What percentage of an Inbound marketer's day doesn't involve working with spreadsheets? How much of this work is time-consuming and repetitive? In this interactive session, you will learn how to manipulate Google Sheets to automate common data analysis workflows using Python, a very easy to use programming language.

1. THE PYTHON CHEAT SHEET FOR
THE BUSY MARKETER
H A M L E T B A T I S T A
#INBOUND2020

2. Google Colaboratory
feature walkthrough
GOOGLE COLAB
Preparing your Python
notebook environment
SETUP
Publish your analysis to
Google Sheets directly
from Python
EXPORTING TO SHEETS
Convert your data
analysis into simpler to
understand formats
DATA TRANSFORMATION
Process API output to
extract insights
JSON PARSING
Run automated analysis
using Google
Lighthouse
API CALLING
AGENDA
T H E P Y T H O N C H E A T S H E E T F O R T H E
B U S Y M A R K E T E R
@hamletbatista
Fetch content from the
web
WEB SCRAPING
Basic data types,
Containers, Functions,
Classes
PYTHON BASICS

3. INTRODUCTION
P Y T H O N S O U R C E D D A T A S T U D I E S
Published April 7, 2020
PAGE SPEED SCORES OF EVERY
SHOPIFY THEME
Published June 23, 2020
ACCESSING ‘CORE WEB VITALS’ OF
EVERY SHOPIFY THEME
Our work in this presentation
AN SEO ASSESSMENT OF EVERY
SHOPIFY THEME
#INBOUND2020
https://bit.ly/31JXHJ1https://bit.ly/38tEixp https://bit.ly/31JXHJ1
@hamletbatista

4. We will run Google
Lighthouse SEO analysis
feature under mobile
emulation
GOOGLE LIGHTHOUSE
We will scrape the demo
sites of every theme
SHOPIFY THEME STORE
We will use a handy cheat
sheet spread over multiple
Python notebooks
CHEAT SHEET
PYTHON SOURCED
DATA STUDY
A M O B I L E S E O A S S E S S M E N T O F E V E R Y S H O P I F Y T H E M E
#INBOUND2020 @hamletbatista

5. POWERED BY GOOGLE
LIGHTHOUSE
A M O B I L E S E O A S S E S S M E N T O F E V E R Y S H O P I F Y T H E M E
#INBOUND2020 @hamletbatista
We will run Google
Lighthouse SEO analysis
feature under mobile
emulation
GOOGLE LIGHTHOUSE
We will scrape the demo
sites of every theme
SHOPIFY THEME STORE
We will use a handy cheat
sheet spread over multiple
Python notebooks
CHEAT SHEET

6. SETUP
1
Create a new Python notebook directly from
Google Drive
#INBOUND2020
U S I N G G O O G L E C O L A B O R A T O R Y
Getting Started:
• Overview of Colaboratory Features
@hamletbatista

7. GOOGLE COLAB
FEATURES
T U R N N O T E B O O K S I N T O P R O G R A M S
2
Google Colab’s includes many useful
features that simplify data acquisition,
preparation and analysis.
#INBOUND2020
Most common form fields:
input1 = “value” #@param {type:”string”}
input2 = 10 #@param {type:”integer”}
input3 = “2020-09-22” #@param {type:”date”}
@hamletbatista

8. PYTHON
BASICS
3
Google Colab requires Python code. Let’s
let’s review some of the basic constructs.
#INBOUND2020
T H E L A N G U A G E O F D A T A S C I E N C E
@hamletbatista
Python learning resources:
• An Introduction to Python for Technical SEO
• An Introduction to Python for SEO Pros Using Spreadsheets
• How to Use Python to Analyze SEO Data: A Reference Guide

9. 4
#INBOUND2020 @hamletbatista
WEB
SCRAPING
In our example, we will scrape the Shopify
Theme’s Store for theme names and demo
site URLs
F E T C H C O N T E N T F R O M T H E W E B
Example Scrapy spider changes:
• start_urls
• “Next page” CSS selector
• Elements to extract (theme name and link)
Learn more:
• Scrapy.org

10. API CALLING
5
In our example, we will call the Page Speed
Insights API to get SEO assessment metrics
and recommendations
#INBOUND2020
R U N A U T O M A T E D A N A L Y S I S
Most common request call:
r = requests.get(api_url, headers={'Accept': 'application/json'})
if r.status_code == 200:
return r.json()
Learn more:
• Requests: HTTP for Humans
@hamletbatista

11. JSON PARSING
P R O C E S S I N G A P I R E S P O N S E S
6
Complex JSON API responses can be a
pain to process. We will use JSONPath to
drastically simplify this.
#INBOUND2020
Most common JSONPath recipe:
json_path = “$.lighthouseResult.categories.seo.score”
jsonpath_tree = parse(json_path)
value = [match.value for match in jsonpath_tree.find(data)]
Learn more:
JSON JSONPath Tutorial
@hamletbatista

12. DATA
TRANSFORMATION
7
In our example, we will use Python pandas
to reorganize our study in a way that is easy
to interpret
#INBOUND2020
D A T A I S R A R E L Y I N T H E B E S T F O R M A T
Converting a list of dictionaries to Pandas Data Frame:
import pandas as pd
new_format = [{“column1_name”: “value”},{“column2_name”: “value”} ]
df = pd.DataFrame(new_format)
Learn more:
• 10 minutes to pandas
@hamletbatista

13. EXPORTING TO
SHEETS
G O O G L E S H E E T S I S S I M P L E R
8
Once we produce our data in Python we
can transfer it directly to Google Sheets
for further analysis and reporting
#INBOUND2020
Transferring a Pandas Data Frame to a Google Sheet :
spread = Spread("INBOUND2020", creds=creds)
spread.df_to_sheet(df, index=False, sheet="Themes SEO audits",
start='A1', replace=True)
Learn more:
• Examples of gspread Usage
• Introduction to gspread_pandas (video)
@hamletbatista

14. LOCATION
33 Wood Ave South
Suite 600
Iselin, NJ 08830
USA
PHONE
CONTACT US
+1 866.882.2983
https://www.ranksense.com
#INBOUND2020
ABOUT
RANKSENSE
A U T O M A T E A N D R U N S E O E X P E R I M E N T S
F R O M G O O G L E S H E E T S
@RankSense