我目前正在研究python(使用pandas(来处理数据分析。我在 DataCamp 上做了几门课程，并试图将我学到的知识应用到一个真正的问题中：我想监测加拿大的 covid-19 病例。
为此，我从 Apify API 获取数据，该 API 返回一个 json，然后我从中创建数据帧。数据帧结构如下所示：
<class 'pandas.core.frame.DataFrame'>
Int64Index: 57 entries, 0 to 56
Data columns (total 9 columns):
infected 57 non-null float64
deceased 57 non-null float64
infectedByRegion 57 non-null object
measureDate 57 non-null object
measureTime 57 non-null object
感染"和"已死亡"列包含加拿大的总数。
在 infectedByRegion 列中，我每行都有一个字典列表，如下所示：
[{'region': 'Canada', 'infectedCount': '6258', 'deceasedCount': '61'},
{'region': 'Newfoundland and Labrador',
'infectedCount': '135',
'deceasedCount': '0'},
{'region': 'Prince Edward Island',
'infectedCount': '11',
'deceasedCount': '0'},
{'region': 'Nova Scotia', 'infectedCount': '122', 'deceasedCount': '0'},
{'region': 'New Brunswick', 'infectedCount': '66', 'deceasedCount': '0'},
{'region': 'Quebec', 'infectedCount': '2840', 'deceasedCount': '22'},
{'region': 'Ontario', 'infectedCount': '1355', 'deceasedCount': '19'},
{'region': 'Manitoba', 'infectedCount': '72', 'deceasedCount': '1'},
{'region': 'Saskatchewan', 'infectedCount': '134', 'deceasedCount': '0'},
{'region': 'Alberta', 'infectedCount': '621', 'deceasedCount': '2'},
{'region': 'British Columbia', 'infectedCount': '884', 'deceasedCount': '17'},
{'region': 'Yukon', 'infectedCount': '4', 'deceasedCount': '0'},
{'region': 'Northwest Territories',
'infectedCount': '1',
'deceasedCount': '0'},
{'region': 'Nunavut', 'infectedCount': '0', 'deceasedCount': '0'},
{'region': 'Repatriated travellers',
'infectedCount': '13',
'deceasedCount': '0'}]
我正在尝试在每个区域感染和死亡计数的数据帧末尾创建列。例：
... measureTime Quebec_infectedCount Quebec_deceasedCount Ontario_infectedCount ...
... 22:30:15 2840 22 1355 ...
我尝试使用json_normalize函数，但它给我抛出了一个错误：
AttributeError: 'list' object has no attribute 'values'
然后我试着在这里查看stackoverflow，我找到了这个链接：
Python：json_normalize熊猫系列给出 TypeError
它对我不起作用，因为它只创建了一个名为区域的列，该列在数据框末尾的每一行中仅包含"加拿大"作为值
... measureDate measureTime region
... 2020-03-29 22:30:15 Canada
... 2020-03-30 22:30:15 Canada
有人可以帮助或指出我在这里的适当帖子作为堆栈溢出来帮助我解决问题吗？由于我仍然是初学者，我试图搜索几个小时，但我想我什至不知道如何准确地构建我的问题，但真的很想学习如何处理这种情况。
提前感谢！
- 给定以下数据帧，其中一列(
infectedByRegion(是字典列表
infectedByRegion字典列表
data = [{'region': 'Canada', 'infectedCount': '6258', 'deceasedCount': '61'},
{'region': 'Newfoundland and Labrador', 'infectedCount': '135', 'deceasedCount': '0'},
{'region': 'Prince Edward Island', 'infectedCount': '11', 'deceasedCount': '0'},
{'region': 'Nova Scotia', 'infectedCount': '122', 'deceasedCount': '0'},
{'region': 'New Brunswick', 'infectedCount': '66', 'deceasedCount': '0'},
{'region': 'Quebec', 'infectedCount': '2840', 'deceasedCount': '22'},
{'region': 'Ontario', 'infectedCount': '1355', 'deceasedCount': '19'},
{'region': 'Manitoba', 'infectedCount': '72', 'deceasedCount': '1'},
{'region': 'Saskatchewan', 'infectedCount': '134', 'deceasedCount': '0'},
