Tutorial 11: Python Fundamentals for Marketers — No CS Degree Required

What you’ll learn: Variables, functions, loops, working with CSVs and JSON, DataFrames with pandas, and how to read/write Google Sheets—everything you need to automate marketing tasks. Zero programming experience assumed.

The What: Python Is Your Marketing Superpower

Python is the most popular programming language for data analysis and AI. It is also the easiest to learn. If you can write a Google Ads query in the platform’s interface, you can learn Python. The syntax reads like English: ‘for each campaign in campaigns: print the name’ is almost exactly how you write it in Python.

The How: Core Concepts in 60 Minutes

Variables and Data Types

# Variables store data
campaign_name = 'Brand Search'     # String (text)
daily_budget = 150.00               # Float (decimal number)
clicks = 1234                       # Integer (whole number)
is_active = True                    # Boolean (True/False)

# Lists store multiple items
keywords = ['running shoes', 'marathon gear', 'trail shoes']
cpas = [12.50, 18.30, 9.75, 22.10]

# Dictionaries store key-value pairs (like a spreadsheet row)
campaign = {
'name': 'Brand Search',
'budget': 150.00,
'clicks': 1234,
'conversions': 89,
'cpa': 13.87
}

print(f'Campaign: {campaign["name"]} | CPA: ${campaign["cpa"]}')

Functions: Reusable Calculations

def calculate_roas(revenue, cost):
'''Calculate Return on Ad Spend.'''
if cost == 0:
return 0
return revenue / cost

def is_profitable(campaign, target_roas=3.0):
'''Check if a campaign meets ROAS target.'''
roas = calculate_roas(campaign['revenue'], campaign['cost'])
return roas >= target_roas

# Use it:
camp = {'name': 'Shopping', 'revenue': 45000, 'cost': 12000}
roas = calculate_roas(camp['revenue'], camp['cost'])
print(f'{camp["name"]}: ROAS = {roas:.2f}x')  # Shopping: ROAS = 3.75x

Loops: Process Data at Scale

campaigns = [
{'name':'Brand','cost':5000,'conversions':400,'revenue':20000},
{'name':'Non-Brand','cost':15000,'conversions':200,'revenue':35000},
{'name':'Shopping','cost':8000,'conversions':150,'revenue':18000},
{'name':'PMax','cost':12000,'conversions':180,'revenue':28000},
]

# Analyze every campaign automatically
for camp in campaigns:
cpa = camp['cost'] / camp['conversions'] if camp['conversions'] > 0 else 0
roas = camp['revenue'] / camp['cost'] if camp['cost'] > 0 else 0
status = 'PROFITABLE' if roas > 2.0 else 'REVIEW'
print(f'{camp["name"]:12} | CPA: ${cpa:7.2f} | ROAS: {roas:.2f}x | {status}')

Pandas: DataFrames for Marketing Data

import pandas as pd

# Read a CSV exported from Google Ads
df = pd.read_csv('campaign_report.csv')

# Quick analysis
print(df.describe())              # Summary stats for all columns
print(df.groupby('Campaign')['Cost'].sum())  # Total cost per campaign

# Filter for unprofitable campaigns
wasteful = df[(df['Conversions'] == 0) & (df['Cost'] > 50)]
print(f'Found {len(wasteful)} queries wasting money')

# Export results
wasteful.to_csv('wasteful_queries.csv', index=False)

Working with Google Sheets

import gspread
from google.oauth2.service_account import Credentials

# Setup (one-time): Create service account in Google Cloud Console
# Download JSON key, share your Sheet with the service account email
scopes = ['https://www.googleapis.com/auth/spreadsheets']
creds = Credentials.from_service_account_file('creds.json', scopes=scopes)
gc = gspread.authorize(creds)

# Read data from a sheet
sheet = gc.open('Campaign Data').sheet1
data = sheet.get_all_records()  # Returns list of dictionaries

# Write results back
sheet.update('G1', [['AI Analysis']])  # Header
for i, row in enumerate(data):
analysis = f'CPA ${row["Cost"]/max(row["Conversions"],1):.2f}'
sheet.update(f'G{i+2}', [[analysis]])

The So What: Every GoogleAdsAgent.ai Script Uses These Patterns

The Python scripts powering GoogleAdsAgent.ai use exactly these patterns: dictionaries for campaign data, functions for calculations, loops for batch processing, pandas for analysis, and gspread for Google Sheets integration. Understanding these fundamentals means you can read, modify, and extend every tool in the open-source library.

📦 GitHub: https://github.com/itallstartedwithaidea/google-ads-api-agent — See /actions/ folder for production Python using all patterns above

Website: googleadsagent.ai | GitHub: https://github.com/itallstartedwithaidea | Tools: googleadsagent.ai/tools

John Williams | Senior Paid Media Specialist, Seer Interactive | $48M+ managed spend | Creator, GoogleAdsAgent.ai | Hero Conf Speaker | github.com/itallstartedwithaidea

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