The document provides an overview of using Google APIs with Python, detailing the functionalities and features of Google Cloud Platform (GCP) services including serverless tools, Compute Engine, App Engine, Cloud Functions, and BigQuery. It emphasizes the importance of various authentication methods, the serverless development paradigm, and offers practical examples of deploying applications and leveraging machine learning capabilities. The document also highlights resources for tutorials, code samples, and the speaker's extensive background in software engineering and teaching.

1. Exploring Google APIs
with Python
Wesley Chun - @wescpy
Developer Advocate, Google
Adjunct CS Faculty, Foothill College
Developer Advocate, Google Cloud
● Mission: enable current and future
developers everywhere to be
successful using Google Cloud and
other Google developer tools & APIs
● Focus: GCP serverless (App Engine,
Cloud Functions, Cloud Run); higher
education, Google Workspace, GCP
AI/ML APIs; multi-product use cases
● Content: speak to developers globally;
make videos, create code samples,
produce codelabs (free, self-paced,
hands-on tutorials), publish blog posts
About the speaker
Previous experience / background
● Software engineer & architect for 20+ years
○ Yahoo!, Sun, HP, Cisco, EMC, Xilinx
○ Original Yahoo!Mail engineer/SWE
● Technical trainer, teacher, instructor
○ Taught Math, Linux, Python since 1983
○ Private corporate trainer
○ Adjunct CS Faculty at local SV college
● Python community member
○ Popular Core Python series author
○ Python Software Foundation Fellow
● AB (Math/CS) & CMP (Music/Piano), UC
Berkeley and MSCS, UC Santa Barbara
● Adjunct Computer Science Faculty, Foothill
College (Silicon Valley)

2. Agenda
1
Introduction to
Google APIs
2
Google Cloud
APIs overview
3
Serverless tools +
code samples
4
Inspiration
5
Wrap-up
01
Introduction to
Google APIs
Why are you here?

4. Cloud/GCP console
console.cloud.google.com
● Hub of all developer activity
● Applications == projects
○ New project for new apps
○ Projects have a billing acct
● Manage billing accounts
○ Financial instrument required
○ Personal or corporate credit cards,
Free Trial, and education grants
● Access GCP product settings
● Manage users & security
● Manage APIs in devconsole
● View application statistics
● En-/disable Google APIs
● Obtain application credentials
Using Google APIs
goo.gl/RbyTFD
API manager aka Developers Console (devconsole)
console.developers.google.com

5. Two different client library "styles"
● "Platform-level" client libraries (lower level)
○ Supports multiple products as a "lowest-common denominator"
○ Manage API service endpoints (setup & use)
○ Manage authorization (API keys, OAuth client IDs, service accounts)
○ Google Workspace, Google Analytics, YouTube, Google Ads APIs, etc.
○ Install: developers.google.com/api-client-library
● "Product-level" client libraries (higher level)
○ Custom client libraries made specifically for each product
○ Managing API service endpoints & security mostly taken care of
○ Only need to create a "client" to use API services
○ Install (Cloud/GCP & Firebase): cloud.google.com/apis/docs/cloud-client-libraries
○ Install (Maps): developers.google.com/places/web-service/client-library
● Some Google APIs families support both, e.g., Cloud
Google APIs client
libraries for common
languages; demos in
developers.google.com/api-
client-library
cloud.google.com/apis/docs
/cloud-client-libraries

6. Three different authz credential types
● Simple: API keys (to access public data)
○ Simplest form of authorization: an API key; tied to a project
○ Allows access to public data
○ Do not put in code, lose, or upload to GitHub! (can be restricted however)
○ Supported by: Google Maps, (some) YouTube, (some) GCP, etc.
● Authorized: OAuth client IDs (to access data owned by [human] user)
○ Provides additional layer of security via OAuth2 (RFC 6749)
○ Owner must grant permission for your app to access their data
○ Access granularity determined by requested permissions (user scopes)
○ Supported by: Google Workspace, (some) YouTube, (some) GCP, etc.
● Authorized: service accounts (to access data owned by an app/robot user)
○ Provides additional layer of security via OAuth2 or JWT (RFC 7519)
○ Project owning data grants permission implicitly; requires public-private key-pair
○ Access granularity determined by Cloud IAM permissions granted to service account key-pair
○ Supported by: GCP, (some) Google Workspace, etc.
SIMPLE
AUTHORIZED
Which do you choose?

