Blog > Python

Using Azure Functions with Python

Using Azure Functions with Python


Lately, I worked a lot with the Azure Cloud. Overall I have to say Azure offers a lot but is still not on the same level as its hardest competitors (AWS, Google). One thing that caught my eye is the compatibility of certain programming languages. Azure supports a few different languages (C#, JavaScript, Java, Python, etc.) but the supported features for these languages differ a lot. I think Azure Cloud is really great for coding with C# but there is a lack of features for Python.

If we look at Azure Functions (serverless computing like AWS Lambda) we see that there is no support for editing functions in the portal with Python, we have to use the CLI. Also, there is quite a lack of documentation for different scenarios (Blobtrigger, etc.) using Python. So with this post, I want to save you some time, if you want to implement a Python function (with a blobtrigger) on the Azure Cloud and hopefully, you can learn from my errors.

A blobtrigger function listens to an Azure BlobStorage and is activated every time a new file is added or updated.

1. Set up local environment

I expect you have Python (3.6 or 3.7; 3.8 is not yet supported) installed.

Azure function core tools

First, we need the Azure Function Core Tools. On macOS we can easily install it with homebrew:

brew tap azure/functions

brew install azure-functions-core-tools

For Windows you need node installed, refer to

Azure CLI

Additionally, we need the Azure CLI installed. Again using homebrew:

brew update && brew install azure-cli

For Windows using PowerShell in Administrator mode:

Invoke-WebRequest -Uri -OutFile .\AzureCLI.msi; Start-Process msiexec.exe -Wait -ArgumentList '/I AzureCLI.msi /quiet'

To verify that the installation was successful:

az --version

Continue to login:

az login

A browser window will pop up and you should now login with your Azure account.


To not mess up your system python environment, let's use virtualenv. You will need this environment if you want to develop your function further.

Create the env:

python -m venv azure_functions

Activate the env:

source azure_function/bin/activate

Local function project

Now you can create a local function project. A function project is a container for one or more functions which all share the same configuration.

To create a function project:

func init BlogFunctionTest --python

Navigate into the folder:

cd BlogFunctionTest

Now you can create the actual function in the project, let’s use the blob trigger template.

func new --name BlobTriggerTestPython --template AzureBlobStorageTrigger

This will create a new folder. The most important files are `` and `function.json`. Let’s have a quick look at both. Change your files to look like the following:

def main(myblob: func.InputStream):"Python blob trigger function processed blob \n"

f"Name: {}\n"

f"Blob Size: {myblob.length} bytes")

file =

This file is the default starting point for your function. You can read your blob file with `.read()`

A full reference to the `func.InputStream` can be found here:


"scriptFile": "",

"bindings": [


"name": "myblob",

"type": "blobTrigger",

"direction": "in",

"path": "data/{name}",

"connection": "MyStorageConnectionString"



This is the configuration file for your function.

  • `scriptFile` allows you to invoke another Python file.
  • `type` defines the type of the trigger
  • `direction` defines if its an inward or outward trigger (in/out)
  • `path` this option defines the path for the blob storage where we are listening to. Currently, we are listening to all new files created in the blob storage path “data/”.
  • `connection` is a ConnectionString that defines the connection to your blob storage. You will define this String as an environmental variable later on.

2. Create an Azure function app

Now that you have set up your local environment, you need to create a new Function App on Azure. You can either do this using the web client ( or via CLI. I will only go through the CLI process because I think the Web Client way is self-explanatory. Everything you create here, you can also create with the help of the Web Client and you should get the same results.

2.1 Make sure you are logged in:

az login

2.2 You can switch subscriptions with:

az account set --subscription <SUBSCRIPTION_ID>

If you don’t know your subscription ids:

az account list

2.3 Create a resource group named PythonTriggerTest in the west Europe region (or any region you like).

az group create --name PythonTriggerTest --location westeurope

2.4 You need to create a general-purpose storage account in your resource group and region. The Function App needs this storage to maintain state and other information about your projects. The Storage name has to be globally unique. Also, you will use this storage account as your blob storage.

az storage account create --name <STORAGE_NAME> --location westeurope --resource-group PythonTriggerTest --sku Standard_LRS

2.5 After that, create the Functions app, replace <STORAGE_NAME> with the chosen Storage name in the step before and choose a globally unique App name for <APP_NAME>.

az functionapp create --resource-group PythonTriggerTest --os-type Linux --consumption-plan-location westeurope --runtime python --runtime-version 3.6 --name <APP_NAME> --storage-account <STORAGE_NAME>

3. Create a blob storage container

Now it’s time to create a BlobStorage Container where you will be able to save your files, which you want to trigger your function from.

Again you can do that directly through the CLI.

Where <storage-account> is the account you just created in step 2.

az storage container create \

   --account-name <storage-account> \

   --name data \

   --auth-mode login

4. Pulling the strings together

Now that you created every resource needed, it is time to publish your function to Azure. `<APP_NAME>` is the globally unique name you set in step 2.5. Make sure you are inside your created function.

func azure functionapp publish <APP_NAME> --python

If you get an error about a missing requirements.txt file. Just make sure you are in the right folder inside `BlobFunctionTest` and not inside `BlogTriggerTestPython`.

Connecting Azure function and storage

In the last step, you have to give your Azure Function access to the Storage Account. To do that, go to and navigate to your storage account you previously created in this tutorial. Go to “Access keys” and copy the key1 Connection string.

Now navigate to your Azure Function App, and go to configuration. There create a new Application setting and paste the key you just copied as the value.

`Name: “MyStorageConnectionString”`


Don’t forget to click save. The Function App will now restart and the key will be available to your function as an environment variable.

5. Run it

To test the trigger you just built, upload a file to your blob storage:

Upload any file you want or just create a new one:

touch helloworld

echo 'Hello World!' > helloworld

az storage blob upload \

   --account-name <storage-account> \

   --container-name data \

   --name <filename> \

   --file <filename> \

   --auth-mode login

If you have problems with the authentication, another way is to use ` — auth-mode key`.

After you upload your file, you can open a browser, head to the Azure portal, navigate to your Function app and open Functions → BlobTriggerTestPython → Monitor. If you have done everything correctly you will see a successful run (It can take up to 5 minutes to show the run).

6. Clean up and conclusion

If you do not want to keep what you just built you can easily delete everything.

To avoid further costs, delete the resource group and all associated resources.

az group delete --name PythonTriggerTest

7. Side notes

There is quite a restrictive import management in place for python azure functions. To save you some time, have a look at:


Written by

Silas Stulz

Data Engineer. I write about Machine Learning, Big Data, and Web Technologies.