问题

我可以帮你将asset_list列转化为一个单独的数据框（DataFrame），其中包含asset_ID、asset_class、begin_date_utc、begin_date_mpt和metered_volume作为列名。以下是相应的代码：

import pandas as pd

# 假设你已经有一个名为df的数据框，其中包含了JSON数据

# 首先，将asset_list列展开成一个新的数据框
asset_df = pd.concat([pd.json_normalize(x) for x in df['return']], ignore_index=True)

# 选取所需的列
asset_df = asset_df[['asset_ID', 'asset_class', 'metered_volume_list']]

# 将metered_volume_list列展开
metered_volume_df = pd.concat([pd.json_normalize(x) for x in asset_df['metered_volume_list']], ignore_index=True)

# 选取所需的列
metered_volume_df = metered_volume_df[['begin_date_utc', 'begin_date_mpt', 'metered_volume']]

# 将两个数据框合并
result_df = pd.concat([asset_df[['asset_ID', 'asset_class']], metered_volume_df], axis=1)

# 打印结果数据框
print(result_df)

这段代码将会把asset_list列中的数据展开成一个新的数据框，然后从中选择所需的列，并将metered_volume_list列也展开并选择所需的列，最后将两个数据框合并为一个包含asset_ID、asset_class、begin_date_utc、begin_date_mpt和metered_volume列的数据框。

英文:

I am using an API pull to extract data from the AESO API in python. My code is as follows:

API_KEY = &#39;api_key_here&#39;

merit_order_url = &#39;https://api.aeso.ca/report/v1/meteredvolume/details?startDate=2022-01-01&#39;

url = merit_order_url

headers = {&#39;accept&#39;: &#39;application/json&#39;, &#39;X-API-Key&#39;: API_KEY}

response = requests.get(url, headers=headers)

The JSON response looks something like this:

