Develop Reference

Is there any documentation about Office365 SSO using python?

I'm currently trying to develop an application to use ms-graph API using python.
I'm trying to use SSO authentication but I cant find any resources or documentation related.
I found this documentation https://learn.microsoft.com/en-us/office/dev/add-ins/develop/sso-in-office-add-ins
but it is only for javascript.
I wrote a script using Flask and another script using O365 python package.
It doesnt seem like its a Single sign on application that I try to achieve...
I'm looking for any documentation that could help.

You are referring to the Office web add-ins where you could also use SSO. But it seems you are developing a standalone application where Graph API is used. In that case you may find the Configure SAML-based single sign-on for your application using the Microsoft Graph API tutorial helpful.

WazirX API wrapper library

I am new to using APIs and was trying to build a trading bot in python using WazirX exchange's REST API. I am unable to understand how to make API calls using their documentation (Also, is this an example of a good API documentation or of a bad one?). Can anyone point to any python wrapper libraries through which I can execute trades using an API key and secret? I tried searching a lot but couldn't find any Github repository using their API to execute trades (I have seen some using the public endpoints only).
Any help would be appreciated.

Wazirx itself has provided Ruby and Python connectors
There are also 3rd party libraries available which can be used.
https://docs.wazirx.com/#api-third-party-integrations

Python in Google Cloud Functions

Can Google Cloud Functions handle python with packages like sklearn, pandas, etc? If so, can someone point me in the direction of resources on how to do so.
I've been searching a while and it seems like this is impossible, all I've found are resources to deploy the base python language to google cloud.

Python 3.7 is supported now.
Steps to create one via the google cloud console:
go to google cloud functions in the google cloud console and click on create function
2.specify the function's properties
select trigger
4.change runtime to python 3.7
enter your cloud function logic and entry point
enter python dependencies in requirements.txt

EDIT: As of July 2018 there is now a Python runtime (3.7) available for Google Cloud Functions!
OLD ANSWER: Google Cloud Functions (GCF) are written in JavaScript (executed in a Node.js runtime), so there is no way for them to actually handle Python at this moment. There is a Python module at GitHub that you might have come across and it can be used to write and deploy GCF with one of three trigger types: http, Pub/Sub and bucket. The module takes care of translating your Python logic to a JavaScript code that is later run inside Google Cloud Platform.
When it comes to other packages like pandas, the ‘translation’ into JavaScript was not prepared for them by anyone AFAIK. If you really don’t like the idea of jumping into JavaScript and writing the Cloud Function code on your own (with the logic you intended to use in a Python script), you have a possible workaround. You can evoke your Python script from inside of the Cloud Function written in JS - the idea was discussed in this topic. Another way is using Object Change Notifications or Pub/Sub Notifications as explained here.

As of 19th July 2018, Google Cloud Functions supports Python 3.7.
Kindly check the Runtime environment to find the Python 3.7 runtime and sample script (based on Flask) .
--UPDATED--
Official Documentation for the Google Cloud Functions - Python 3.7 support Beta Release.
This is a beta release of the Python runtime for Google Cloud
Functions. This feature might be changed in backward-incompatible ways
and is not subject to any SLA or deprecation policy.
SkLearn, Numpy is supported in Google Cloud function. Also I've run a sample test to confirm the availability of Pandas as well and its working fine.
https://github.com/mkanchwala/google-functions-python-example
Hope this helps to all the "Py" lovers.

You can use AWS lambda as well if you want to work around and still use Python as your main language. Some modules/packages will need to be imported via zip file with AWS Lambda but it has a broader range of usable languages than GCF

When accessing Bigquery using Python API, what is the difference of using google client API and gcloud

I searched for Python API to interact with google bigquery. And I found two packages provides similar APIs: Google BigQuery Client(Part of Google API Client Package googleapiclient) and Gcloud package gcloud.
Here are the documentation about using these two APIs for Bigquery:
Google API Client:googleapiclient
https://developers.google.com/resources/api-libraries/documentation/bigquery/v2/python/latest/index.html
https://cloud.google.com/bigquery/docs/reference/v2/
Google Cloud package: gcloud
http://googlecloudplatform.github.io/gcloud-python/stable/bigquery-usage.html
Both packages are from google, and provides similar functionalities interacting with bigquery. I have the following confusions:
It seems both package includes a wide range of functionalities of Google Cloud Platform. In my view, gcloud provides commandline tool and local environment setup. Generally, what are the differences of these two packages?
In terms of python module, what are the differences of their usage?
Is there any relation between these two packages?
Which is more suitable for accessing Bigquery?
What kind of job are they suitable for?

The googleapiclient client is generated directly from the raw API definition (the definition is a json file, hosted here.)
Because it is automatically generated, it is not what any sane python programmer would do if they were trying to write a python client for BigQuery. That said, it is the lowest-level representation of the API.
The gcloud client, on the other hand, was what a group of more-or-less sane folks at Google came up with when they tried to figure out what a client should look like for BigQuery. It is really quite nice, and lets you focus on what's important rather than converting results from the strange f/v format used in the BigQuery API into something useful.
Additionally, the documentation for the gcloud API was written by a doc writer. The documentation for the googleapiclient was, like the code, automatically generated from a definition of the API.
My advice, having used both (and having, mostly unsuccessfully, helped design the BigQuery API to try to make the generated client behave reasonably), is to use the gcloud client. It will handle a bunch of low-level details for you and generally make your life easier.

What is the difference between the AWS boto and boto3

I'm trying to learn the boto API and I noticed that there are two major versions/packages for Python: boto and boto3.
What is the difference between the AWS boto and boto3 libraries?

The boto package is the hand-coded Python library that has been around since 2006. It is very popular and is fully supported by AWS but because it is hand-coded and there are so many services available (with more appearing all the time) it is difficult to maintain.
So, boto3 is a new version of the boto library based on botocore. All of the low-level interfaces to AWS are driven from JSON service descriptions that are generated automatically from the canonical descriptions of the services. So, the interfaces are always correct and always up to date. There is a resource layer on top of the client-layer that provides a nicer, more Pythonic interface.
The boto3 library is being actively developed by AWS and is the one I would recommend people use if they are starting new development.