{'region': 'Alberta', 'infectedCount': '621', 'deceasedCount': '2'},
{'region': 'British Columbia', 'infectedCount': '884', 'deceasedCount': '17'},
{'region': 'Yukon', 'infectedCount': '4', 'deceasedCount': '0'},
{'region': 'Northwest Territories', 'infectedCount': '1', 'deceasedCount': '0'},
{'region': 'Nunavut', 'infectedCount': '0', 'deceasedCount': '0'},
{'region': 'Repatriated travellers', 'infectedCount': '13', 'deceasedCount': '0'}]
代表性数据帧
import pandas as pd
from ast import literal_eval
df = pd.DataFrame({'measureDate': ['2020-03-29', '2020-03-30', '2020-03-31'], 'measureTime': ['22:30:15', '21:30:16', '20:56:29'],
'infectedByRegion': [data, data, data], 'infected': [12516, 13000, 14000], 'deceased': [122, 133, 143]})
measureDate measureTime infected deceased infectedByRegion
0 2020-03-29 22:30:15 12516 122 [{'region': 'Canada', 'infectedCount': '6258', 'deceasedCount': '61'}, {'region': 'Newfoundland and Labrador', 'infectedCount': '135', 'deceasedCount': '0'}, {'region': 'Prince Edward Island', 'infectedCount': '11', 'deceasedCount': '0'}, {'region': 'Nova Scotia', 'infectedCount': '122', 'deceasedCount': '0'}, {'region': 'New Brunswick', 'infectedCount': '66', 'deceasedCount': '0'}, {'region': 'Quebec', 'infectedCount': '2840', 'deceasedCount': '22'}, {'region': 'Ontario', 'infectedCount': '1355', 'deceasedCount': '19'}, {'region': 'Manitoba', 'infectedCount': '72', 'deceasedCount': '1'}, {'region': 'Saskatchewan', 'infectedCount': '134', 'deceasedCount': '0'}, {'region': 'Alberta', 'infectedCount': '621', 'deceasedCount': '2'}, {'region': 'British Columbia', 'infectedCount': '884', 'deceasedCount': '17'}, {'region': 'Yukon', 'infectedCount': '4', 'deceasedCount': '0'}, {'region': 'Northwest Territories', 'infectedCount': '1', 'deceasedCount': '0'}, {'region': 'Nunavut', 'infectedCount': '0', 'deceasedCount': '0'}, {'region': 'Repatriated travellers', 'infectedCount': '13', 'deceasedCount': '0'}]
1 2020-03-30 21:30:16 13000 133 [{'region': 'Canada', 'infectedCount': '6258', 'deceasedCount': '61'}, {'region': 'Newfoundland and Labrador', 'infectedCount': '135', 'deceasedCount': '0'}, {'region': 'Prince Edward Island', 'infectedCount': '11', 'deceasedCount': '0'}, {'region': 'Nova Scotia', 'infectedCount': '122', 'deceasedCount': '0'}, {'region': 'New Brunswick', 'infectedCount': '66', 'deceasedCount': '0'}, {'region': 'Quebec', 'infectedCount': '2840', 'deceasedCount': '22'}, {'region': 'Ontario', 'infectedCount': '1355', 'deceasedCount': '19'}, {'region': 'Manitoba', 'infectedCount': '72', 'deceasedCount': '1'}, {'region': 'Saskatchewan', 'infectedCount': '134', 'deceasedCount': '0'}, {'region': 'Alberta', 'infectedCount': '621', 'deceasedCount': '2'}, {'region': 'British Columbia', 'infectedCount': '884', 'deceasedCount': '17'}, {'region': 'Yukon', 'infectedCount': '4', 'deceasedCount': '0'}, {'region': 'Northwest Territories', 'infectedCount': '1', 'deceasedCount': '0'}, {'region': 'Nunavut', 'infectedCount': '0', 'deceasedCount': '0'}, {'region': 'Repatriated travellers', 'infectedCount': '13', 'deceasedCount': '0'}]