{&#39;timestamp&#39;: &#39;2023-08-10 14:07:24.976+0000&#39;,
 &#39;responseCode&#39;: &#39;200&#39;,
 &#39;return&#39;: [{&#39;pool_participant_ID&#39;: &#39;9496&#39;,
   &#39;asset_list&#39;: [{&#39;asset_ID&#39;: &#39;941A&#39;,
     &#39;asset_class&#39;: &#39;RETAILER&#39;,
     &#39;metered_volume_list&#39;: [{&#39;begin_date_utc&#39;: &#39;2022-01-01 07:00&#39;,
       &#39;begin_date_mpt&#39;: &#39;2022-01-01 00:00&#39;,
       &#39;metered_volume&#39;: &#39;0.0005865&#39;},
      {&#39;begin_date_utc&#39;: &#39;2022-01-01 08:00&#39;,
       &#39;begin_date_mpt&#39;: &#39;2022-01-01 01:00&#39;,
       &#39;metered_volume&#39;: &#39;0.0005363&#39;},
      {&#39;begin_date_utc&#39;: &#39;2022-01-01 09:00&#39;,
       &#39;begin_date_mpt&#39;: &#39;2022-01-01 02:00&#39;,
       &#39;metered_volume&#39;: &#39;0.0005209&#39;},
      {&#39;begin_date_utc&#39;: &#39;2022-01-01 10:00&#39;,
       &#39;begin_date_mpt&#39;: &#39;2022-01-01 03:00&#39;,
       &#39;metered_volume&#39;: &#39;0.0005171&#39;},
      {&#39;begin_date_utc&#39;: &#39;2022-01-01 11:00&#39;,
       &#39;begin_date_mpt&#39;: &#39;2022-01-01 04:00&#39;,
       &#39;metered_volume&#39;: &#39;0.0005152&#39;},
      {&#39;begin_date_utc&#39;: &#39;2022-01-01 12:00&#39;,
       &#39;begin_date_mpt&#39;: &#39;2022-01-01 05:00&#39;,
       &#39;metered_volume&#39;: &#39;0.0005104&#39;},
      {&#39;begin_date_utc&#39;: &#39;2022-01-01 13:00&#39;,
       &#39;begin_date_mpt&#39;: &#39;2022-01-01 06:00&#39;,
       &#39;metered_volume&#39;: &#39;0.0005164&#39;},
      {&#39;begin_date_utc&#39;: &#39;2022-01-01 14:00&#39;,
       &#39;begin_date_mpt&#39;: &#39;2022-01-01 07:00&#39;,
       &#39;metered_volume&#39;: &#39;0.0005426&#39;},
      {&#39;begin_date_utc&#39;: &#39;2022-01-01 15:00&#39;,
       &#39;begin_date_mpt&#39;: &#39;2022-01-01 08:00&#39;,
       &#39;metered_volume&#39;: &#39;0.0005907&#39;},
      {&#39;begin_date_utc&#39;: &#39;2022-01-01 16:00&#39;,
       &#39;begin_date_mpt&#39;: &#39;2022-01-01 09:00&#39;,
       &#39;metered_volume&#39;: &#39;0.0006283&#39;},
      {&#39;begin_date_utc&#39;: &#39;2022-01-01 17:00&#39;,
       &#39;begin_date_mpt&#39;: &#39;2022-01-01 10:00&#39;,
       &#39;metered_volume&#39;: &#39;0.0006528&#39;},
      {&#39;begin_date_utc&#39;: &#39;2022-01-01 18:00&#39;,
       &#39;begin_date_mpt&#39;: &#39;2022-01-01 11:00&#39;,
       &#39;metered_volume&#39;: &#39;0.0007141&#39;},
      {&#39;begin_date_utc&#39;: &#39;2022-01-01 19:00&#39;,
       &#39;begin_date_mpt&#39;: &#39;2022-01-01 12:00&#39;,
       &#39;metered_volume&#39;: &#39;0.0007192&#39;},
      {&#39;begin_date_utc&#39;: &#39;2022-01-01 20:00&#39;,
       &#39;begin_date_mpt&#39;: &#39;2022-01-01 13:00&#39;,
       &#39;metered_volume&#39;: &#39;0.0007495&#39;},
      {&#39;begin_date_utc&#39;: &#39;2022-01-01 21:00&#39;,
       &#39;begin_date_mpt&#39;: &#39;2022-01-01 14:00&#39;,
