Starting Into Python

A collection of my first Python programs, built from scratch — starting on a mobile phone before moving to a full machine. These cover the core fundamentals of Python: input handling, string manipulation, arithmetic, loops, lists, dictionaries, and object-oriented programming.

I came in with a C# background, which gave me a head start on logic and structure. The focus here was learning Python's syntax, conventions, and built-in capabilities as quickly as possible through hands-on experimentation.

Mobile app used for early learning: Python Coding
IDE / Runtime: Python 3

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Table of Contents

• Project Overview
• Programs
  • 1. PrintInput.py
  • 2. stringMethods.py
  • 3. simpleMaths.py
  • 4. range.py
  • 5. whileloopPyramid.py
  • 6. Listvalues.py
  • 7. lstdatatables.py
  • 8. dictionaries(kvpairs).py
  • 9. addDicts2List.py
  • 10. StatisticalCalculations.py
  • 11. ClassesAndMethods / Class1Employees.ipynb
• Known Issues
• Concepts Covered

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Project Overview

graph TD
    A[Python Foundations] --> B[Input & Output]
    A --> C[String Manipulation]
    A --> D[Arithmetic]
    A --> E[Control Flow]
    A --> F[Data Structures]
    A --> G[Object-Oriented Programming]

    E --> E1[if / elif / else]
    E --> E2[while loops]
    E --> E3[for loops with range]

    F --> F1[Lists]
    F --> F2[Dictionaries]
    F --> F3[List of Dictionaries]

    G --> G1[Classes and __init__]
    G --> G2[Instance Methods]
    G --> G3[Tax Bracket Logic]

    F1 --> F1a[lstdatatables.py]
    F3 --> F3a[addDicts2List.py]
    G1 --> G1a[Class1Employees.ipynb]

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Programs

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1. PrintInput.py

The first program written — collects a user's name and age, greets them, and classifies their life stage. Includes exception handling for invalid input.

Concepts: input(), string concatenation, int() type conversion, if/elif/else, try/except

Enter your first name: Romeo
Enter your last name: Ndlovu
Hello Romeo Ndlovu, welcome to the Python world.
Please enter your age: 24
Your at an adolescent age.

Age classification logic:

Age Range	Category
13 – 17	Teen
18 – 25	Adolescent
26 – 31	Peak young adult
32 – 50	Middle aged adult
51+	Retirement range

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2. stringMethods.py

Explores Python's built-in string methods against a fixed course string.

Concepts: .upper(), .lower(), .find(), .replace(), .split(), in operator, user search input

course = "Python for beginners"

Method	Output
.upper()	PYTHON FOR BEGINNERS
.lower()	python for beginners
.find("n")	5 (first index of "n")
.replace("for", "by")	Python by beginners

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3. simpleMaths.py

Demonstrates Python's arithmetic operators and how operator precedence works in compound expressions.

Concepts: +, -, *, /, BODMAS / operator precedence

x = (10 - 5) + 3 * 10 / 2
# Evaluates as: 5 + 15.0 = 20.0

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4. range.py

Uses a for loop with range(start, stop, step) to print a growing pattern of asterisks.

Concepts: for loop, range(), step parameter, string repetition with *

for number in range(5, 20, 2):
    print(number * "*")

*****
*******
*********
...

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5. whileloopPyramid.py

Creates a two-sided shape using two back-to-back while loops — one ascending, one descending.

Concepts: while loop, increment/decrement, loop direction control

#
##
###
...
##########
@@@@@@@@@@
@@@@@@@@@
...
@

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6. Listvalues.py

Demonstrates the core list operations Python provides — adding, inserting, removing, searching, and iterating.

Concepts: list[], .append(), .insert(), .remove(), in operator, while loop iteration, len()

lstname = ["John", "Sammy", "Sarah", "Linda"]

⚠️ Bug noted: lstname.find(searchname) on line 33 will raise an AttributeError — Python lists do not have a .find() method. That method belongs to strings. The correct list equivalent is lstname.index(searchname).

Fix:

# ❌ Before
print(str(lstname.find(searchname)))

# ✅ After
if searchname in lstname:
    print(str(lstname.index(searchname)))

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7. lstdatatables.py

The most layered program in the fundamentals section. Manages a pre-populated employee table using parallel lists, calculates tax deductions via a tiered function, then loops to allow the user to add new employees at runtime.

Concepts: multiple parallel lists, functions, tiered conditional logic, while loops, insert(), append(), dynamic data growth

Data Flow Diagram

flowchart TD
    A[Program Start] --> B[Initialise parallel lists\nlstnames, lstdept, lstsalary, lstid, lsttaxdeduction]
    B --> C[while icount is less than len of lstnames]
    C --> D[lstid.insert at icount]
    D --> E[Call taxrates with icount and salary]

    subgraph taxrates Function
        E --> F{Salary bracket check}
        F -->|<= 10000| G[taxrate = 8.5%]
        F -->|10001 to 14999| H[taxrate = 11%]
        F -->|15000 and above| I[taxrate = 15%]
        G & H & I --> J[taxvalue = taxrate x salary]
        J --> K[lsttaxdeduction.insert at num index]
    end

    K --> L[Print employee row:\nID, Name, Dept, Salary, Tax]
    L --> M[icount plus plus]
    M -->|loop| C
    C -->|done| N[Prompt: Add new user? Yes/No]

    subgraph Append Loop
        N -->|YES| O[Input name, dept, salary]
        O --> P[Append to all lists]
        P --> Q[Call taxrates for new entry]
        Q --> N
        N -->|NO| R[Print all lists]
    end

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Pre-loaded Employee Data

ID	Name	Department	Salary	Tax Rate
0	Sarah	HR	R12,456	15%
1	Mike	Finance	R16,500	15%
2	Thabo	Sales	R9,567	8.5%
3	Palesa	Marketing	R9,863	8.5%
4	Mlu	ICT	R10,345	11%
5	Thandiwe	Sales	R10,000	11%

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8. dictionaries(kvpairs).py

Introduces Python dictionaries as a key-value store and demonstrates accessing keys and values separately.

