如何将作为字典的列表元素卸载到dataframe中（使用它的第一个值作为前缀）问题的回答

如何将作为字典的列表元素卸载到dataframe中（使用它的第一个值作为前缀）

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<p>我目前正在学习python（使用pandas）来处理数据分析。我在DataCamp上做了一些课程，并试图将我学到的知识应用到一个实际问题中：我想监测加拿大的新冠病毒-19病例</p> <p>为此，我从Apify API获取数据，该API返回一个json，然后创建一个数据帧。数据帧结构如下所示：</p> <pre><code><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 </code></pre> <p>感染和死亡列包含加拿大的总数</p> <p>在infectedByRegion列中，每行都有一个字典列表，如下所示：</p> <pre><code> [{'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'}] </code></pre> <p>我试图在数据框的末尾为每个地区的感染和死亡人数创建列。例如：</p> <pre><code>... measureTime Quebec_infectedCount Quebec_deceasedCount Ontario_infectedCount ... ... 22:30:15 2840 22 1355 ... </code></pre> <p>我尝试使用json_normalize函数，但它给我带来了一个错误：</p> <pre><code>AttributeError: 'list' object has no attribute 'values' </code></pre> <p>然后我尝试在这里查看stackoverflow，发现了以下链接：</p> <p><a href="https://stackoverflow.com/questions/45325208/python-json-normalize-a-pandas-series-gives-typeerror">Python: json_normalize a pandas series gives TypeError</a></p> <p>这对我来说不起作用，因为它只创建了一个名为region的列，在数据帧末尾的每一行中只包含“Canada”作为一个值</p> <pre><code>... measureDate measureTime region ... 2020-03-29 22:30:15 Canada ... 2020-03-30 22:30:15 Canada </code></pre> <p>有没有人能帮我或者给我指一个合适的帖子，帮助我解决问题？由于我还是一个初学者，我试着搜索了几个小时，但我想我甚至不知道如何准确地界定我的问题，但我真的想学习如何处理这种情况</p> <p>提前谢谢</p>

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匿名 1天前

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<ul> <li>给定以下数据帧，其中一列（<code>infectedByRegion</code>）是字典列表</li> </ul> <h2><code>infectedByRegion</code>的目录列表</h2> <pre class="lang-py prettyprint-override"><code>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'}] </code></pre> <h2>代表性数据帧</h2> <pre class="lang-py prettyprint-override"><code>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'}] </code></pre> <h2><code>explode</code>将目录列表分成单独的行</h2> <ul> <li>不清楚<code>infectedByRegion</code>列在数据帧中是类型<code>list</code>还是<code>str</code>，因此可能需要修复</li> </ul> <pre class="lang-py prettyprint-override"><code># 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 | </code></pre> <h2>将字典键转换为列</h2> <pre class="lang-py prettyprint-override"><code>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 | </code></pre> <h2>转向所需的格式</h2> <pre class="lang-py prettyprint-override"><code>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 </code></pre>

如何将作为字典的列表元素卸载到dataframe中（使用它的第一个值作为前缀）

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