2 2020-03-31 20:56:29 14000 143 [{'region': 'Canada', 'infectedCount': '6258', 'deceasedCount': '61'}, {'region': 'Newfoundland and Labrador', 'infectedCount': '135', 'deceasedCount': '0'}, {'region': 'Prince Edward Island', 'infectedCount': '11', 'deceasedCount': '0'}, {'region': 'Nova Scotia', 'infectedCount': '122', 'deceasedCount': '0'}, {'region': 'New Brunswick', 'infectedCount': '66', 'deceasedCount': '0'}, {'region': 'Quebec', 'infectedCount': '2840', 'deceasedCount': '22'}, {'region': 'Ontario', 'infectedCount': '1355', 'deceasedCount': '19'}, {'region': 'Manitoba', 'infectedCount': '72', 'deceasedCount': '1'}, {'region': 'Saskatchewan', 'infectedCount': '134', 'deceasedCount': '0'}, {'region': 'Alberta', 'infectedCount': '621', 'deceasedCount': '2'}, {'region': 'British Columbia', 'infectedCount': '884', 'deceasedCount': '17'}, {'region': 'Yukon', 'infectedCount': '4', 'deceasedCount': '0'}, {'region': 'Northwest Territories', 'infectedCount': '1', 'deceasedCount': '0'}, {'region': 'Nunavut', 'infectedCount': '0', 'deceasedCount': '0'}, {'region': 'Repatriated travellers', 'infectedCount': '13', 'deceasedCount': '0'}]
explode字典列表到单独的行中
- 不清楚
infectedByRegion列是数据帧中的类型list还是str，因此可能需要修复
# convert str to list; may not be required
df.infectedByRegion = df.infectedByRegion.apply(literal_eval)
# combine columns to datetime the drop them
df['DateTime'] = pd.to_datetime(df.measureDate + ' ' + df.measureTime)
df.drop(columns=['measureDate', 'measureTime'], inplace=True)
# explode infectedByRedion; pandas >= 0.25
df = df.explode('infectedByRegion')
| | infectedByRegion | infected | deceased | DateTime |
|---:|:--------------------------------------------------------------------------------------|-----------:|-----------:|:--------------------|
| 0 | {'region': 'Canada', 'infectedCount': '6258', 'deceasedCount': '61'} | 12516 | 122 | 2020-03-29 22:30:15 |
| 0 | {'region': 'Newfoundland and Labrador', 'infectedCount': '135', 'deceasedCount': '0'} | 12516 | 122 | 2020-03-29 22:30:15 |
| 0 | {'region': 'Prince Edward Island', 'infectedCount': '11', 'deceasedCount': '0'} | 12516 | 122 | 2020-03-29 22:30:15 |
| 0 | {'region': 'Nova Scotia', 'infectedCount': '122', 'deceasedCount': '0'} | 12516 | 122 | 2020-03-29 22:30:15 |
| 0 | {'region': 'New Brunswick', 'infectedCount': '66', 'deceasedCount': '0'} | 12516 | 122 | 2020-03-29 22:30:15 |
| 0 | {'region': 'Quebec', 'infectedCount': '2840', 'deceasedCount': '22'} | 12516 | 122 | 2020-03-29 22:30:15 |
| 0 | {'region': 'Ontario', 'infectedCount': '1355', 'deceasedCount': '19'} | 12516 | 122 | 2020-03-29 22:30:15 |