Concepts: dict{}, .keys(), .values(), key-value pairs

tinydict = {
    "empid": 100,
    "name": "john",
    "paycode": 6734,
    "dept": "sales",
    "salary": 16_250,
    "monthly tax": 3_650
}

Note: Python allows underscores in numeric literals (16_250) for readability — they have no effect on the value.

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9. addDicts2List.py

Combines lists and dictionaries — each element in the list is a full dictionary record, allowing structured multi-field data to be stored and iterated cleanly.

Concepts: list of dicts, .append() with dict literal, while loop, .keys(), .values()

Structure Diagram

graph TD
    L["dict  list"]
    L --> D1["dict index 0"]
    L --> D2["dict index 1"]

    D1 --> K1[empid: 100]
    D1 --> K2[name: john]
    D1 --> K3[paycode: 6734]
    D1 --> K4[dept: sales]
    D1 --> K5[salary: 16250]
    D1 --> K6[monthly tax: 3650]

    D2 --> K7[empid: 101]
    D2 --> K8[name: Ben]
    D2 --> K9[paycode: 4332]
    D2 --> K10[dept: marketing]
    D2 --> K11[salary: 10000]
    D2 --> K12[monthly tax: 1230]

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Note: naming the list variable dict shadows Python's built-in dict type. Renaming it to employees or records is the safe convention.

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10. StatisticalCalculations.py

Applies Python's standard library and manual index calculations to derive descriptive statistics from a 40-point dataset.

Concepts: sorted(), sum(), len(), manual median formula, statistics.mode, f-strings

data = [12, 18, 14, 20, 16, 22, 8, 24, 11, 13, ...]  # 40 values

Statistic	Result
Minimum	7
Maximum	30
Range	23
Mean	~16.5
Median	~16.5
Mode	15

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11. ClassesAndMethods / Class1Employees.ipynb

The most structurally complex program in the repository — introduces object-oriented programming by defining an Employee class with instance attributes and two methods: one for full name formatting and one for tiered tax calculation.

Concepts: class, __init__, self, instance attributes, instance methods, tax bracket logic, Jupyter Notebook

Class Structure

classDiagram
    class Employee {
        +string Firstname
        +string Lastname
        +string Email
        +int Salary
        +__init__(first, last, pay)
        +fullname() string
        +taxdeduction(pay) float
    }

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Object Instantiation and Method Flow

flowchart TD
    A[User Input:\nfirst, last, pay] --> B[Employee.__init__]

    subgraph __init__ Constructor
        B --> C[self.Firstname = first]
        B --> D[self.Lastname = last]
        B --> E["self.Email = first + '.' + last + '@Java.com'"]
        B --> F[self.Salary = pay]
    end

    C & D & E & F --> G[emp object created]

    G --> H[Call emp.taxdeduction with emp.Salary]

    subgraph taxdeduction Method
        H --> I{Salary bracket}
        I -->|pay <= 10000| J[8.7% rate]
        I -->|10001 to 25000| K[11.5% rate]
        I -->|25001 to 40000| L[13.5% rate]
        I -->|above 40000| M[15.7% rate]
        J & K & L & M --> N[Return pay x rate]
    end

    N --> O[Print tax deduction amount]

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Tax Bracket Reference

Salary Range	Tax Rate	Example (R22,000)
Up to R10,000	8.7%	—
R10,001 – R25,000	11.5%	R2,530
R25,001 – R40,000	13.5%	—
Above R40,000	15.7%	—

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Known Issues

File	Issue	Fix
Listvalues.py	lstname.find() does not exist on lists — this is a string method and will raise AttributeError at runtime	Replace with lstname.index(searchname) inside the if searchname in lstname check
addDicts2List.py	Variable named dict shadows Python's built-in dict type	Rename to employees or records

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Concepts Covered

Concept	Files
User input and output	PrintInput.py
String methods	stringMethods.py
Arithmetic and operator precedence	simpleMaths.py
For loops and range	range.py
While loops	whileloopPyramid.py, lstdatatables.py
List operations	Listvalues.py, lstdatatables.py
Functions and parameters	lstdatatables.py
Tiered conditional logic	lstdatatables.py, Class1Employees.ipynb
Dictionaries	dictionaries(kvpairs).py, addDicts2List.py
List of dictionaries	addDicts2List.py
Statistical calculations	StatisticalCalculations.py
Classes and OOP	ClassesAndMethods/Class1Employees.ipynb
Exception handling	PrintInput.py
Jupyter Notebook	ClassesAndMethods/Class1Employees.ipynb

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Started on mobile. Finished with curiosity.