ecom | project for ecommerce lecture | Ecommerce library

You could try adding the index to your for loop and making it part of your name, might help you avoid elipsis/tuple conversion.

your fetch syntax should have a comma(,) after the method and headers

the correct syntax is method: "POST", headers: {.....}, body:JSON.stringify({....})

From your description it sounds like your problem could be modeled more directly as a map of objects where each element represents the settings for a particular availability zone:

In your configuration you've declared that data "aws_instances" "ecom-instances" depends on aws_instance.ecom-validation-service. Since that other object won't exist yet on your first run, Terraform must therefore wait until the apply step to read data.aws_instances.ecom-instances because otherwise it would fail to honor the dependency you've declared, because aws_instance.ecom-validation-service wouldn't exist yet.

To avoid the error message you saw here, you need to make sure that for_each only refers to values that Terraform will know before any objects are actually created. Because EC2 assigns instance ids only once the instance is created, it's not correct to use an EC2 instance id as part of a for_each instance key.

Furthermore, there's no need for a data "aws_instances" block to retrieve instance information here because you already have the relevant instance information as a result of the resource "aws_instance" "ecom-validation-service" block.

With all of that said, let's start from your input variables and build things up again while making sure that we only build instance keys only from values we'll know during planning. The variables you have stay essentially the same; I've just tweaked the type constraints a little to match how we're using each one:

If I understand correctly, you can construct a helper local variable and use that:

You can create a helper variable and use setproduct:

Since you are setting keep=False all duplicates are being dropped on the df.drop_duplicates method, you need to set keep="first" or "last" in order to keep those duplicate entries.

You need to convert Order_Date to dates - stripping the time information. One quick way to do this is:

df['Order_Date1'] = pd.to_datetime(df['Order_Date']).dt.strftime('%Y-%m-%d')

Then proceed with a groupby using Order_Date1.

The below code worked