': '900', 'D': 'WSW', 'G': '40', 'H': '85.4', 'P': '1009', 'S': '29', 'T': '9.4', 'V': '12000', 'W': '8', 'Dp': '7.1', 'Pt': 'F'}, {'$': '960', 'D': 'SW', 'G': '39', 'H': '86.0', 'P': '1009', 'S': '25', 'T': '9.2', 'V': '11000', 'W': '8', 'Dp': '7.0', 'Pt': 'F'}, {'$': '1020', 'D': 'SW', 'G': '33', 'H': '87.8', 'P': '1009', 'S': '23', 'T': '8.9', 'V': '11000', 'W': '8', 'Dp': '7.0', 'Pt': 'F'}, {'$': '1080', 'D': 'SW', 'G': '36', 'H': '85.5', 'P': '1008', 'S': '23', 'T': '8.9', 'V': '11000', 'W': '8', 'Dp': '6.6', 'Pt': 'F'}, {'$': '1140', 'D': 'SW', 'G': '40', 'H': '86.6', 'P': '1007', 'S': '28', 'T': '8.8', 'V': '14000', 'W': '8', 'Dp': '6.7', 'Pt': 'F'}, {'$': '1200', 'D': 'SSW', 'G': '39', 'H': '84.8', 'P': '1006', 'S': '28', 'T': '8.8', 'V': '13000', 'W': '8', 'Dp': '6.4', 'Pt': 'F'}, {'$': '1260', 'D': 'SSW', 'G': '37', 'H': '87.7', 'P': '1005', 'S': '26', 'T': '8.0', 'V': '15000', 'W': '8', 'Dp': '6.1', 'Pt': 'F'}, {'$': '1320', 'D': 'S', 'G': '37', 'H': '88.4', 'P': '1003', 'S': '24', 'T': '8.0', 'V': '13000', 'W': '8', 'Dp': '6.2', 'Pt': 'F'}, {'$': '1380', 'D': 'S', 'G': '38', 'H': '89.6', 'P': '1002', 'S': '29', 'T': '7.6', 'V': '11000', 'W': '8', 'Dp': '6.0', 'Pt': 'F'}], 'type': 'Day', 'value': '2020-01-01Z'}] SCOTLAND EUROPE 15.0
1 3005 60.139 -1.183 LERWICK (S. SCREEN) [{'Rep': {'$': '1380', 'D': 'W', 'G': '41', 'H': '89.5', 'P': '1020', 'S': '28', 'T': '7.2', 'V': '15000', 'W': '8', 'Dp': '5.6', 'Pt': 'F'}, 'type': 'Day', 'value': '2019-12-31Z'}, {'Rep': [{'$': '0', 'D': 'WSW', 'G': '44', 'H': '88.1', 'P': '1019', 'S': '33', 'T': '6.9', 'V': '15000', 'W': '7', 'Dp': '5.1', 'Pt': 'F'}, {'$': '60', 'D': 'WSW', 'G': '47', 'H': '90.2', 'P': '1018', 'S': '36', 'T': '6.9', 'V': '15000', 'W': '7', 'Dp': '5.4', 'Pt': 'F'}, {'$': '120', 'D': 'WSW', 'G': '52', 'H': '88.8', 'P': '1018', 'S': '32', 'T': '6.9', 'V': '17000', 'W': '8', 'Dp': '5.2', 'Pt': 'F'}, {'$': '180', 'D': 'WSW', 'G': '47', 'H': '89.4', 'P': '1017', 'S': '34', 'T': '7.4', 'V': '12000', 'W': '8', 'Dp': '5.8', 'Pt': 'F'}, {'$': '240', 'D': 'WSW', 'G': '51', 'H': '89.4', 'P': '1016', 'S': '38', 'T': '7.4', 'V': '14000', 'W': '8', 'Dp': '5.8', 'Pt': 'F'}, {'$': '300', 'D': 'WSW', 'G': '48', 'H': '90.8', 'P': '1015', 'S': '33', 'T': '7.7', 'V': '13000', 'W': '8', 'Dp': '6.3', 'Pt': 'F'}, {'$': '360', 'D': 'WSW', 'G': '49', 'H': '92.0', 'P': '1015', 