A Python Azure Cloud Service that gathers data from PyPI and uploads it to Azure Table Storage.
This was created to do some research on Python 3 support levels. If you want to read my take on things you can read the blog on Microsoft Python Engineering.
Using the Collected Data
The easiest way to use the collected data from my service is to view the Jupyter Notebook on Cortana Analytics Gallery.
If you would rather run it locally though, you can download the Azure SDK from PyPI and use the following code:
from azure.storage.table import TableService account_name='pypidata' config_sas='se=2030-01-01&sp=r&sv=2014-02-14&sig=cbluw1yeoAnmXSGtMbUM9dOmDgndoFnjSpeTAoz5Zls%3D&tn=config' packagesummarydata_sas='se=2030-01-01&sp=r&sv=2014-02-14&sig=JbQiFfxRRqJqUn7lyyoY8ek2fC3r7%2Bb9rndXlGBvhwI%3D&tn=packagesummarydata' packageversiondata_sas='se=2030-01-01&sp=r&sv=2014-02-14&sig=OpQRTKCCr7Bp%2BoFv%2BpElQ%2BF/fhA3HEiLHQfFb7bJy5o%3D&tn=packageversiondata' config_table_service = TableService(account_name, sas_token=config_sas) packagesummarydata_table_service = TableService(account_name, sas_token=packagesummarydata_sas) packageversiondata_table_service = TableService(account_name, sas_token=packageversiondata_sas)
Collecting the Data
If you prefer to collect the data yourself you will need an Azure subscription.
- Create a storage account on Azure
- Input the storage account info to the variables
- Deploy the cloud service to your account.
That should allow you to collect the data yourself.