| 0 | {'region': 'Manitoba', 'infectedCount': '72', 'deceasedCount': '1'} | 12516 | 122 | 2020-03-29 22:30:15 |
| 0 | {'region': 'Saskatchewan', 'infectedCount': '134', 'deceasedCount': '0'} | 12516 | 122 | 2020-03-29 22:30:15 |
| 0 | {'region': 'Alberta', 'infectedCount': '621', 'deceasedCount': '2'} | 12516 | 122 | 2020-03-29 22:30:15 |
| 0 | {'region': 'British Columbia', 'infectedCount': '884', 'deceasedCount': '17'} | 12516 | 122 | 2020-03-29 22:30:15 |
| 0 | {'region': 'Yukon', 'infectedCount': '4', 'deceasedCount': '0'} | 12516 | 122 | 2020-03-29 22:30:15 |
| 0 | {'region': 'Northwest Territories', 'infectedCount': '1', 'deceasedCount': '0'} | 12516 | 122 | 2020-03-29 22:30:15 |
| 0 | {'region': 'Nunavut', 'infectedCount': '0', 'deceasedCount': '0'} | 12516 | 122 | 2020-03-29 22:30:15 |
| 0 | {'region': 'Repatriated travellers', 'infectedCount': '13', 'deceasedCount': '0'} | 12516 | 122 | 2020-03-29 22:30:15 |
| 1 | {'region': 'Canada', 'infectedCount': '6258', 'deceasedCount': '61'} | 13000 | 133 | 2020-03-30 21:30:16 |
| 1 | {'region': 'Newfoundland and Labrador', 'infectedCount': '135', 'deceasedCount': '0'} | 13000 | 133 | 2020-03-30 21:30:16 |
| 1 | {'region': 'Prince Edward Island', 'infectedCount': '11', 'deceasedCount': '0'} | 13000 | 133 | 2020-03-30 21:30:16 |
| 1 | {'region': 'Nova Scotia', 'infectedCount': '122', 'deceasedCount': '0'} | 13000 | 133 | 2020-03-30 21:30:16 |
| 1 | {'region': 'New Brunswick', 'infectedCount': '66', 'deceasedCount': '0'} | 13000 | 133 | 2020-03-30 21:30:16 |
| 1 | {'region': 'Quebec', 'infectedCount': '2840', 'deceasedCount': '22'} | 13000 | 133 | 2020-03-30 21:30:16 |
| 1 | {'region': 'Ontario', 'infectedCount': '1355', 'deceasedCount': '19'} | 13000 | 133 | 2020-03-30 21:30:16 |
| 1 | {'region': 'Manitoba', 'infectedCount': '72', 'deceasedCount': '1'} | 13000 | 133 | 2020-03-30 21:30:16 |
| 1 | {'region': 'Saskatchewan', 'infectedCount': '134', 'deceasedCount': '0'} | 13000 | 133 | 2020-03-30 21:30:16 |
| 1 | {'region': 'Alberta', 'infectedCount': '621', 'deceasedCount': '2'} | 13000 | 133 | 2020-03-30 21:30:16 |
| 1 | {'region': 'British Columbia', 'infectedCount': '884', 'deceasedCount': '17'} | 13000 | 133 | 2020-03-30 21:30:16 |
| 1 | {'region': 'Yukon', 'infectedCount': '4', 'deceasedCount': '0'} | 13000 | 133 | 2020-03-30 21:30:16 |
| 1 | {'region': 'Northwest Territories', 'infectedCount': '1', 'deceasedCount': '0'} | 13000 | 133 | 2020-03-30 21:30:16 |
| 1 | {'region': 'Nunavut', 'infectedCount': '0', 'deceasedCount': '0'} | 13000 | 133 | 2020-03-30 21:30:16 |
| 1 | {'region': 'Repatriated travellers', 'infectedCount': '13', 'deceasedCount': '0'} | 13000 | 133 | 2020-03-30 21:30:16 |
| 2 | {'region': 'Canada', 'infectedCount': '6258', 'deceasedCount': '61'} | 14000 | 143 | 2020-03-31 20:56:29 |
| 2 | {'region': 'Newfoundland and Labrador', 'infectedCount': '135', 'deceasedCount': '0'} | 14000 | 143 | 2020-03-31 20:56:29 |