7. OAuth2 or
API key
HTTP-based REST APIs 1
HTTP
2
Google APIs request-response workflow
● Application makes request
● Request received by service
● Process data, return response
● Results sent to application
(typical client-server model)
02
Google Cloud
APIs overview

8. Running Code: Compute Engine
>
Google Compute Engine delivers
configurable virtual machines
of all shapes and sizes, from
"micro" to 416 vCPUs, 11.776
TB RAM, 256 TB HDD or SSD
disk; GPUs & TPUs
(Debian, CentOS, CoreOS, SUSE, Red Hat Enterprise
Linux, Ubuntu, FreeBSD; Windows Server 2008 R2, 2012
R2, 2016, 1803, 1809, 1903/2019, 1909)
cloud.google.com/compute

9. Running Code: App Engine
Got a great app idea? Now what?
VMs? Operating systems? Big disk?
Web servers? Load balancing?
Database servers? Autoscaling?
With Google App Engine, you don't
think about those. Just upload
your code; we do everything else.
>
cloud.google.com/appengine
Running Code: Cloud Functions
Don't have an entire app? Just want
to deploy small microservices or
"RPCs" online globally? That's what
Google Cloud Functions are for!
(+Firebase version for mobile apps)
cloud.google.com/functions
firebase.google.com/products/functions

10. Running Code: Cloud Run
Got a containerized app? Want its
flexibility along with the convenience
of serverless that's fully-managed
plus auto-scales? Google Cloud Run is
exactly what you're looking for!
Need custom HW? Cloud Run on GKE
cloud.google.com/run
Managed containers: Kubernetes Engine
Got a containerized application?
Google Kubernetes Engine is an
enterprise-grade, fully-managed
container orchestration service.
cloud.google.com/kubernetes-engine

11. Storing Data: Cloud Storage & Cloud Filestore
cloud.google.com/storage
cloud.google.com/filestore
Storing Data: Cloud SQL
SQL servers in the cloud
High-performance, fully-managed
600MB to 416GB RAM; up to 64 vCPUs
Up to 10 TB storage; 40,000 IOPS
Types:
MySQL
Postgres
SQLServer (2019)
cloud.google.com/sql

12. Storing Data: Cloud Firestore
The best of both worlds: the next
generation of Cloud Datastore
(w/product rebrand) plus features
from the Firebase realtime database
(For choosing between Firebase & Cloud Firestore: see
firebase.google.com/docs/firestore/rtdb-vs-firestore;
for choosing between Cloud Datastore & Firestore: see
cloud.google.com/datastore/docs/firestore-or-datastore)
cloud.google.com/firestore
Storing and Analyzing Data: BigQuery
Google BigQuery is a fast, highly
scalable, fully-managed data
warehouse in the cloud for
analytics with built-in machine
learning (BQML); issue SQL queries
across multi-terabytes of data
cloud.google.com/bigquery

13. Passing Data & Events: Pub/Sub
Google Pub/Sub: a fast, highly
scalable, fully-managed multi
fan-in/fan-out publisher-subscriber
queuing system for messaging &
event ingestion (and processing)
cloud.google.com/pubsub
Machine Learning: Cloud Translation
Access Google Translate
programmatically through this
API; translate an arbitrary
string into any supported
language using state-of-the-art
Neural Machine Translation
cloud.google.com/translate

14. Machine Learning: Cloud Natural Language
Google Cloud Natural Language API
reveals the structure and meaning
of text, performing sentiment
analysis, content classification,
entity extraction, and syntactical
structure analysis; multi-lingual
cloud.google.com/language
Machine Learning: Cloud Vision & Video Intelligence
Google Cloud Vision & Video
Intelligence APIs enable developers
to extract metadata & understand the
content of images & videos, making
them searchable & discoverable.
cloud.google.com/vision
cloud.google.com/video-intelligence

