Uploading Google API python library to Lambda

I have a Jupyter notebook that i've built a script in for extracting data from a Google Sheet using these two imports:

from googleapiclient.discovery import build
from google.oauth import service_account

I'm trying to copy it to AWS Lambda and I'm having trouble uploading these three libraries to a layer:

google-api-python-client

google-auth-httplib2

google-auth-oauthlib

I downloaded them from pypi.org. They all only have one download option and don't specify which version of python 3 they're compatible with, except google-api-python-client which has "Python 3.7, 3.8, 3.9, 3.10 and 3.11 are fully supported and tested." in the comments.

I just checked and it looks like my Jupyter notebook is running Python 3.10. I've also copied the script into VSCode and these libraries also appear to only work in Python 3.10. Which is weird since at least one of them should still work in all versions.
It makes me think i'm doing something wrong.

Also, it doesn't look like Lambda supports 3.10? So is there no way to run Google libraries on it? Or do I need to use older libraries?

If you don't have 3.9 locally, you can use Docker to run it inside a container and see which packages you need.

FROM amazon/aws-lambda-python:3.9

RUN pip install google-api-python-client google-auth-httplib2 google-auth-oauthlib

Build it:

docker build . --progress=plain

See logs:

> #5 24.65 Successfully installed cachetools-5.3.0 certifi-2022.12.7 charset-normalizer-3.0.1 google-api-core-2.11.0
> google-api-python-client-2.77.0 google-auth-2.16.0
> google-auth-httplib2-0.1.0 google-auth-oauthlib-1.0.0
> googleapis-common-protos-1.58.0 httplib2-0.21.0 idna-3.4
> oauthlib-3.2.2 protobuf-4.21.12 pyasn1-0.4.8 pyasn1-modules-0.2.8
> pyparsing-3.0.9 requests-2.28.2 requests-oauthlib-1.3.1 rsa-4.9
> six-1.16.0 uritemplate-4.1.1 urllib3-1.26.14

So your requirements.txt for Python 3.9 will look like:

google-api-python-client==2.77.0
google-auth-httplib2==0.1.0
google-auth-oauthlib==1.0.0

I recommend you work locally using the same version of Python and its packages. Docker is great for that!