Azure / Azure/MachineLearningNotebooks

register_pandas_dataframe crashes kernel when receiving dataframe with multiple columns with same name

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描述

My Synapse kernel crashed when I tried to register a dataset where the dataframe had multiple columns with the same name. I am not completely sure if this is the correct place to post this, but this is the best place I could find.

Minimum reproducible example:
```python
dataset = Dataset.Tabular.register_pandas_dataframe(
dataframe = pd.DataFrame([[1,2],[1,2]], columns=['a', 'a']),
target = datastore,
name = "bad_dataframe",
show_progress=True,
)
```
What I would expect:
To get some feedback about why it fails, and the kernel should not crash.

What I get:
```
Validating arguments.
Arguments validated.
Successfully obtained datastore reference and path.
Uploading file to managed-dataset/13e4b3c6-8730-4951-bde5-2d2bc6456e2d/
InternalError: Ipython kernel exits with code -11. Please restart your session.
```

---
#### Document Details

⚠ *Do not edit this section. It is required for docs.microsoft.com ➟ GitHub issue linking.*

* ID: 9b1aee32-5a1f-2ef1-ee76-30b17999db87
* Version Independent ID: f4785272-a81d-e9cc-c6a5-9d41c8dcd90b
* Content: [azureml.data.dataset_factory.TabularDatasetFactory class - Azure Machine Learning Python](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.dataset_factory.tabulardatasetfactory?view=azure-ml-py)
* Content Source: [AzureML-Docset/stable/docs-ref-autogen/azureml-core/azureml.data.dataset_factory.TabularDatasetFactory.yml](https://github.com/MicrosoftDocs/MachineLearning-Python-pr/blob/live/AzureML-Docset/stable/docs-ref-autogen/azureml-core/azureml.data.dataset_factory.TabularDatasetFactory.yml)
* Service: **machine-learning**
* Sub-service: **core**
* GitHub Login: @DebFro
* Microsoft Alias: **debfro**

貢獻指南

這個儲存庫沒有索引到貢獻指南

研究方向

首先執行 issue 中使用重複欄位名稱的 pandas DataFrame 範例,並觀察 kernel 結束。該 payload 指出了 AzureML-Docset/stable/docs-ref-autogen/azureml-core/azureml.data.dataset_factory.tabulardatasetfactory.yml 中的 TabularDatasetFactory 文件來源,但沒有指定實作檔案或測試。完成的標準是:失敗時返回有用的回饋,而不會導致 IPython kernel 當機。

由索引模型根據 Issue 內容生成。

評估

技術堆疊
azure, jupyter-notebook, pandas, python
領域
data, machine-learning
Issue 類型
缺陷
難度
4/5
預估耗時
3-5 天
活躍度
停滯
描述清晰度
基本清楚
新手友好度
30/100

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