15. Google Workspace
Top-level documentation and comprehensive developers
overview video at developers.google.com/gsuite
(formerly G Suite and Google Apps)
APIs
03
Serverless tools
+ code samples
Using Google APIs w/Python

16. > Google Compute Engine configurable
VMs of all shapes & sizes, from
"micro" to 416 vCPUs, 11.776 TB
RAM, 256 TB HDD/SSD plus Google
Cloud Storage for data lake "blobs"
(Debian, CentOS, CoreOS, SUSE, Red Hat Enterprise Linux,
Ubuntu, FreeBSD; Windows Server 2008 R2, 2012 R2, 2016, 1803,
1809, 1903/2019, 1909)
cloud.google.com/compute
cloud.google.com/storage
Yeah, we got VMs & big disk… but why*?
Serverless: what & why
● What is serverless?
○ Misnomer
○ "No worries"
○ Developers focus on writing code & solving business problems*
● Why serverless?
○ Fastest growing segment of cloud... per analyst research*:
■ $1.9B (2016) and $4.25B (2018) ⇒ $7.7B (2021) and $14.93B (2023)
○ What if you go viral? Autoscaling: your new best friend
○ What if you don't? Code not running? You're not paying.
* in USD; source:Forbes (May 2018), MarketsandMarkets™ & CB Insights (Aug 2018)

17. Why does App Engine exist?
● Focus on app not DevOps
○ Web app
○ Mobile backend
○ Cloud service
● Enhance productivity
● Deploy globally
● Fully-managed
● Auto-scaling
● Pay-per-use
● Familiar languages
● Test w/local dev server
Hello World (Python "MVP")
app.yaml
runtime: python38
main.py
from flask import Flask
app = Flask(__name__)
@app.route('/')
def hello():
return 'Hello World!'
requirements.txt
Flask>=1.1.2
Deploy:
$ gcloud app deploy
Access globally:
PROJECT_ID.appspot.com
cloud.google.com/appengine/docs/standard/python3/quickstart

18. Why does Cloud Functions exist?
● Don't have entire app?
○ No framework "overhead" (LAMP, MEAN...)
○ Deploy microservices
● Event-driven
○ Triggered via HTTP or background events
■ Pub/Sub, Cloud Storage, Firebase, etc.
○ Auto-scaling & highly-available; pay per use
● Flexible development environment
○ Cmd-line or developer console (in-browser)
○ Develop/test locally with Functions Framework
● Cloud Functions for Firebase
○ Mobile app use-cases
● Available runtimes
○ JS/Node.js 8, 10, 12, 14
○ Python 3.7, 3.8, 3.9
○ Go 1.11, 1.13
○ Java 11
○ Ruby 2.6, 2.7
○ .NET Core 3.1
main.py
def hello_world(request):
return 'Hello World!'
Deploy:
$ gcloud functions deploy hello --runtime python38 --trigger-http
Access globally (curl):
$ curl REGION-PROJECT_ID.cloudfunctions.net/hello
Access globally (browser):
https://REGION-PROJECT_ID.cloudfunctions.net/hello
Hello World (Python "MVP")
cloud.google.com/functions/docs/quickstart-python

19. The rise of containers... ● Any language
● Any library
● Any binary
● Ecosystem of base images
● Industry standard
FLEXIBILITY
“We can’t be locked in.”
“How can we use
existing binaries?”
“Why do I have to choose between
containers and serverless?”
“Can you support language _______ ?”
Serverless inaccessible for some...
CONVENIENCE

20. Cloud Run: code, build, deploy
.js .rb .go
.sh
.py ...
● Any language, library, binary
○ HTTP port, stateless
● Bundle into container
○ Build w/Docker OR
○ Google Cloud Build
○ Image ⇒ Container Registry
● Deploy to Cloud Run (managed or GKE)
● GitOps: (CI/)CD Push-to-deploy from Git
State
HTTP
Hello World (Python "MVP")
main.py
import os
from flask import Flask
app = Flask(__name__)
@app.route('/')
def hello_world():
return 'Hello World!'
if __name__ == '__main__':
app.run(debug=True, host='0.0.0.0', port=int(os.environ.get('PORT', 8080)))
cloud.google.com/run/docs/quickstarts/build-and-deploy
requirements.txt
Flask>=1.1.2