       &#39;metered_volume&#39;: &#39;0.0006842&#39;},
      {&#39;begin_date_utc&#39;: &#39;2022-01-01 22:00&#39;,
       &#39;begin_date_mpt&#39;: &#39;2022-01-01 15:00&#39;,
       &#39;metered_volume&#39;: &#39;0.0006804&#39;},
      {&#39;begin_date_utc&#39;: &#39;2022-01-01 23:00&#39;,
       &#39;begin_date_mpt&#39;: &#39;2022-01-01 16:00&#39;,
       &#39;metered_volume&#39;: &#39;0.0007282&#39;},
      {&#39;begin_date_utc&#39;: &#39;2022-01-02 00:00&#39;,
       &#39;begin_date_mpt&#39;: &#39;2022-01-01 17:00&#39;,
       &#39;metered_volume&#39;: &#39;0.0008322&#39;},
      {&#39;begin_date_utc&#39;: &#39;2022-01-02 01:00&#39;,
       &#39;begin_date_mpt&#39;: &#39;2022-01-01 18:00&#39;,
       &#39;metered_volume&#39;: &#39;0.0008516&#39;},
      {&#39;begin_date_utc&#39;: &#39;2022-01-02 02:00&#39;,
       &#39;begin_date_mpt&#39;: &#39;2022-01-01 19:00&#39;,
       &#39;metered_volume&#39;: &#39;0.0007729&#39;},
      {&#39;begin_date_utc&#39;: &#39;2022-01-02 03:00&#39;,
       &#39;begin_date_mpt&#39;: &#39;2022-01-01 20:00&#39;,
       &#39;metered_volume&#39;: &#39;0.0006861&#39;},
      {&#39;begin_date_utc&#39;: &#39;2022-01-02 04:00&#39;,
       &#39;begin_date_mpt&#39;: &#39;2022-01-01 21:00&#39;,
       &#39;metered_volume&#39;: &#39;0.0006861&#39;},
      {&#39;begin_date_utc&#39;: &#39;2022-01-02 05:00&#39;,
       &#39;begin_date_mpt&#39;: &#39;2022-01-01 22:00&#39;,
       &#39;metered_volume&#39;: &#39;0.0006434&#39;},
      {&#39;begin_date_utc&#39;: &#39;2022-01-02 06:00&#39;,
       &#39;begin_date_mpt&#39;: &#39;2022-01-01 23:00&#39;,
       &#39;metered_volume&#39;: &#39;0.0005783&#39;}]},
    {&#39;asset_ID&#39;: &#39;941C&#39;,
     &#39;asset_class&#39;: &#39;RETAILER&#39;,
     &#39;metered_volume_list&#39;: [{&#39;begin_date_utc&#39;: &#39;2022-01-01 07:00&#39;,
       &#39;begin_date_mpt&#39;: &#39;2022-01-01 00:00&#39;,
       &#39;metered_volume&#39;: &#39;0&#39;},
      {&#39;begin_date_utc&#39;: &#39;2022-01-01 08:00&#39;,
       &#39;begin_date_mpt&#39;: &#39;2022-01-01 01:00&#39;,
       &#39;metered_volume&#39;: &#39;0&#39;},
      {&#39;begin_date_utc&#39;: &#39;2022-01-01 09:00&#39;,
       &#39;begin_date_mpt&#39;: &#39;2022-01-01 02:00&#39;,
       &#39;metered_volume&#39;: &#39;0&#39;},
      {&#39;begin_date_utc&#39;: &#39;2022-01-01 10:00&#39;,
       &#39;begin_date_mpt&#39;: &#39;2022-01-01 03:00&#39;,
       &#39;metered_volume&#39;: &#39;0&#39;},
      {&#39;begin_date_utc&#39;: &#39;2022-01-01 11:00&#39;,
       &#39;begin_date_mpt&#39;: &#39;2022-01-01 04:00&#39;,
       &#39;metered_volume&#39;: &#39;0&#39;},
      {&#39;begin_date_utc&#39;: &#39;2022-01-01 12:00&#39;,
       &#39;begin_date_mpt&#39;: &#39;2022-01-01 05:00&#39;,
       &#39;metered_volume&#39;: &#39;0&#39;},
      {&#39;begin_date_utc&#39;: &#39;2022-01-01 13:00&#39;,
       &#39;begin_date_mpt&#39;: &#39;2022-01-01 06:00&#39;,