| 2 | {'region': 'Prince Edward Island', 'infectedCount': '11', 'deceasedCount': '0'} | 14000 | 143 | 2020-03-31 20:56:29 |
| 2 | {'region': 'Nova Scotia', 'infectedCount': '122', 'deceasedCount': '0'} | 14000 | 143 | 2020-03-31 20:56:29 |
| 2 | {'region': 'New Brunswick', 'infectedCount': '66', 'deceasedCount': '0'} | 14000 | 143 | 2020-03-31 20:56:29 |
| 2 | {'region': 'Quebec', 'infectedCount': '2840', 'deceasedCount': '22'} | 14000 | 143 | 2020-03-31 20:56:29 |
| 2 | {'region': 'Ontario', 'infectedCount': '1355', 'deceasedCount': '19'} | 14000 | 143 | 2020-03-31 20:56:29 |
| 2 | {'region': 'Manitoba', 'infectedCount': '72', 'deceasedCount': '1'} | 14000 | 143 | 2020-03-31 20:56:29 |
| 2 | {'region': 'Saskatchewan', 'infectedCount': '134', 'deceasedCount': '0'} | 14000 | 143 | 2020-03-31 20:56:29 |
| 2 | {'region': 'Alberta', 'infectedCount': '621', 'deceasedCount': '2'} | 14000 | 143 | 2020-03-31 20:56:29 |
| 2 | {'region': 'British Columbia', 'infectedCount': '884', 'deceasedCount': '17'} | 14000 | 143 | 2020-03-31 20:56:29 |
| 2 | {'region': 'Yukon', 'infectedCount': '4', 'deceasedCount': '0'} | 14000 | 143 | 2020-03-31 20:56:29 |
| 2 | {'region': 'Northwest Territories', 'infectedCount': '1', 'deceasedCount': '0'} | 14000 | 143 | 2020-03-31 20:56:29 |
| 2 | {'region': 'Nunavut', 'infectedCount': '0', 'deceasedCount': '0'} | 14000 | 143 | 2020-03-31 20:56:29 |
| 2 | {'region': 'Repatriated travellers', 'infectedCount': '13', 'deceasedCount': '0'} | 14000 | 143 | 2020-03-31 20:56:29 |
将字典键转换为列
df_concat = pd.concat([df, df.infectedByRegion.apply(pd.Series)], axis=1).drop('infectedByRegion', axis=1)
| | infected | deceased | DateTime | region | infectedCount | deceasedCount |
|---:|-----------:|-----------:|:--------------------|:--------------------------|----------------:|----------------:|
| 0 | 12516 | 122 | 2020-03-29 22:30:15 | Canada | 6258 | 61 |
| 0 | 12516 | 122 | 2020-03-29 22:30:15 | Newfoundland and Labrador | 135 | 0 |
| 0 | 12516 | 122 | 2020-03-29 22:30:15 | Prince Edward Island | 11 | 0 |
| 0 | 12516 | 122 | 2020-03-29 22:30:15 | Nova Scotia | 122 | 0 |
| 0 | 12516 | 122 | 2020-03-29 22:30:15 | New Brunswick | 66 | 0 |
| 0 | 12516 | 122 | 2020-03-29 22:30:15 | Quebec | 2840 | 22 |
| 0 | 12516 | 122 | 2020-03-29 22:30:15 | Ontario | 1355 | 19 |
| 0 | 12516 | 122 | 2020-03-29 22:30:15 | Manitoba | 72 | 1 |
| 0 | 12516 | 122 | 2020-03-29 22:30:15 | Saskatchewan | 134 | 0 |
| 0 | 12516 | 122 | 2020-03-29 22:30:15 | Alberta | 621 | 2 |
| 0 | 12516 | 122 | 2020-03-29 22:30:15 | British Columbia | 884 | 17 |
| 0 | 12516 | 122 | 2020-03-29 22:30:15 | Yukon | 4 | 0 |
| 0 | 12516 | 122 | 2020-03-29 22:30:15 | Northwest Territories | 1 | 0 |
| 0 | 12516 | 122 | 2020-03-29 22:30:15 | Nunavut | 0 | 0 |
| 0 | 12516 | 122 | 2020-03-29 22:30:15 | Repatriated travellers | 13 | 0 |