21. Hello World (Python "MVP")
Dockerfile
FROM python:3-slim
WORKDIR /app
COPY . .
RUN pip install -r requirements.txt
CMD ["python", "main.py"]
.dockerignore
Dockerfile
README.md
*.pyc
*.pyo
.git/
__pycache__
Build (think docker build and docker push) then deploy (think docker run):
$ gcloud builds submit --tag gcr.io/PROJ_ID/IMG_NAME
$ gcloud run deploy SVC_NAME --image gcr.io/PROJ_ID/IMG_NAME
OR… Build and Deploy (1-line combo of above commands):
$ gcloud run deploy SVC_NAME --source .
Deploy (think docker push):
$ gcloud run deploy --image
gcr.io/PROJ_ID/IMG_NAME
--platform managed
Access globally:
SVC_NAME-HASH-REG_ABBR.a.run.app
Docker &
Dockerfile
OPTIONAL!!
BigQuery: querying Shakespeare words
from google.cloud import bigquery
TITLE = "The most common words in all of Shakespeare's works"
QUERY = '''
SELECT LOWER(word) AS word, sum(word_count) AS count
FROM `bigquery-public-data.samples.shakespeare`
GROUP BY word ORDER BY count DESC LIMIT 10
'''
rsp = bigquery.Client().query(QUERY).result()
print('n*** Results for %r:n' % TITLE)
print('t'.join(col.name.upper() for col in rsp.schema)) # HEADERS
print('n'.join('t'.join(str(x) for x in row.values()) for row in rsp)) # DATA

22. Top 10 most common Shakespeare words
$ python bq_shake.py
*** Results for "The most common words in all of Shakespeare's works":
WORD COUNT
the 29801
and 27529
i 21029
to 20957
of 18514
a 15370
you 14010
my 12936
in 11722
that 11519
● BigQuery public data sets: cloud.google.com/bigquery/public-data
● BQ sandbox (1TB/mo free): cloud.google.com/bigquery/docs/sandbox
● Other public data sets: cloud.google.com/public-datasets (Google Cloud),
research.google/tools/datasets (Google Research), and Kaggle (kaggle.com)
● COVID-19
○ How to use our data sets (see blog post)
○ JHU Coronavirus COVID-19 Global Cases data set
○ List of all COVID-19 data sets
● Cloud Life Sciences API: cloud.google.com/life-sciences (see blog post)
● Cloud Healthcare API: cloud.google.com/healthcare (see blog post)
BigQuery & public data sets
Spring 2020

23. from google.cloud import vision
image_uri = 'gs://cloud-samples-data/vision/using_curl/shanghai.jpeg'
client = vision.ImageAnnotatorClient()
image = vision.types.Image()
image.source.image_uri = image_uri
response = client.label_detection(image=image)
print('Labels (and confidence score):')
print('=' * 30)
for label in response.label_annotations:
print(label.description, '(%.2f%%)' % (label.score*100.))
Vision: label annotation/object detection
$ python3 label-detect.py
Labels (and confidence score):
==============================
People (95.05%)
Street (89.12%)
Mode of transport (89.09%)
Transport (85.13%)
Vehicle (84.69%)
Snapshot (84.11%)
Urban area (80.29%)
Infrastructure (73.14%)
Road (72.74%)
Pedestrian (68.90%)
Vision: label annotation/object detection
g.co/codelabs/vision-python