'S': '34', 'T': '7.9', 'V': '10000', 'W': '8', 'Dp': '6.7', 'Pt': 'F'}, {'$': '420', 'D': 'WSW', 'G': '47', 'H': '92.1', 'P': '1014', 'S': '38', 'T': '8.0', 'V': '8000', 'W': '8', 'Dp': '6.8', 'Pt': 'F'}, {'$': '480', 'D': 'WSW', 'G': '48', 'H': '94.0', 'P': '1014', 'S': '34', 'T': '7.9', 'V': '10000', 'W': '11', 'Dp': '7.0', 'Pt': 'F'}, {'$': '540', 'D': 'WSW', 'G': '55', 'H': '90.2', 'P': '1014', 'S': '40', 'T': '8.1', 'V': '12000', 'W': '7', 'Dp': '6.6', 'Pt': 'F'}, {'$': '600', 'D': 'WSW', 'G': '52', 'H': '88.9', 'P': '1013', 'S': '39', 'T': '8.3', 'V': '15000', 'W': '7', 'Dp': '6.6', 'Pt': 'F'}, {'$': '660', 'D': 'WSW', 'G': '54', 'H': '90.1', 'P': '1013', 'S': '39', 'T': '8.3', 'V': '12000', 'W': '7', 'Dp': '6.8', 'Pt': 'F'}, {'$': '720', 'D': 'WSW', 'G': '53', 'H': '90.9', 'P': '1012', 'S': '38', 'T': '8.5', 'V': '15000', 'W': '7', 'Dp': '7.1', 'Pt': 'F'}, {'$': '780', 'D': 'WSW', 'G': '53', 'H': '91.5', 'P': '1011', 'S': '39', 'T': '8.5', 'V': '12000', 'W': '7', 'Dp': '7.2', 'Pt': 'F'}, {'$': '840', 'D': 'WSW', 'G': '49', 'H': '92.7', 'P': '1011', 'S': '37', 'T': '8.3', 'V': '12000', 'W': '7', 'Dp': '7.2', 'Pt': 'F'}, {'$': '900', 'D': 'WSW', 'G': '51', 'H': '89.6', 'P': '1010', 'S': '34', 'T': '8.3', 'V': '12000', 'W': '7', 'Dp': '6.7', 'Pt': 'F'}, {'$': '960', 'D': 'WSW', 'G': '46', 'H': '88.9', 'P': '1010', 'S': '34', 'T': '8.3', 'V': '15000', 'W': '7', 'Dp': '6.6', 'Pt': 'F'}, {'$': '1020', 'D': 'WSW', 'G': '46', 'H': '86.5', 'P': '1009', 'S': '34', 'T': '8.4', 'V': '18000', 'W': '7', 'Dp': '6.3', 'Pt': 'F'}, {'$': '1080', 'D': 'WSW', 'G': '46', 'H': '84.8', 'P': '1009', 'S': '36', 'T': '8.5', 'V': '18000', 'W': '7', 'Dp': '6.1', 'Pt': 'F'}, {'$': '1140', 'D': 'SSW', 'G': '43', 'H': '88.3', 'P': '1009', 'S': '28', 'T': '7.8', 'V': '18000', 'W': '7', 'Dp': '6.0', 'Pt': 'F'}, {'$': '1200', 'D': 'SSW', 'G': '36', 'H': '88.9', 'P': '1008', 'S': '25', 'T': '7.5', 'V': '20000', 'W': '8', 'Dp': '5.8', 'Pt': 'F'}, {'$': '1260', 'D': 'SSW', 'G': '36', 'H': '88.9', 'P': '1006', 'S': '25', 'T': '7.5', 'V': '15000', 'W': '8', 'Dp': '5.8', 'Pt': 'F'}, {'$': '1320', 'D': 'SSW', 'G': '36', 'H': '89.6', 'P': '1005', 'S': '24', 'T': '7.1', 'V': '13000', 'W': '8', 'Dp': '5.5', 'Pt': 'F'}, {'$': '1380', 'D': 'SSW', 'G': '38', 'H': '86.4', 'P': '1003', 'S': '28', 'T': '7.2', 'V': '18000', 'W': '8', 'Dp': '5.1', 'Pt': 'F'}], 'type': 'Day', 'value': '2020-01-01Z'}] SCOTLAND EUROPE 82.0