| 1 | 13000 | 133 | 2020-03-30 21:30:16 | Canada | 6258 | 61 |
| 1 | 13000 | 133 | 2020-03-30 21:30:16 | Newfoundland and Labrador | 135 | 0 |
| 1 | 13000 | 133 | 2020-03-30 21:30:16 | Prince Edward Island | 11 | 0 |
| 1 | 13000 | 133 | 2020-03-30 21:30:16 | Nova Scotia | 122 | 0 |
| 1 | 13000 | 133 | 2020-03-30 21:30:16 | New Brunswick | 66 | 0 |
| 1 | 13000 | 133 | 2020-03-30 21:30:16 | Quebec | 2840 | 22 |
| 1 | 13000 | 133 | 2020-03-30 21:30:16 | Ontario | 1355 | 19 |
| 1 | 13000 | 133 | 2020-03-30 21:30:16 | Manitoba | 72 | 1 |
| 1 | 13000 | 133 | 2020-03-30 21:30:16 | Saskatchewan | 134 | 0 |
| 1 | 13000 | 133 | 2020-03-30 21:30:16 | Alberta | 621 | 2 |
| 1 | 13000 | 133 | 2020-03-30 21:30:16 | British Columbia | 884 | 17 |
| 1 | 13000 | 133 | 2020-03-30 21:30:16 | Yukon | 4 | 0 |
| 1 | 13000 | 133 | 2020-03-30 21:30:16 | Northwest Territories | 1 | 0 |
| 1 | 13000 | 133 | 2020-03-30 21:30:16 | Nunavut | 0 | 0 |
| 1 | 13000 | 133 | 2020-03-30 21:30:16 | Repatriated travellers | 13 | 0 |
| 2 | 14000 | 143 | 2020-03-31 20:56:29 | Canada | 6258 | 61 |
| 2 | 14000 | 143 | 2020-03-31 20:56:29 | Newfoundland and Labrador | 135 | 0 |
| 2 | 14000 | 143 | 2020-03-31 20:56:29 | Prince Edward Island | 11 | 0 |
| 2 | 14000 | 143 | 2020-03-31 20:56:29 | Nova Scotia | 122 | 0 |
| 2 | 14000 | 143 | 2020-03-31 20:56:29 | New Brunswick | 66 | 0 |
| 2 | 14000 | 143 | 2020-03-31 20:56:29 | Quebec | 2840 | 22 |
| 2 | 14000 | 143 | 2020-03-31 20:56:29 | Ontario | 1355 | 19 |
| 2 | 14000 | 143 | 2020-03-31 20:56:29 | Manitoba | 72 | 1 |
| 2 | 14000 | 143 | 2020-03-31 20:56:29 | Saskatchewan | 134 | 0 |
| 2 | 14000 | 143 | 2020-03-31 20:56:29 | Alberta | 621 | 2 |
| 2 | 14000 | 143 | 2020-03-31 20:56:29 | British Columbia | 884 | 17 |
| 2 | 14000 | 143 | 2020-03-31 20:56:29 | Yukon | 4 | 0 |
| 2 | 14000 | 143 | 2020-03-31 20:56:29 | Northwest Territories | 1 | 0 |
| 2 | 14000 | 143 | 2020-03-31 20:56:29 | Nunavut | 0 | 0 |
| 2 | 14000 | 143 | 2020-03-31 20:56:29 | Repatriated travellers | 13 | 0 |
透视到所需格式
df_pivot = df_concat.pivot(index='DateTime', columns='region', values=['infectedCount', 'deceasedCount'])
# rename multi-index column names
df_pivot.columns = [f'{col[1]}_{col[0]}' for col in df_pivot.columns.values]
# output form
Alberta_infectedCount British Columbia_infectedCount Canada_infectedCount Manitoba_infectedCount New Brunswick_infectedCount Newfoundland and Labrador_infectedCount Northwest Territories_infectedCount Nova Scotia_infectedCount Nunavut_infectedCount Ontario_infectedCount Prince Edward Island_infectedCount Quebec_infectedCount Repatriated travellers_infectedCount Saskatchewan_infectedCount Yukon_infectedCount Alberta_deceasedCount British Columbia_deceasedCount Canada_deceasedCount Manitoba_deceasedCount New Brunswick_deceasedCount Newfoundland and Labrador_deceasedCount Northwest Territories_deceasedCount Nova Scotia_deceasedCount Nunavut_deceasedCount Ontario_deceasedCount Prince Edward Island_deceasedCount Quebec_deceasedCount Repatriated travellers_deceasedCount Saskatchewan_deceasedCount Yukon_deceasedCount
