Beginner Python in ArcGIS Pro Notebooks: Geospatial Data Analysis Skills
Learn Python for geospatial data analysis using ArcGIS Pro Notebooks (built on Jupyter notebooks)—a practical, job-relevant workflow for running experiments, analyzing data, and creating quick visualizations right inside ArcGIS Pro. In this video you’ll build real skills step-by-step: Python fundamentals, working with notebook cells and markdown, core data structures, and using pandas to create a table and plot results—exactly the kind of workflow used in modern GIS + data science roles. All code used in the video is available in the video description.
What you’ll learn:
• How ArcGIS Pro Notebooks work (Jupyter-style cells, markdown, running code)
• Essential Python basics for GIS: variables, types, printing, f-strings
• Lists, dictionaries, conditionals, and loops (core “thinking like code” skills)
• Intro to pandas for table-style data + a quick bar chart
• How Python skills connect to real geospatial job postings and workflows
Chapters
0:00:00 Intro: ArcGIS Pro Notebooks = Jupyter notebooks
0:00:27 The “data science notebook” metaphor and why it matters
0:01:19 ArcGIS Notebooks inside ArcGIS Pro (and beyond Esri)
0:02:53 Why Python matters for geospatial careers
0:08:45 Quick intro to pandas (why it’s important)
0:09:53 ArcPy overview (Python in the ArcGIS Pro ecosystem)
0:12:02 Python environments + Package Manager (libraries & setup)
0:15:18 Create and organize a new notebook in ArcGIS Pro
0:19:23 Notebook cells: code vs markdown, running cells
0:22:01 Python basics: variables, strings, and data types
0:24:08 Using the type() function to inspect data types
0:26:27 Printing + f-strings (clean output with variables)
0:31:38 Lists: creating, printing, and indexing
0:38:30 Dictionaries: key-value pairs and why they’re useful
0:44:40 Conditionals: if / elif / else + indentation rules
0:54:27 Loops: iterating through a list
0:59:38 pandas in ArcGIS Pro: confirming it’s available
1:01:33 Import pandas, build a DataFrame, and plot a bar chart
1:08:04 Wrap-up: where this workflow goes next for spatial analysis
Code Used in the Video
Code Used in the Video
Part 1: Variables & Types (Python Fundamentals)
project_name = "IGME 770"
type(project_name)
Part 2: Printing + f-Strings (Readable Output)
print(f"Course: {project_name}")
Part 3: Lists (Your First Data Structure)
layers = ["Roads", "Parcels", "Buildings", "Hydro"]
print(layers)
print(layers[0]) # first item
Part 4: Dictionaries (Key → Value Thinking)
field_descriptions = {
"POP": "Population estimate",
"AREA_SQKM": "Area in square kilometers",
"DENSITY": "Population density"
}
print(field_descriptions["POP"])
Part 5: Conditionals (Making Decisions)
population = 12000
NOTE: Replace the word LESS_THAN with the less-than comparison symbol when running in Python
if population LESS_THAN 5000:
size = "Small"
elif population LESS_THAN 20000:
size = "Medium"
else:
size = "Large"
print(f"Settlement size: {size}")
Part 6: Loops (Automation Begins)
for layer in layers:
print(f"Processing layer: {layer}")
total = 0
for value in [10, 20, 30]:
total += value
Part 7: Functions (Reusable Tools)
def meters_to_feet(meters):
"""Convert meters to feet"""
return meters * 3.28084
meters_to_feet(250)
Part 8: Working with Tables (pandas)
import pandas as pd
data = {
"Name": ["A", "B", "C"],
"Population": [1200, 5400, 22000]
}
df = pd.DataFrame(data)
Part 9: Simple Visualization (Quick Insight)
df["Population"].plot(kind="bar", title="Population by Area")
Part 10: Gentle ArcGIS Tie-In
import arcpy
arcpy.env.workspace = "CURRENT"
arcpy.env.scratchGDB
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Thanks for stopping by!
Brian Tomaszewski, Ph.D.
@bangeobrian
https://gisfordisastermanagement.com/
/ brian-tomaszewski-452b1387
Tags: Python, ArcGIS Pro, ArcGIS Pro Notebooks, Jupyter Notebook, GIS, geospatial, geospatial data analysis, spatial analysis, pandas, ArcPy, Python basics, data science, data visualization, DataFrame, programming for GIS, Esri, GIS programming, Python for beginners, spatial data science, GIS careers