2 3008 59.527 -1.628 FAIR ISLE [{'Rep': {'$': '1380', 'D': 'SW', 'G': '31', 'H': '83.8', 'P': '1022', 'S': '24', 'T': '6.4', 'V': '17000', 'W': '7', 'Dp': '3.9', 'Pt': 'F'}, 'type': 'Day', 'value': '2019-12-31Z'}, {'Rep': [{'$': '0', 'D': 'SW', 'G': '30', 'H': '88.1', 'P': '1022', 'S': '16', 'T': '6.0', 'V': '11000', 'W': '0', 'Dp': '4.2', 'Pt': 'F'}, {'$': '60', 'D': 'SW', 'H': '82.1', 'P': '1021', 'S': '18', 'T': '6.5', 'V': '15000', 'W': '0', 'Dp': '3.7', 'Pt': 'F'}, {'$': '120', 'D': 'WSW', 'G': '33', 'H': '74.3', 'P': '1020', 'S': '18', 'T': '6.6', 'V': '24000', 'W': '0', 'Dp': '2.4', 'Pt': 'F'}, {'$': '180', 'D': 'WSW', 'G': '30', 'H': '79.2', 'P': '1019', 'S': '23', 'T': '6.6', 'V': '20000', 'W': '0', 'Dp': '3.3', 'Pt': 'F'}, {'$': '240', 'D': 'SW', 'G': '31', 'H': '82.6', 'P': '1018', 'S': '21', 'T': '6.5', 'V': '17000', 'W': '2', 'Dp': '3.8', 'Pt': 'F'}, {'$': '300', 'D': 'SW', 'H': '81.5', 'P': '1018', 'S': '17', 'T': '6.5', 'V': '18000', 'W': '0', 'Dp': '3.6', 'Pt': 'F'}, {'$': '360', 'D': 'SW', 'H': '80.9', 'P': '1018', 'S': '16', 'T': '6.6', 'V': '15000', 'W': '0', 'Dp': '3.6', 'Pt': 'F'}, {'$': '420', 'D': 'SW', 'H': '78.7', 'P': '1017', 'S': '17', 'T': '7.2', 'V': '14000', 'W': '7', 'Dp': '3.8', 'Pt': 'F'}, {'$': '480', 'D': 'SW', 'H': '84.0', 'P': '1017', 'S': '18', 'T': '7.6', 'V': '18000', 'W': '8', 'Dp': '5.1', 'Pt': 'F'}, {'$': '540', 'D': 'WSW', 'G': '39', 'H': '84.1', 'P': '1016', 'S': '26', 'T': '8.2', 'V': '17000', 'W': '7', 'Dp': '5.7', 'Pt': 'F'}, {'$': '600', 'D': 'SW', 'G': '34', 'H': '78.8', 'P': '1016', 'S': '24', 'T': '8.0', 'V': '16000', 'W': '7', 'Dp': '4.6', 'Pt': 'F'}, {'$': '660', 'D': 'SW', 'G': '29', 'H': '82.3', 'P': '1016', 'S': '21', 'T': '8.1', 'V': '15000', 'W': '7', 'Dp': '5.3', 'Pt': 'F'}, {'$': '720', 'D': 'SSW', 'G': '30', 'H': '84.7', 'P': '1015', 'S': '18', 'T': '8.2', 'V': '10000', 'W': '7', 'Dp': '5.8', 'Pt': 'F'}, {'$': '780', 'D': 'SW', 'G': '30', 'H': '85.3', 'P': '1014', 'S': '23', 'T': '8.1', 'V': '12000', 'W': '7', 'Dp': '5.8', 'Pt': 'F'}, {'$': '840', 'D': 'SW', 'G': '32', 'H': '86.5', 'P': '1013', 'S': '23', 'T': '7.9', 'V': '9000', 'W': '7', 'Dp': '5.8', 'Pt': 'F'}, {'$': '900', 'D': 'SW', 'G': '33', 'H': '87.0', 'P': '1013', 'S': '22', 'T': '8.0', 'V': '12000', 'W': '7', 'Dp': '6.0', 'Pt': 