       &#39;metered_volume&#39;: &#39;0&#39;},
      {&#39;begin_date_utc&#39;: &#39;2022-01-01 14:00&#39;,
       &#39;begin_date_mpt&#39;: &#39;2022-01-01 07:00&#39;,
       &#39;metered_volume&#39;: &#39;0&#39;},
      {&#39;begin_date_utc&#39;: &#39;2022-01-01 15:00&#39;,
       &#39;begin_date_mpt&#39;: &#39;2022-01-01 08:00&#39;,
       &#39;metered_volume&#39;: &#39;0&#39;},
      {&#39;begin_date_utc&#39;: &#39;2022-01-01 16:00&#39;,
       &#39;begin_date_mpt&#39;: &#39;2022-01-01 09:00&#39;,
       &#39;metered_volume&#39;: &#39;0&#39;},
      {&#39;begin_date_utc&#39;: &#39;2022-01-01 17:00&#39;,
       &#39;begin_date_mpt&#39;: &#39;2022-01-01 10:00&#39;,
       &#39;metered_volume&#39;: &#39;0&#39;},
      {&#39;begin_date_utc&#39;: &#39;2022-01-01 18:00&#39;,
       &#39;begin_date_mpt&#39;: &#39;2022-01-01 11:00&#39;,
       &#39;metered_volume&#39;: &#39;0&#39;},
      {&#39;begin_date_utc&#39;: &#39;2022-01-01 19:00&#39;,
       &#39;begin_date_mpt&#39;: &#39;2022-01-01 12:00&#39;,
       &#39;metered_volume&#39;: &#39;0&#39;},
      {&#39;begin_date_utc&#39;: &#39;2022-01-01 20:00&#39;,
       &#39;begin_date_mpt&#39;: &#39;2022-01-01 13:00&#39;,
       &#39;metered_volume&#39;: &#39;0&#39;},
      {&#39;begin_date_utc&#39;: &#39;2022-01-01 21:00&#39;,
       &#39;begin_date_mpt&#39;: &#39;2022-01-01 14:00&#39;,
       &#39;metered_volume&#39;: &#39;0&#39;},
      {&#39;begin_date_utc&#39;: &#39;2022-01-01 22:00&#39;,
       &#39;begin_date_mpt&#39;: &#39;2022-01-01 15:00&#39;,
       &#39;metered_volume&#39;: &#39;0&#39;},
      {&#39;begin_date_utc&#39;: &#39;2022-01-01 23:00&#39;,
       &#39;begin_date_mpt&#39;: &#39;2022-01-01 16:00&#39;,
       &#39;metered_volume&#39;: &#39;0&#39;},
      {&#39;begin_date_utc&#39;: &#39;2022-01-02 00:00&#39;,
       &#39;begin_date_mpt&#39;: &#39;2022-01-01 17:00&#39;,
       &#39;metered_volume&#39;: &#39;0&#39;},
      {&#39;begin_date_utc&#39;: &#39;2022-01-02 01:00&#39;,
       &#39;begin_date_mpt&#39;: &#39;2022-01-01 18:00&#39;,
       &#39;metered_volume&#39;: &#39;0&#39;},
      {&#39;begin_date_utc&#39;: &#39;2022-01-02 02:00&#39;,
       &#39;begin_date_mpt&#39;: &#39;2022-01-01 19:00&#39;,
       &#39;metered_volume&#39;: &#39;0&#39;},
      {&#39;begin_date_utc&#39;: &#39;2022-01-02 03:00&#39;,
       &#39;begin_date_mpt&#39;: &#39;2022-01-01 20:00&#39;,
       &#39;metered_volume&#39;: &#39;0&#39;},
      {&#39;begin_date_utc&#39;: &#39;2022-01-02 04:00&#39;,
       &#39;begin_date_mpt&#39;: &#39;2022-01-01 21:00&#39;,
       &#39;metered_volume&#39;: &#39;0&#39;},
      {&#39;begin_date_utc&#39;: &#39;2022-01-02 05:00&#39;,
       &#39;begin_date_mpt&#39;: &#39;2022-01-01 22:00&#39;,
       &#39;metered_volume&#39;: &#39;0&#39;},
      {&#39;begin_date_utc&#39;: &#39;2022-01-02 06:00&#39;,
       &#39;begin_date_mpt&#39;: &#39;2022-01-01 23:00&#39;,
       &#39;metered_volume&#39;: &#39;0&#39;}]},