24. ● Not just for conversations
● Create microservice utilities
● Build chat bots to...
○ Automate workflows
○ Query for information
○ Other heavy-lifting
● Plain text or rich UI "cards"
● Very flexible ("any")
development environment
○ POST to HTTP port
Hangouts Chat bots
(bot framework & API)
"Hello World" (echo bot)
Python+Flask: GAE or other hosting
from flask import Flask, request, json
app = Flask(__name__)
@app.route('/', methods=['POST'])
def on_event():
event = request.get_json()
msg = {}
if event['type'] == 'MESSAGE':
text = 'Hi {}. You sent: {}'.format(
event['user']['displayName'], event['message']['text'])
msg = {'text': text}
return json.jsonify(msg)
Hangouts Chat bots
goo.gl/jt3FqK

25. Search YouTube for videos
from __future__ import print_function
from googleapiclient import discovery
from settings import API_KEY
QUERY = 'python -snake'
trim = lambda x, ct: ('%s%s' % (x[:ct],
'...' if len(x)>ct else '')).ljust(ct+3)
print('n** Searching for %r videos...' % QUERY)
YOUTUBE = discovery.build('youtube', 'v3', developerKey=API_KEY)
res = YOUTUBE.search().list(q=QUERY, type='video',
part='id,snippet').execute().get('items', [])
for item in res:
print('http://youtu.be/%st%s' % (
trim(item['id']['videoId'], 24),
trim(item['snippet']['title'], 48)))
Maps APIs geocoding & places queries
import googlemaps
from settings import API_KEY
GMAPS = googlemaps.Client(key=API_KEY)
print('n** Geocode address:')
rsp = GMAPS.geocode('1600 Amphitheatre Pkwy 94043')
latlong = rsp[0]['geometry']['location']
print('tGeocode:', latlong['lat'], ',', latlong['lng'])
print('n** Address lookup:')
rsp = GMAPS.reverse_geocode((37.4222934, -122.0841409))
print('tAddress:', rsp[0]['formatted_address'])
print('n** Place query:')
pl_id = GMAPS.find_place('Villa Tugendhat',
input_type='textquery')['candidates'][0]['place_id']
place = GMAPS.place(pl_id)['result']
print('tPlace:t', place['name'])
print('tAddress:t', place['formatted_address'])
print('tWebsite:t', place['website'])
print('tMaps URL:t', place['url'])
$ python3 maps-demo-pub.py
** Geocode address:
Geocode: 37.4222934 , -122.0841409
** Address lookup:
Address: 1600 Amphitheatre Pkwy, Mountain View, CA
94043, USA
** Place query:
Place: Villa Tugendhat
Address: Černopolní 45, 613 00 Brno, Czechia
Website: tugendhat.eu
Maps URL: maps.google.com/?cid=5889127146370224362
Google Maps APIs
● Directions API
● Distance Matrix API
● Elevation API
● Geocoding API
● Geolocation API
● Time Zone API
● Roads API
● Places API
● Maps Static API

26. Other Google APIs & platforms
● Firebase (mobile development platform + RT DB; ML Kit)
○ firebase.google.com & firebase.google.com/docs/ml-kit
● Google Data Studio (data visualization, dashboards, etc.)
○ datastudio.google.com/overview
○ goo.gle/datastudio-course
● Actions on Google/Assistant/DialogFlow (voice apps)
○ developers.google.com/actions
● YouTube (Data, Analytics, and Livestreaming APIs)
○ developers.google.com/youtube
● Google Maps (Maps, Routes, and Places APIs)
○ developers.google.com/maps
● Flutter (native apps [Android, iOS, web] w/1 code base[!])
○ flutter.dev
04
All of Cloud
(inspiration)
Build powerful solutions
with GCP and G Suite

27. Cloud image processing workflow
Archive and analyze Google Workspace
(formerly G Suite) images with GCP

28. Cloud
Vision
Google
Workspac
e
(formerly
G Suite)
GCP
Cloud image processing workflow
Cloud
Storage
Drive
Sheets
Archive
image
Categorize
image
Record
results