'F'}, {'$': '960', 'D': 'SW', 'G': '31', 'H': '87.7', 'P': '1012', 'S': '22', 'T': '7.9', 'V': '14000', 'W': '7', 'Dp': '6.0', 'Pt': 'F'}, {'$': '1020', 'D': 'SSW', 'G': '31', 'H': '86.5', 'P': '1012', 'S': '22', 'T': '7.9', 'V': '11000', 'W': '7', 'Dp': '5.8', 'Pt': 'F'}, {'$': '1080', 'D': 'SSW', 'G': '32', 'H': '89.0', 'P': '1011', 'S': '21', 'T': '7.7', 'V': '10000', 'W': '7', 'Dp': '6.0', 'Pt': 'F'}, {'$': '1140', 'D': 'SSW', 'G': '33', 'H': '88.9', 'P': '1010', 'S': '25', 'T': '7.8', 'V': '11000', 'W': '7', 'Dp': '6.1', 'Pt': 'F'}, {'$': '1200', 'D': 'S', 'G': '36', 'H': '88.3', 'P': '1009', 'S': '26', 'T': '7.5', 'V': '15000', 'W': '8', 'Dp': '5.7', 'Pt': 'F'}, {'$': '1260', 'D': 'S', 'G': '43', 'H': '83.5', 'P': '1007', 'S': '33', 'T': '7.5', 'V': '15000', 'W': '8', 'Dp': '4.9', 'Pt': 'F'}, {'$': '1320', 'D': 'S', 'G': '43', 'H': '80.0', 'P': '1006', 'S': '31', 'T': '7.6', 'V': '15000', 'W': '7', 'Dp': '4.4', 'Pt': 'F'}, {'$': '1380', 'D': 'S', 'G': '45', 'H': '81.3', 'P': '1005', 'S': '30', 'T': '7.5', 'V': '17000', 'W': '8', 'Dp': '4.5', 'Pt': 'F'}], 'type': 'Day', 'value': '2020-01-01Z'}] SCOTLAND EUROPE 57.0
3 3017 58.954 -2.9 KIRKWALL [{'Rep': {'$': '1380', 'D': 'SW', 'H': '85.9', 'P': '1022', 'S': '21', 'T': '3.7', 'V': '35000', 'W': '0', 'Dp': '1.6', 'Pt': 'F'}, 'type': 'Day', 'value': '2019-12-31Z'}, {'Rep': [{'$': '0', 'D': 'SW', 'H': '84.0', 'P': '1022', 'S': '13', 'T': '3.9', 'V': '35000', 'W': '0', 'Dp': '1.5', 'Pt': 'F'}, {'$': '60', 'D': 'SW', 'H': '78.6', 'P': '1021', 'S': '11', 'T': '3.6', 'V': '50000', 'W': '0', 'Dp': '0.3', 'Pt': 'F'}, {'$': '120', 'D': 'SSW', 'H': '79.4', 'P': '1020', 'S': '15', 'T': '3.7', 'V': '55000', 'W': '0', 'Dp': '0.5', 'Pt': 'F'}, {'$': '180', 'D': 'SSW', 'H': '80.1', 'P': '1020', 'S': '9', 'T': '4.0', 'V': '45000', 'W': '0', 'Dp': '0.9', 'Pt': 'F'}, {'$': '240', 'D': 'S', 'H': '83.9', 'P': '1018', 'S': '10', 'T': '2.6', 'V': '35000', 'W': '0', 'Dp': '0.2', 'Pt': 'F'}, {'$': '300', 'D': 'W', 'H': '81.0', 'P': '1018', 'S': '2', 'T': '2.5', 'V': '45000', 'W': '0', 'Dp': '-0.4', 'Pt': 'F'}, {'$': '360', 'D': 'SSW', 'H': '75.3', 'P': '1018', 'S': '10', 'T': '3.8', 'V': '55000', 'W': '0', 'Dp': '-0.1', 'Pt': 'F'}, {'$': '420', 'D': 'SSW', 'H': '80.5', 'P': '1017', 'S': '11', 'T': '3.7', 'V': '50000', 'W': '0', 'Dp': '0.7', 'Pt': 'F'}, {'$': '480', 'D': 'SSW', 'H': '76.7', 