When I use the following code:

df1 = pd.json_normalize(df[&#39;return&#39;])

The dataset looks like the following:

I would like to convert the asset_list column into its own dataframe. Where asset_ID, asset_class, begin_date_utc, begin_date_mpt and metered_volume` are column. How would I go about this?

答案1

得分: 3

使用 json_normalize()，您需要映射 meta 和 record_path 中的级别：

代码：

df = pd.json_normalize(
    data=data,
    meta=[
        ["return", "pool_participant_ID"],
        ["return", "asset_list", "asset_ID"],
        ["return", "asset_list", "asset_class"],
    ],
    record_path=["return", "asset_list", "metered_volume_list"]
).rename(columns=lambda x: x.split(".")[-1])
print(df)

输出：

      begin_date_utc    begin_date_mpt metered_volume pool_participant_ID asset_ID asset_class
0   2022-01-01 07:00  2022-01-01 00:00      0.0005865                9496     941A    RETAILER
1   2022-01-01 08:00  2022-01-01 01:00      0.0005363                9496     941A    RETAILER
2   2022-01-01 09:00  2022-01-01 02:00      0.0005209                9496     941A    RETAILER
3   2022-01-01 10:00  2022-01-01 03:00      0.0005171                9496     941A    RETAILER
4   2022-01-01 11:00  2022-01-01 04:00      0.0005152                9496     941A    RETAILER
5   2022-01-01 12:00  2022-01-01 05:00      0.0005104                9496     941A    RETAILER
6   2022-01-01 13:00  2022-01-01 06:00      0.0005164                9496     941A    RETAILER
7   2022-01-01 14:00  2022-01-01 07:00      0.0005426                9496     941A    RETAILER
8   2022-01-01 15:00  2022-01-01 08:00      0.0005907                9496     941A    RETAILER
9   2022-01-01 16:00  2022-01-01 09:00      0.0006283                9496     941A    RETAILER
10  2022-01-01 17:00  2022-01-01 10:00      0.0006528                9496     941A    RETAILER
11  2022-01-01 18:00  2022-01-01 11:00      0.0007141                9496     941A    RETAILER
12  2022-01-01 19:00  2022-01-01 12:00      0.0007192                9496     941A    RETAILER
13  2022-01-01 20:00  2022-01-01 13:00      0.0007495                9496     941A    RETAILER
14  2022-01-01 21:00  2022-01-01 14:00      0.0006842                9496     941A    RETAILER
15  2022-01-01 22:00  2022-01-01 15:00      0.0006804                9496     941A    RETAILER
16  2022-01-01 23:00  2022-01-01 16:00      0.0007282                9496     941A    RETAILER
17  2022-01-02 00:00  2022-01-01 17:00      0.0008322                9496     941A    RETAILER
18  2022-01-02 01:00  2022-01-01 18:00      0.0008516                9496     941A    RETAILER
19  2022-01-02 02:00  2022-01-01 19:00      0.0007729                9496     941A    RETAILER
20  2022-01-02 03:00  2022-01-01 20:00      0.0006861                9496     941A    RETAILER
21  2022-01-02 04:00  2022-01-01 21:00      0.0006861                9496     941A    RETAILER
22  2022-01-02 05:00  2022-01-01 22:00      0.0006434                9496     941A    RETAILER
23  2022-01-02 06:00  2022-01-01 23:00      0.0005783                9496     941A    RETAILER
24  2022-01-01 07:00  2022-01-01 00:00              0                9496     941C    RETAILER
25  2022-01-01 08:00  2022-01-01 01:00              0                9496     941C    RETAILER
26  2022-01-01 09:00  2022-01-01 02:00              0                9496     941C    RETAILER
27  2022-01-01 10:00  2022-01-01 03:00              0                9496     941C    RETAILER
28  2022-01-01 11:00  2022-01-01 04:00              0                9496     941C    RETAILER
29  2022-01-01 12:00  2022-01-01 05:00              0                9496     941C    RETAILER
30  2022-01-01 13:00  2022-01-01 06:00              0                9496     941C    RETAILER
31  2022-01-01 14:00  2022-01-01 07:00              0                9496    

<details>
<summary>英文:</summary>

Using [json_normalize()][1] You need to map the levels in meta and record_path:

Code:

    df = pd.json_normalize(
        data=data,
        meta=[
            [&quot;return&quot;, &quot;pool_participant_ID&quot;],
            [&quot;return&quot;, &quot;asset_list&quot;, &quot;asset_ID&quot;],
            [&quot;return&quot;, &quot;asset_list&quot;, &quot;asset_class&quot;],
        ],
        record_path=[&quot;return&quot;, &quot;asset_list&quot;, &quot;metered_volume_list&quot;]
    ).rename(columns=lambda x: x.split(&quot;.&quot;)[-1])
    print(df)


