Introduction to Programming with Python

Data Science Training Intensive · NC State University Libraries

While we get started

Open the materials

go.ncsu.edu/dsti-python

Want a head start? Start Setup on the materials page now.

Install stuck? Colab is the backup: the same notebook, in your browser.

QR code for the workshop materials page, go.ncsu.edu/dsti-python

Data Science Services

Who we are

Data Science Services, who we are: Mara Blake, Head, Data Science Services; Shannon Ricci, Associate Head, Data Science Services; Jeff Essic, Lead Librarian for GIS; Alp Tezbasaran, Data Science Librarian; Selene Schmittling, Data Science Librarian; Bin Liu, Data Science Librarian; Ashley Pearson, University Library Specialist

Our team comes from a variety of academic backgrounds, including nuclear engineering, electrical engineering, industrial and environmental engineering, marine ecology, information science, natural resources, business administration, and geographic information sciences.

How to contact us

Today's finish line

  • Install Python with uv
  • Understand the differences between Python, Jupyter, JupyterLab and Google Colab
  • Navigate the JupyterLab environment
  • Create variables
  • Use basic data types and data structures, such as lists and dictionaries
  • Use arithmetic, comparison, and logical operators
  • Write an if statement and a for loop
  • Use methods with dot notation
  • Write a user-defined function

Install Python with uv

15 minutes, together

The pieces

Python The language itself.

uv The small tool that installs Python and packages for you.

JupyterLab The notebook app on your laptop. Today's workspace.

Colab The same notebooks on Google's computers. Our backup.

Install uv

Mac

Open Terminal: ⌘ Space, type "Terminal"

curl -LsSf https://astral.sh/uv/install.sh | sh

Windows

Open PowerShell: Start, type "PowerShell"

powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

Close and reopen the terminal, then check:

uv --version

Your project folder

uv init --python 3.13 --vcs none --no-package dss-python
cd dss-python
uv add jupyterlab matplotlib
  • A folder with its own Python
  • uv add downloads Python, JupyterLab and matplotlib: the slow step
  • While it runs: download the notebook from Setup

Already use conda?

conda create -y -n dss-python -c conda-forge --override-channels python=3.13 jupyterlab matplotlib
conda activate dss-python
mkdir dss-python
cd dss-python
jupyter lab
  • Windows: use Anaconda Prompt or Miniforge Prompt
  • Then the same notebook steps
  • Steps on Setup

Your notebook in JupyterLab

Everything from here runs on your laptop

Open the notebook

  1. Download the learner notebook from go.ncsu.edu/dsti-python, then Setup
  2. Run uv run jupyter lab, inside dss-python. Leave that terminal open
  3. Drag the notebook into the file browser, then double-click it
  4. Run the bar plot cell

Around 9:30, no chart yet? Switch to Colab and keep going. A helper finishes your install at the break.

Checkpoint: the bar plot cell, section 1

If you see...

If you see Do
uv: command not found Open a new terminal, or run source $HOME/.local/bin/env
Mac offers "command line developer tools" Not Now is fine
Windows SmartScreen or antivirus warning Flag a helper
ModuleNotFoundError: No module named 'matplotlib' Ctrl+C twice, then cd dss-python, then uv run jupyter lab
The notebook isn't in the file list Drag it in again. Check you started JupyterLab inside dss-python
Still stuck Flag a helper: we finish during the break

Around 9:30: no chart? Switch to Colab.

Backup: Google Colab

  1. Open go.ncsu.edu/dsti-python, choose Colab backup, then learner notebook in Colab
  2. Sign in with Google, then click Copy to Drive
  3. Work in the new tab titled "Copy of introduction-to-programming-with-python.ipynb"

Edits in the original tab aren't saved. Every tab title should start with "Copy of".

Notebook: Introduction to Programming with Python, top cell

Python, Jupyter, JupyterLab and Colab

Python The language.

Jupyter The notebook format, .ipynb.

JupyterLab Runs notebooks on your laptop.

Colab Runs them on Google's computers.

Section 1: Python and JupyterLab

Find your way around

  • File browser on the left: your dss-python folder
  • Table of contents in the left sidebar: how we navigate
  • Kernel status at the top right
  • Run menu
  • Ctrl/⌘+S saves
  • Only the last line of a cell shows automatically
len?

Put ? after a name for help.

%whos

Lists the variables in memory.

Sections 1 and 2

Two modes

Edit mode Press Enter. For typing in a cell.

Command mode Press Esc. For whole cells. A blue bar marks the cell.