29. Cloud image processing workflow
def drive_get_file(fname):
rsp = DRIVE.files().list(q="name='%s'" % fname).execute().get['files'][0]
fileId, fname, mtype = rsp['id'], rsp['name'], rsp['mimeType']
blob = DRIVE.files().get_media(fileId).execute()
return fname, mtype, rsp['modifiedTime'], blob
def gcs_blob_upload(fname, bucket, blob, mimetype):
body = {'name': fname, 'uploadType': 'multipart',
'contentType': mimetype}
return GCS.objects().insert(bucket, body, blob).execute()
def vision_label_img(img, top):
body = {'requests': [{'image': {'content': img}, 'features':
[{'type': 'LABEL_DETECTION', 'maxResults': top}]}]}
rsp = VISION.images().annotate(
body=body).execute().get['responses'][0]
return ', '.join('%s (%.2f%%)' % (label['description'],
label['score']*100.) for label in rsp['labelAnnotations'])
def sheet_append_rows(sheet, rows):
rsp = SHEETS.spreadsheets().values().append(
spreadsheetId=sheet, range='Sheet1',
body={'values': rows}).execute()
return rsp.get('updates').get('updatedCells')
def main(fname, bucket, sheet_id, folder, top):
fname, mtype, ftime, data = drive_get_img(fname)
gcs_blob_upload(fname, bucket, data, mtype)
vision_label_img(data, top)
sheet_append_row([sheet_id, fname, mtype,
ftime, len(data), rsp])
API method calls in Bold
Driver calls in Bold Italics
● Project goal: Imagine enterprise use cases Workspace & GCP; got one!
● Specific goals: free-up highly-utilized resource, archive data to
colder/cheaper storage, analyze images, generate report for mgmt
● Download image binary from Google Drive
● Upload object to Cloud Storage bucket
● Send payload for analysis by Cloud Vision
● Write back-up location & analysis results into Google Sheets
● Blog post: goo.gle/3nPxmlc (original post); Cloud X-post
● Codelab: self-paced (1+-hour) hands-on tutorial
● g.co/codelabs/drive-gcs-vision-sheets
● Application source code
● github.com/googlecodelabs/analyze_gsimg
App summary

30. 05
Wrap-up
Summary & resources
Session Summary
● Google provides more than just apps
○ We're more than search, YouTube, Android, Chrome, and Gmail
○ Much of our tech available to developers through our APIs
● Tour of Google (Cloud) APIs & developer tools
○ Workspace: not just a set of productivity apps… you can code them too!
○ GCP: compute, storage, networking, security, data & machine learning tools
■ Serverless: frees developers from infrastructure
■ So you can focus on building solutions
● Interesting possibilities using ALL of Cloud (GCP + Workspace)
● Also explore other Google developer products & APIs

31. ● Documentation
○ GCP: cloud.google.com/{docs,appengine,functions,run,vision,automl,translate,language,
speech,texttospeech,video-intelligence,firestore,bigquery,compute,storage,gpu,tpu}
○ Workspace: developers.google.com/{gsuite,drive,calendar,gmail,docs,sheets,slides,apps-script}
● Introductory "codelabs" ([free] self-paced, hands-on tutorials)
○ Workspace APIs: g.co/codelabs/gsuite-apis-intro (featuring Drive API)
○ Apps Script: g.co/codelabs/apps-script-intro
○ App Engine: codelabs.developers.google.com/codelabs/cloud-app-engine-python
○ Cloud Functions: codelabs.developers.google.com/codelabs/cloud-starting-cloudfunctions
○ Cloud Run: codelabs.developers.google.com/codelabs/cloud-run-hello-python3
○ Others: g.co/codelabs (all Google codelabs) and gcplab.me (GCP-only)
● Videos: youtube.com/GoogleCloudPlatform (GCP) and goo.gl/JpBQ40 (Google Workspace)
● Code samples: github.com/GoogleCloudPlatform (GCP) and github.com/gsuitedevs (Google Workspace)
● Free trial (new users) and Always Free (daily/monthly tier) - cloud.google.com/free
● Know AWS/Azure? Compare w/GCP at cloud.google.com/docs/compare/{aws,azure}
Resources (industry)
Thank you! Questions?
Wesley Chun
@wescpy
Progress bars: goo.gl/69EJVw
Slides: bit.ly/2SHybQg