Output:

          begin_date_utc    begin_date_mpt metered_volume pool_participant_ID asset_ID asset_class
    0   2022-01-01 07:00  2022-01-01 00:00      0.0005865                9496     941A    RETAILER
    1   2022-01-01 08:00  2022-01-01 01:00      0.0005363                9496     941A    RETAILER
    2   2022-01-01 09:00  2022-01-01 02:00      0.0005209                9496     941A    RETAILER
    3   2022-01-01 10:00  2022-01-01 03:00      0.0005171                9496     941A    RETAILER
    4   2022-01-01 11:00  2022-01-01 04:00      0.0005152                9496     941A    RETAILER
    5   2022-01-01 12:00  2022-01-01 05:00      0.0005104                9496     941A    RETAILER
    6   2022-01-01 13:00  2022-01-01 06:00      0.0005164                9496     941A    RETAILER
    7   2022-01-01 14:00  2022-01-01 07:00      0.0005426                9496     941A    RETAILER
    8   2022-01-01 15:00  2022-01-01 08:00      0.0005907                9496     941A    RETAILER
    9   2022-01-01 16:00  2022-01-01 09:00      0.0006283                9496     941A    RETAILER
    10  2022-01-01 17:00  2022-01-01 10:00      0.0006528                9496     941A    RETAILER
    11  2022-01-01 18:00  2022-01-01 11:00      0.0007141                9496     941A    RETAILER
    12  2022-01-01 19:00  2022-01-01 12:00      0.0007192                9496     941A    RETAILER
    13  2022-01-01 20:00  2022-01-01 13:00      0.0007495                9496     941A    RETAILER
    14  2022-01-01 21:00  2022-01-01 14:00      0.0006842                9496     941A    RETAILER
    15  2022-01-01 22:00  2022-01-01 15:00      0.0006804                9496     941A    RETAILER
    16  2022-01-01 23:00  2022-01-01 16:00      0.0007282                9496     941A    RETAILER
    17  2022-01-02 00:00  2022-01-01 17:00      0.0008322                9496     941A    RETAILER
    18  2022-01-02 01:00  2022-01-01 18:00      0.0008516                9496     941A    RETAILER
    19  2022-01-02 02:00  2022-01-01 19:00      0.0007729                9496     941A    RETAILER
    20  2022-01-02 03:00  2022-01-01 20:00      0.0006861                9496     941A    RETAILER
    21  2022-01-02 04:00  2022-01-01 21:00      0.0006861                9496     941A    RETAILER
    22  2022-01-02 05:00  2022-01-01 22:00      0.0006434                9496     941A    RETAILER
    23  2022-01-02 06:00  2022-01-01 23:00      0.0005783                9496     941A    RETAILER
    24  2022-01-01 07:00  2022-01-01 00:00              0                9496     941C    RETAILER
    25  2022-01-01 08:00  2022-01-01 01:00              0                9496     941C    RETAILER
    26  2022-01-01 09:00  2022-01-01 02:00              0                9496     941C    RETAILER
    27  2022-01-01 10:00  2022-01-01 03:00              0                9496     941C    RETAILER
    28  2022-01-01 11:00  2022-01-01 04:00              0                9496     941C    RETAILER
    29  2022-01-01 12:00  2022-01-01 05:00              0                9496     941C    RETAILER
    30  2022-01-01 13:00  2022-01-01 06:00              0                9496     941C    RETAILER
    31  2022-01-01 14:00  2022-01-01 07:00              0                9496     941C    RETAILER
    32  2022-01-01 15:00  2022-01-01 08:00              0                9496     941C    RETAILER
    33  2022-01-01 16:00  2022-01-01 09:00              0                9496     941C    RETAILER
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  [1]: https://pandas.pydata.org/docs/reference/api/pandas.json_normalize.html

</details>



# 答案2
**得分**: 2

你可以尝试这样做。对于列表部分使用 `explode`，然后再次对嵌套对象使用 `json_normalize`。

```python
df = (pd.json_normalize(df['return'], record_path=['asset_list'])
      .explode('metered_volume_list'))

df = pd.concat([df[['asset_ID', 'asset_class']].reset_index(drop=True), 
                pd.json_normalize(df.metered_volume_list)], axis=1)

英文:

you can try this. Use explode for the list and use json_normalize again on the nested object.

df = (pd.json_normalize(df[&#39;return&#39;], record_path=[&#39;asset_list&#39;])
.explode(&#39;metered_volume_list&#39;))
df = pd.concat([df[[&#39;asset_ID&#39;, &#39;asset_class&#39;]].reset_index(drop=True), 
pd.json_normalize(df.metered_volume_list)], axis=1)

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