Skip to content

06-data-structures.md

Latest commit

 

History

History
127 lines (90 loc) · 3.76 KB

06-data-structures.md

File metadata and controls

127 lines (90 loc) · 3.76 KB

06 | Data Structures

Modules

Python ships with a handful of core data structures. Learn these well and you can model almost any engineering problem without reaching for third-party libraries.

Numbers (including booleans)

span = 6            # int (metres)
load = 2.5          # float (kPa)
is_serviceable = True  # bool

Numbers behave like you expect. Booleans are simply True or False, and they slot neatly into comparisons and control flow.

Strings

Strings store text. They are immutable, which means you create new strings when you transform them.

material = "Concrete 40 MPa"
material.lower()      # 'concrete 40 mpa'
material.split()      # ['Concrete', '40', 'MPa']
material.replace("Concrete", "Steel")

Use strings for tags, filenames, report headings, or any other text you need to handle.

Lists

Lists are ordered, mutable collections. Great for sequences of anything, numbers, strings, even other lists.

beam_lengths = [5.2, 6.7, 4.8, 7.1]
beam_lengths.append(6.0)
beam_lengths.sort()

load_cases = [
    {"name": "Dead", "factor": 1.2},
    {"name": "Live", "factor": 1.5},
]

List comprehension packs iteration and conditional logic into a single line:

squared_lengths = [length ** 2 for length in beam_lengths if length > 5]

Tuples

Tuples look like lists but cannot be changed after creation. Use them when the data represents a fixed record.

# (x, y, z) coordinates of a column base
column_base = (0.0, 0.0, 0.0)

# Material properties: (name, density, elastic_modulus)
steel_grade = ("Steel", 7850, 200e9)

Tuples keep their shape, which is handy when you want to guarantee that a set of values stays intact. (Full disclosure: I reach for lists 90% of the time, but tuples shine when immutability matters.)

Sets

Sets store unique, unordered values. They shine when you need membership checks or mathematical set operations.

materials_used = {"Concrete", "Steel", "Timber"}
materials_on_site = {"Steel", "Aluminum", "Concrete"}

new_orders = materials_on_site - materials_used   # {'Aluminum'}
common_materials = materials_used & materials_on_site  # {'Concrete', 'Steel'}

Convert a list to a set to remove duplicates quickly:

unique_sections = set(["W310", "W360", "W310"])

Dictionaries

Dictionaries map keys to values. They are the workhorse of most Python applications because you can describe rich objects without writing classes.

beam = {
    "label": "B1",
    "material": "Steel",
    "length_m": 8.0,
    "section": "W360",
    "loads_kN": [45, 60, 30],
}

beam["material"]          # 'Steel'
beam.get("camber_mm", 0)   # Returns default when missing
beam.keys()                # dict_keys(['label', 'material', ...])

You can nest dictionaries, lists, and any other structures to model complex objects. Just keep things readable.

Choosing the Right Structure

Use this When you need
Number (int, float, bool) Calculations, flags, counters
str Text, IDs, file paths
list Ordered, changeable collection
tuple Fixed-size record that should not change
set Unique members and fast membership tests
dict Named fields and structured data

Spend time with these. Most engineering problems can be expressed as lists of dictionaries, dictionaries of lists, or combinations of the above.

(c) Flocode, 2025