英文:
Python Create dataframe from nested dict with lists
问题
| App | id | stages | requestCpu | requestMemory |
|---|---|---|---|---|
| appName | 123 | dev | 1000 | 1024 |
| appName | 123 | staging | 3200 | 1024 |
test_data = {"appName": {"id": "123", "stages": {"dev": [{"request.cpu": 1000}, {"request.memory": 1024}], "staging": [{"request.cpu": 3200}, {"request.memory": 1024}]}}, "appName2"...}
以前我使用了类似这样的方法：
df = pd.DataFrame.from_dict(test_data, orient='index')df = pd.concat([df.drop(['stages'], axis=1), (df['stages'].apply(pd.Series))], axis=1)df.index.name = "App"
然而，这无法拆分列表部分，而且现在各个阶段都在列中，不是我想要的样子。
英文:
I am trying to create a dataframe / csv that looks like this
| App | id | stages | requestCpu | requestMemory |
|---|---|---|---|---|
| appName | 123 | dev | 1000 | 1024 |
| appName | 123 | staging | 3200 | 1024 |
The dict data looks like this and includes quite a lot of apps, however all the data inside the apps looks the same with the dict layout:
test_data = {"appName": {"id": "123", "stages": {"dev": [{"request.cpu": 1000}, {"request.memory": 1024}], "staging": [{"request.cpu": 3200}, {"request.memory": 1024}]}}, "appName2"...}
I used something like this before:
df = pd.DataFrame.from_dict(test_data, orient='index')df = pd.concat([df.drop(['stages'], axis=1), (df['stages'].apply(pd.Series))], axis=1)df.index.name = "App"
However this wasn't able to split up the list part and also the stages were now in columns so not how i wanted it to look..
Any help much appreciated, thanks
答案1
得分: 0
以下是翻译好的部分：
Easiest solution would be to iterate the rows prior to loading it with pandas:import pandas as pdtest_data = {"appName": {"id": "123", "stages": {"dev": [{"request.cpu": 1000}, {"request.memory": 1024}], "staging": [{"request.cpu": 3200}, {"request.memory": 1024}]}, "appName2": {"id": "456", "stages": {"dev": [{"request.cpu": 1000}, {"request.memory": 1024}], "staging": [{"request.cpu": 3200}, {"request.memory": 1024}]}}rows = []for app, app_data in test_data.items():for stage, stage_data in app_data["stages"].items():row = {"App": app,"id": app_data["id"],"stages": stage}for metric in stage_data:metric_name, metric_value = list(metric.items())[0]row[metric_name] = metric_valuerows.append(row)df = pd.json_normalize(rows)# Reorder columnsdf = df[["App", "id", "stages", "request.cpu", "request.memory"]]
Output:
| App | id | stages | request.cpu | request.memory | |
|---|---|---|---|---|---|
| 0 | appName | 123 | dev | 1000 | 1024 |
| 1 | appName | 123 | staging | 3200 | 1024 |
| 2 | appName2 | 456 | dev | 1000 | 1024 |
| 3 | appName2 | 456 | staging | 3200 | 1024 |
<details><summary>英文:</summary>Easiest solution would be to iterate the rows prior to loading it with pandas:import pandas as pdtest_data = {"appName": {"id": "123", "stages": {"dev": [{"request.cpu": 1000}, {"request.memory": 1024}], "staging": [{"request.cpu": 3200}, {"request.memory": 1024}]}}, "appName2": {"id": "456", "stages": {"dev": [{"request.cpu": 1000}, {"request.memory": 1024}], "staging": [{"request.cpu": 3200}, {"request.memory": 1024}]}}}rows = []for app, app_data in test_data.items():for stage, stage_data in app_data["stages"].items():row = {"App": app,"id": app_data["id"],"stages": stage}for metric in stage_data:metric_name, metric_value = list(metric.items())[0]row[metric_name] = metric_valuerows.append(row)df = pd.json_normalize(rows)# Reorder columnsdf = df[["App", "id", "stages", "request.cpu", "request.memory"]]Output:| | App | id | stages | request.cpu | request.memory ||---:|:---------|-----:|:---------|--------------:|-----------------:|| 0 | appName | 123 | dev | 1000 | 1024 || 1 | appName | 123 | staging | 3200 | 1024 || 2 | appName2 | 456 | dev | 1000 | 1024 || 3 | appName2 | 456 | staging | 3200 | 1024 |</details>
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