In command mode

  • A / B add a cell above / below
  • M / Y make it markdown / code
  • D D deletes it

In either mode

  • Shift+Enter runs a cell and moves on
  • Ctrl/⌘+Enter runs it in place

Section 2: Notebook basics

Part 1: Basics

Notebook sections 1 to 5

Variables

A variable is a name for a value. = assigns; == compares.

number = 7
number_times = number * number
print(f"{number} x {number} = {number_times}")

Try it: number_plus Section 3: Variables and data types

Data types, and counting from 0

type(7) # int
type(7.5) # float
"Hello"[0] # H
"Hello"[0:3] # Hel
  • Numbers, text, and True/False
  • Python counts from 0
  • A slice stops before the end index

Section 3: Variables and data types

Lists and dictionaries

List: a position
cat_toys[1]
Dictionary: a key
birthdays["Spot"]
  • Square brackets hold a position in a list, a key in a dictionary
  • Lists change in place: cat_toys[0] = "feather"
  • Add a key: cats["Carla"] = ...

Try it: my_info, and Spot's birthday Section 4: Lists and dictionaries

Arithmetic

7 / 2 # 3.5
7 // 2 # 3
2 ** 3 # 8
7 % 2 # 1

/ always gives a float. // rounds down. Watch the type of each result.

Try it: (400 * 4) / 14 + 39 Section 5: Arithmetic

Break

Five minutes. Part 2 is in this same notebook.

Everything you made is still here. Save: Ctrl/⌘+S. Helpers: finishing flagged installs.

Part 2: Control flow and functions

Notebook sections 6 to 10

Comparison and logical operators

5 > 2
cat_name == "Sally"
x > 0 and x < 10
  • Each comparison gives True or False
  • Guess first, then run
  • Logic is plain English words

Try it: less than 10 and greater than zero Section 6: Comparison and logical operators

if, elif and else

if cat_name == "Sally":
    print("hi")
elif cat_name in my_cats:
    ...
  • The colon starts a block
  • The indent is the block
  • Skip the indent and you get an IndentationError

Section 7: if, elif and else

for loops

for fruit in fruits:
    print(f"I like {fruit}")

range(5) counts 0, 1, 2, 3, 4. Same indentation rule as if.

Try it: the fruits loop Section 8: for loops

Methods and dot notation

word.upper()
cat_toys.append("catnip")
  • Form: object.method(argument)
  • A string method returns a new value
  • .append changes the list
  • Polars, next workshop, is chains of these

Section 9: Methods and dot notation

Your own functions

def return_hello():
    return "hello"

print shows a value. return hands it back so you can use it again.

Try it: add_nums Section 10: Your own functions

Keep going

What to do after today

What we skipped

On purpose

  • Dates and times
  • Type conversion
  • while loops
  • Reading and writing files
  • Most methods

Where it lives The full Basics and Loops and Functions workshops cover all of it. Links are in the notebook's Keep going section.

Keep practicing

  • Practice at home in the notebook: each cell stands on its own
  • The filled notebook has every answer
  • The cheat sheet on the materials page
  • It's already on your laptop: next time, cd dss-python then uv run jupyter lab. On Colab today? Download the .ipynb and drag it in.
  • Help: go.ncsu.edu/getdatahelp
  • Questions: lib-datascienceservices@ncsu.edu

QR code for the workshop materials page, go.ncsu.edu/dsti-python

go.ncsu.edu/dsti-python

Two data activities

How people spend their day

  • The same file the spreadsheets track cleans
  • Plain Python first, then Polars
  • About 45 minutes

Campus café sales

  • Made-up data from a café register
  • Fix a bad price, then check the weather
  • About 35 minutes

Activities on go.ncsu.edu/dsti-python

Learn on your own

LinkedIn Learning is free with your NC State account. Start from the Libraries' page so you sign in through NC State.

Learning Python

Joe Marini · beginner · 3h 41m (2025)

Syntax, functions, files and data from the web, with coding challenges

linkedin.com/learning/learning-python-25309312

Python Essential Training

Ryan Mitchell · beginner · 4h 23m (2023)

Data types, control flow, functions, errors, files and modules, in Jupyter

linkedin.com/learning/python-essential-training-18764650

How to sign in: go.ncsu.edu/linkedinlearning

Next: exploratory data analysis with Polars

Today's lists, dictionaries, methods and functions are what Polars is built from.

Next in the series: tables of data, loaded and explored in Python.

More AI workshops from us

  • Colab and Chill: A Vibe Coding Approach to Exploratory Data Analysis using AI
  • Agentic Coding with Claude
  • Working with Local LLMs
  • Build Your First Public Portfolio with GitHub: AI, Version Control, and IDE Basics

QR code for the Data Science Services workshop list, go.ncsu.edu/datavizworkshops

go.ncsu.edu/datavizworkshops

Thank you

Tell us how we did

go.ncsu.edu/dss-workshop-eval

QR code for the workshop evaluation, go.ncsu.edu/dss-workshop-eval

Survey time

At 11:30 we stay here in East Learning Lab A, with the other two tracks, to complete the post-training survey.

Survey 1

Beginning of training

Survey 2: now

End of training

Survey 3

After training (3–5 months)

Plus an invitation to a 30-minute interview

Complete the survey

We'll take the next 10–15 minutes for everyone to complete the survey.

go.ncsu.edu/dsti2026_post-survey

QR code for the DSTI post-training survey, go.ncsu.edu/dsti2026_post-survey