'P': '1017', 'S': '16', 'T': '5.2', 'V': '50000', 'W': '0', 'Dp': '1.5', 'Pt': 'F'}, {'$': '540', 'D': 'SSW', 'H': '83.7', 'P': '1017', 'S': '14', 'T': '5.6', 'V': '30000', 'W': '2', 'Dp': '3.1', 'Pt': 'F'}, {'$': '600', 'D': 'SW', 'H': '85.7', 'P': '1016', 'S': '16', 'T': '5.5', 'V': '29000', 'W': '3', 'Dp': '3.3', 'Pt': 'F'}, {'$': '660', 'D': 'SW', 'H': '79.5', 'P': '1016', 'S': '14', 'T': '7.9', 'V': '35000', 'W': '8', 'Dp': '4.6', 'Pt': 'F'}, {'$': '720', 'D': 'SSW', 'H': '80.0', 'P': '1016', 'S': '16', 'T': '7.8', 'V': '30000', 'W': '7', 'Dp': '4.6', 'Pt': 'F'}, {'$': '780', 'D': 'SW', 'H': '83.4', 'P': '1015', 'S': '18', 'T': '7.6', 'V': '30000', 'W': '8', 'Dp': '5.0', 'Pt': 'F'}, {'$': '840', 'D': 'SW', 'H': '82.9', 'P': '1014', 'S': '15', 'T': '7.8', 'V': '40000', 'W': '7', 'Dp': '5.1', 'Pt': 'F'}, {'$': '900', 'D': 'SW', 'G': '29', 'H': '84.0', 'P': '1013', 'S': '22', 'T': '7.6', 'V': '40000', 'W': '7', 'Dp': '5.1', 'Pt': 'F'}, {'$': '960', 'D': 'SSW', 'H': '82.9', 'P': '1012', 'S': '18', 'T': '7.1', 'V': '50000', 'W': '0', 'Dp': '4.4', 'Pt': 'F'}, {'$': '1020', 'D': 'S', 'H': '86.3', 'P': '1012', 'S': '17', 'T': '6.6', 'V': '26000', 'W': '7', 'Dp': '4.5', 'Pt': 'F'}, {'$': '1080', 'D': 'S', 'H': '87.5', 'P': '1011', 'S': '21', 'T': '6.3', 'V': '28000', 'W': '7', 'Dp': '4.4', 'Pt': 'F'}, {'$': '1140', 'D': 'SSW', 'H': '88.1', 'P': '1010', 'S': '19', 'T': '6.4', 'V': '23000', 'W': '2', 'Dp': '4.6', 'Pt': 'F'}, {'$': '1200', 'D': 'S', 'G': '29', 'H': '87.6', 'P': '1009', 'S': '21', 'T': '6.6', 'V': '24000', 'W': '7', 'Dp': '4.7', 'Pt': 'F'}, {'$': '1260', 'D': 'S', 'G': '29', 'H': '83.9', 'P': '1007', 'S': '19', 'T': '6.7', 'V': '29000', 'W': '8', 'Dp': '4.2', 'Pt': 'F'}, {'$': '1320', 'D': 'S', 'G': '29', 'H': '81.7', 'P': '1006', 'S': '22', 'T': '6.8', 'V': '30000', 'W': '8', 'Dp': '3.9', 'Pt': 'F'}, {'$': '1380', 'D': 'S', 'G': '31', 'H': '82.4', 'P': '1004', 'S': '24', 'T': '7.1', 'V': '26000', 'W': '8', 'Dp': '4.3', 'Pt': 'F'}], 'type': 'Day', 'value': '2020-01-01Z'}] SCOTLAND EUROPE 26.0
