Lecture1 Python Basics

Main ideas and lessons

Purpose of the lecture (Python for engineering)

• The course introduces Python specifically for engineering, gradually building coding skills through lecture content and interactive lab exercises.
• Students learn by working in an environment where they can:
  • Experiment with code
  • Solve instruction-based tasks
  • Receive customized, instant feedback

Why Python became widely used

• Invented by Guido van Rossum: it started as a project and became a general-purpose programming language.
• Python is open source and free to use.
• Python supports building packages (reusable code shared to solve specific problems).
• There is a growing ecosystem of packages for data science and engineering, such as:
  • Visualization packages (e.g., for company sales data)
  • Database connectivity packages (e.g., analyzing sensor measurements)
• Common analogy: Python as the “Swiss Army knife” because it can almost do anything.
• The course focus is to build engineering coding competence step-by-step.

Two ways to run Python

1. Python Shell (interactive)

   • Type code and immediately see results.
   • The lecture uses iPython (Interactive Python) rather than the original shell.
   • iPython was created by Fernando Pérez and is part of the Jupyter ecosystem.
   • Example (calculator-like usage): 4 + 5 → 9

2. Python scripts (saved code)

   • Python scripts are text files with extension .py.
   • A script is a sequence of Python commands executed line-by-line.
   • Scripts don’t automatically display results unless you explicitly use:
     • print(...)
   • Demonstrated workflow:
     • Put a calculation inside print(...)
     • Run the script (via Run code / Submit answer in the interface)
     • Output appears only because print is used
   • Benefit emphasized: scripting keeps code structured, avoids repetitive retyping, and makes edits easier.

Practicing Python basics with calculations

• Demonstrates calculator-like operations:
  • addition, subtraction, multiplication, division
• Emphasizes correct use of print(...) to display results.
• Notes: Python can handle basic arithmetic, supporting engineering calculations.

Variables: storing values and reusing them

• Variables store a value under a name (case-sensitive).
• Example concept: measured height and weight:
  • Assign values using = (assignment)
  • Later refer to the variable name to retrieve the stored value
• Derived quantity example: BMI
  • BMI = weight / (height ** 2)
  • BMI is stored in a new variable and can be recalculated by changing input variables (e.g., weight).

Data types and the role they play

• Python values have types; use type(...) to inspect them.
• Main types discussed:
  • int: integers
  • float: real numbers (decimal / fractional part)
  • str: strings (text, created using single or double quotes)
  • bool: boolean values True/False (capitalization matters)
• Key principle: operators behave differently depending on data types.
  • Example:
    • + adds numbers for integers
    • + concatenates strings for strings
• Lesson: type awareness is important because it changes how code behaves.

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Methodology / instructions presented (detailed)

A) Using the Python Shell vs. scripts

• In the shell:
  • Type an expression (e.g., 4 + 5)
  • Press Enter
  • The shell interprets and immediately prints the result
• In a .py script:
  • Write code in a text file with extension .py
  • If you want output, explicitly add:
    • print(<expression>)
  • Run the script using the interface button (e.g., Run code / Submit answer)
  • If output is missing, check whether print(...) was used

B) Computing with variables (height/weight/BMI)

• Assign measurements to variables:
  • height = <value>
  • weight = <value>
• Compute a derived value:
  • BMI = <formula using height and weight>
• Recalculate by:
  • changing the assigned input variables
  • rerunning the script (BMI updates accordingly)

C) Inspecting types with type()

• Check the type of a value/variable:
  • type(<variable>)
• Check the type of an expression result:
  • compute the expression
  • store in a variable
  • then:
    • print(type(<result_variable>))

D) Practice instructions: variable assignment and printing

• Task 1 (variable saving):

  • Create a variable named savings and assign it 100
  • Print it:
    • print(savings)

• Task 2 (compute money saved over months):

  • Create:
    • monthly_saving = 10
    • number_of_months = 4
  • Compute total:
    • new_savings = monthly_saving * number_of_months
  • Display:
    • print(new_savings) (expected: 40)

• Task 3 (work with different data types):

  • Create a float variable half with value 5 (implied decimal form)
  • Create a string variable intro:
    • "Hello, how are you?"
  • Create a bool variable is_good:
    • True or False (capitalization matters)
  • Test by printing variables (e.g., print(half))

E) Type-checking results of operations

• Create expressions combining typed variables (example described: summing savings values)
• Assign the result to total_savings
• Determine output type by:
  • print(type(total_savings))
• Compare with string behavior (example indicates concatenating strings by using:
  • double_intro = intro + intro)

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Speakers / sources featured

• Guido van Rossum — credited as the creator of Python.
• Fernando Pérez — credited with creating iPython (Interactive Python).
• Narrator / Lecture presenter — implied as the person delivering the instruction (no name provided in subtitles).