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Included link to the iris.csv dataset from Kaggle
Instructions make use of the dataset, however they did not previously provide the dataset, or a link to it.
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articles/batch/tutorial-run-python-batch-azure-data-factory.md

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@@ -29,6 +29,7 @@ If you don’t have an Azure subscription, create a [free account](https://azure
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* An installed [Python](https://www.python.org/downloads/) distribution, for local testing.
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* The [Azure](https://pypi.org/project/azure/) `pip` package.
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* The [iris.csv dataset](https://www.kaggle.com/uciml/iris/version/2#Iris.csv)
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* An Azure Batch account and a linked Azure Storage account. See [Create a Batch account](quick-create-portal.md#create-a-batch-account) for more information on how to create and link Batch accounts to storage accounts.
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* An Azure Data Factory account. See [Create a data factory](../data-factory/quickstart-create-data-factory-portal.md#create-a-data-factory) for more information on how to create a data factory through the Azure portal.
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* [Batch Explorer](https://azure.github.io/BatchExplorer/).
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1. Sign in to Storage Explorer using your Azure credentials.
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1. Using the storage account linked to your Batch account, create two blob containers (one for input files, one for output files) by following the steps at [Create a blob container](../vs-azure-tools-storage-explorer-blobs.md#create-a-blob-container).
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* In this example, we'll call our input container `input`, and our output container `output`.
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1. Upload `main.py` and `iris.csv` to your input container `input` using Storage Explorer by following the steps at [Managing blobs in a blob container](../vs-azure-tools-storage-explorer-blobs.md#managing-blobs-in-a-blob-container)
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1. Upload `main.py` and [`iris.csv`](https://www.kaggle.com/uciml/iris/version/2#Iris.csv) to your input container `input` using Storage Explorer by following the steps at [Managing blobs in a blob container](../vs-azure-tools-storage-explorer-blobs.md#managing-blobs-in-a-blob-container)
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## Develop a script in Python

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