4 3023 57.358 -7.397 SOUTH UIST RANGE [{'Rep': {'$': '1380', 'D': 'S', 'H': '89.4', 'P': '1025', 'S': '22', 'T': '7.3', 'V': '15000', 'W': '8', 'Dp': '5.7', 'Pt': 'F'}, 'type': 'Day', 'value': '2019-12-31Z'}, {'Rep': [{'$': '0', 'D': 'S', 'H': '93.3', 'P': '1024', 'S': '19', 'T': '7.3', 'V': '15000', 'W': '8', 'Dp': '6.3', 'Pt': 'F'}, {'$': '60', 'D': 'S', 'H': '94.6', 'P': '1023', 'S': '22', 'T': '7.9', 'V': '12000', 'W': '8', 'Dp': '7.1', 'Pt': 'F'}, {'$': '120', 'D': 'S', 'G': '33', 'H': '90.2', 'P': '1022', 'S': '26', 'T': '8.5', 'V': '25000', 'W': '7', 'Dp': '7.0', 'Pt': 'F'}, {'$': '180', 'D': 'S', 'G': '39', 'H': '87.7', 'P': '1021', 'S': '29', 'T': '8.1', 'V': '40000', 'W': '8', 'Dp': '6.2', 'Pt': 'F'}, {'$': '240', 'D': 'SSW', 'G': '39', 'H': '84.7', 'P': '1021', 'S': '29', 'T': '8.5', 'V': '20000', 'W': '8', 'Dp': '6.1', 'Pt': 'F'}, {'$': '300', 'D': 'SSW', 'G': '43', 'H': '85.9', 'P': '1020', 'S': '31', 'T': '8.5', 'V': '23000', 'W': '8', 'Dp': '6.3', 'Pt': 'F'}, {'$': '360', 'D': 'S', 'G': '38', 'H': '90.8', 'P': '1020', 'S': '25', 'T': '8.5', 'V': '15000', 'W': '8', 'Dp': '7.1', 'Pt': 'F'}, {'$': '420', 'D': 'SSW', 'G': '38', 'H': '92.0', 'P': '1019', 'S': '26', 'T': '8.4', 'V': '5000', 'W': '8', 'Dp': '7.2', 'Pt': 'F'}, {'$': '480', 'D': 'S', 'G': '38', 'H': '97.9', 'P': '1019', 'S': '26', 'T': '8.2', 'V': '3700', 'W': '9', 'Dp': '7.9', 'Pt': 'F'}, {'$': '540', 'D': 'SSW', 'G': '41', 'H': '97.9', 'P': '1018', 'S': '30', 'T': '8.4', 'V': '4800', 'W': '8', 'Dp': '8.1', 'Pt': 'F'}, {'$': '600', 'D': 'SSW', 'G': '37', 'H': '95.9', 'P': '1018', 'S': '28', 'T': '8.9', 'V': '11000', 'W': '8', 'Dp': '8.3', 'Pt': 'F'}, {'$': '660', 'D': 'SSW', 'G': '38', 'H': '93.4', 'P': '1018', 'S': '28', 'T': '9.1', 'V': '13000', 'W': '8', 'Dp': '8.1', 'Pt': 'F'}, {'$': '720', 'D': 'SSW', 'G': '37', 'H': '92.1', 'P': '1017', 'S': '28', 'T': '9.0', 'V': '15000', 'W': '8', 'Dp': '7.8', 'Pt': 'F'}, {'$': '780', 'D': 'S', 'G': '38', 'H': '90.9', 'P': '1016', 'S': '28', 'T': '9.1', 'V': '9000', 'W': '8', 'Dp': '7.7', 'Pt': 'F'}, {'$': '840', 'D': 'S', 'G': '41', 'H': '87.8', 'P': '1015', 'S': '30', 'T': '9.1', 'V': '19000', 'W': '8', 'Dp': '7.2', 'Pt': 'F'}, {'$': '900', 'D': 'S', 'G': '44', 'H': '87.2', 'P': '1014', 'S': '31', 'T': '9.1', 'V': '18000', 'W': '8', 'Dp': '7.1', 'Pt': 'F'}, {'$': '960', 'D': 'S', 'G': '46', 'H': '86.6', 'P': '1013', 'S': '31', 'T': '9.1', 'V': '24000', 'W': '8', 'Dp': '7.0', 'Pt': 'F'}, {'$': '1020', 'D': 'S', 'G': '43', 'H': '87.2', 'P': '1012', 'S': '29', 'T': '9.1', 'V': '25000', 'W': '8', 'Dp': '7.1', 'Pt': 'F'}, {'$': '1080', 'D': 'S', 'G': '44', 'H': '91.5', 'P': '1011', 'S': '33', 'T': '8.9', 'V': '14000', 