The Python Cheat Sheet for the Busy Marketer
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The document is a guide detailing how marketers can use Python and Google Colab for data analysis related to SEO assessments of Shopify themes. It covers topics such as web scraping, data transformation, API calling, and exporting data to Google Sheets. Key features and basic Python constructs are also highlighted to help users set up their analytical processes.
![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](https://image.slidesharecdn.com/thepythoncheatsheetforthebusymarketerfinal-200924214040/85/The-Python-Cheat-Sheet-for-the-Busy-Marketer-11-320.jpg)
![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](https://image.slidesharecdn.com/thepythoncheatsheetforthebusymarketerfinal-200924214040/85/The-Python-Cheat-Sheet-for-the-Busy-Marketer-12-320.jpg)
The Python Cheat Sheet for the Busy Marketer
- 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