DateTime
2020-03-29 22:30:15 621 884 6258 72 66 135 1 122 0 1355 11 2840 13 134 4 2 17 61 1 0 0 0 0 0 19 0 22 0 0 0
2020-03-30 21:30:16 621 884 6258 72 66 135 1 122 0 1355 11 2840 13 134 4 2 17 61 1 0 0 0 0 0 19 0 22 0 0 0
2020-03-31 20:56:29 621 884 6258 72 66 135 1 122 0 1355 11 2840 13 134 4 2 17 61 1 0 0 0 0 0 19 0 22 0 0 0
您可以使用拥有的列表创建数据帧。
df = pd.DataFrame(
[
{"region": "Canada", "infectedCount": "6258", "deceasedCount": "61"},
{
"region": "Newfoundland and Labrador",
"infectedCount": "135",
"deceasedCount": "0",
},
{"region": "Prince Edward Island", "infectedCount": "11", "deceasedCount": "0"},
{"region": "Nova Scotia", "infectedCount": "122", "deceasedCount": "0"},
{"region": "New Brunswick", "infectedCount": "66", "deceasedCount": "0"},
{"region": "Quebec", "infectedCount": "2840", "deceasedCount": "22"},
{"region": "Ontario", "infectedCount": "1355", "deceasedCount": "19"},
{"region": "Manitoba", "infectedCount": "72", "deceasedCount": "1"},
{"region": "Saskatchewan", "infectedCount": "134", "deceasedCount": "0"},
{"region": "Alberta", "infectedCount": "621", "deceasedCount": "2"},
{"region": "British Columbia", "infectedCount": "884", "deceasedCount": "17"},
{"region": "Yukon", "infectedCount": "4", "deceasedCount": "0"},
{"region": "Northwest Territories", "infectedCount": "1", "deceasedCount": "0"},
{"region": "Nunavut", "infectedCount": "0", "deceasedCount": "0"},
{
"region": "Repatriated travellers",
"infectedCount": "13",
"deceasedCount": "0",
},
]
)
print(df)
region infectedCount deceasedCount
0 Canada 6258 61
1 Newfoundland and Labrador 135 0
2 Prince Edward Island 11 0
3 Nova Scotia 122 0
4 New Brunswick 66 0
5 Quebec 2840 22
6 Ontario 1355 19
7 Manitoba 72 1
8 Saskatchewan 134 0
9 Alberta 621 2
10 British Columbia 884 17
11 Yukon 4 0
12 Northwest Territories 1 0
13 Nunavut 0 0
14 Repatriated travellers 13 0
让我们添加您的日期和时间，并为索引设置日期、时间和区域。
df["measureDate"] = "2020-03-29"
df["measureTime"] = "22:30:15"
df = df.set_index(["measureDate", "measureTime", "region"])
print(df)
measureDate measureTime region
2020-03-29 22:30:15 Canada 6258 61
Newfoundland and Labrador 135 0
Prince Edward Island 11 0
Nova Scotia 122 0
New Brunswick 66 0
Quebec 2840 22
Ontario 1355 19
Manitoba 72 1
Saskatchewan 134 0
Alberta 621 2
British Columbia 884 17
Yukon 4 0
Northwest Territories 1 0
Nunavut 0 0
Repatriated travellers 13 0
接下来，我们将索引中的区域 level=2 解锁到列，交换级别并对列进行排序。
df = df.unstack(level=2)
df.swaplevel(axis=1).sort_index(axis=1)
这在这里打印不好....
region Alberta British Columbia
deceasedCount infectedCount deceasedCount infectedCount
measureDate measureTime
2020-03-29 22:30:15 2 621 17
884