'W': '7', 'Dp': '7.6', 'Pt': 'F'}, {'$': '1140', 'D': 'S', 'G': '47', 'H': '92.8', 'P': '1010', 'S': '33', 'T': '8.7', 'V': '7000', 'W': '8', 'Dp': '7.6', 'Pt': 'F'}, {'$': '1200', 'D': 'S', 'G': '48', 'H': '91.4', 'P': '1009', 'S': '33', 'T': '8.8', 'V': '12000', 'W': '8', 'Dp': '7.5', 'Pt': 'F'}, {'$': '1260', 'D': 'S', 'G': '47', 'H': '91.5', 'P': '1008', 'S': '34', 'T': '8.7', 'V': '18000', 'W': '8', 'Dp': '7.4', 'Pt': 'F'}, {'$': '1320', 'D': 'S', 'G': '46', 'H': '89.0', 'P': '1007', 'S': '33', 'T': '9.0', 'V': '19000', 'W': '8', 'Dp': '7.3', 'Pt': 'F'}, {'$': '1380', 'D': 'S', 'G': '44', 'H': '88.5', 'P': '1006', 'S': '34', 'T': '9.2', 'V': '12000', 'W': '8', 'Dp': '7.4', 'Pt': 'F'}], 'type': 'Day', 'value': '2020-01-01Z'}] SCOTLAND EUROPE 4.0
展平“期间”列的最佳方法是什么？有没有更好的方法来达到预期的效果？
谢谢。
PS的完整json文件位于https://wetransfer.com/downloads/5dd39d51e640d94a87e04297bfa1db3d20200909162616/c41164
答案 0 :(得分：2)
json_normalize的组合来打开dicts .explode爆炸lists中的dicts
dict将移至另一行.json_normalize的新列上使用dicts 'Location'都有一个'Period' 'Period'是dicts的列表。
dict是'Rep'，它是一个dict dict也是'Rep'，但它是list中的dicts 'Period'进行规范化后，第一个'Rep'会扩展为单独的列（'Rep.$'，'Rep.D'等），而第二个'Rep'是NaN的{{1}}和lists的{{1}}列。dicts中lists中的dicts爆炸了，因此每个'Rep'在单独的行中。
dict标准化为单独的列（dicts，'$'等），重命名列标题以将'D'添加到最前面，最后，用于在数据帧'Rep.'的相应列中填充NaNs。dfimport pandas as pd
import json
# read in the JSON file
with open('metoffice.json', encoding='utf-8') as f:
data = json.loads(f.read())
# normalize Location
df = pd.json_normalize(data, ['SiteRep', 'DV', 'Location'])
# explode the list of dicts in Period
df = df.explode('Period').reset_index(drop=True)
# normalize and join Period back to df
df = df.join(pd.json_normalize(df.Period)).drop(columns=['Period'])
# Rep contains NaNs or lists of dicts
# NaN can't be exploded so they must be filled with empty lists
# .fillna([]) does not work
df.Rep = df.Rep.fillna({i: [] for i in df.index})
# explode the lists on Rep
df = df.explode('Rep').reset_index(drop=True)
# fillna with {} to use json_normalize
df.Rep = df.Rep.fillna({i: {} for i in df.index})
# normalize Rep
rep = pd.json_normalize(df.Rep)
# add Rep. to beginning of column names in the rep dataframe
rep.columns = [f'Rep.{v}' for v in rep.columns]
# fillna on the the Rep. columns from the rep dataframe and drop the Rep column
df = df.fillna(rep).drop(columns=['Rep'])
和第一个'Rep'都有一行（25：0-24），该行与JSON文件匹配。'Location'