This script extracts metadata from a Parquet file stored in Azure Blob Storage, processes its contents, and uploads the results back to Azure Blob Storage. Here's a brief overview of its functionality:
- Connects to Azure Blob Storage using a connection string
- Downloads a Parquet file from the 'raw' container
- Extracts metadata, labels, and images from the Parquet file
- Uploads the extracted information to the 'out' container
- BlobServiceClient: Used to interact with Azure Blob Storage[1][3]
- pandas: Used to read and process the Parquet file
- PIL (Python Imaging Library): Used to handle image data
- argparse: Enables command-line arguments for input blob and output prefix
This function performs the following tasks:
- Downloads the Parquet file from the input container
- Reads the Parquet file into a pandas DataFrame
- Extracts and uploads DataFrame info as a text file
- Extracts and uploads labels as a CSV file
- Extracts and uploads individual images as PNG files
Run the script with the following command-line arguments:
python script_name.py --inputBlob <input_blob_name> --outputPrefix <output_prefix>
The connection string in the script contains sensitive information. It's recommended to use more secure methods for handling credentials, such as environment variables or Azure Key Vault[2].
Citations: [1] https://github.com/Huachao/azure-content/blob/master/articles/storage/storage-python-how-to-use-blob-storage.md [2] https://learn.microsoft.com/en-us/azure/storage/blobs/storage-quickstart-blobs-python [3] https://learn.microsoft.com/en-us/python/api/overview/azure/storage-blob-readme?view=azure-python [4] https://www.youtube.com/watch?v=bX-SSTCe2CY [5] https://learn.microsoft.com/en-us/azure/storage/blobs/storage-blob-python-get-started?tabs=azure-ad [6] https://learn.microsoft.com/en-in/azure/storage/blobs/storage-blob-upload-python [7] https://stackoverflow.com/questions/48881228/azure-blob-read-using-python [8] https://learn.microsoft.com/en-us/azure/storage/blobs/storage-blobs-list-python
generated with perplexity.ai with the following prompt:
generate a very short and simple doc for this script to be kept as markdown in the repo: extract_parquet_sas.py