DEV Community: abiud kipngetich

Python Data Structures, Strings, and File Handling

abiud kipngetich — Sat, 12 Sep 2026 10:44:32 +0000

1. Core Data Structures

Lists

Ordered, mutable collections that allow duplicates.
fruits = ["apple", "banana", "cherry"]

Slicing

Extract a sub-sequence using [start:stop:step]. Works on lists, strings, and tuples

numbers = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
print(numbers[2:5])        # [2, 3, 4] 
print(numbers[:3])         # [0, 1, 2] 
print(numbers[7:])         # [7, 8, 9] 
print(numbers[::2])        # [0, 2, 4, 6, 8] 
print(numbers[::-1])       # reversed list

Dictionaries

Unordered collection of key-value pairs. Keys must be unique and immutable.

person = {"name": "Alice", "age": 30} 
print(person["name"])                 # Alice 
print(person.get("email", "N/A"))     # safe lookup with default

Tuples

Ordered, immutable collections. Once created, they can't be changed.

coordinates = (10.0, 20.0) 
x, y = coordinates       # unpacking

Sets

Unordered collections of unique elements. No duplicates, no indexing.

unique_numbers = {1, 2, 2, 3, 3, 3} 
print(unique_numbers)      # {1, 2, 3}

2. List Methods, Adding and Updating Values

nums = [1, 2, 3] 
nums.append(4)       # add to end -> [1, 2, 3, 4] 
nums.insert(0, 0)    # insert at index -> [0, 1, 2, 3, 4] 
nums.extend([5, 6])  # add multiple -> [0, 1, 2, 3, 4, 5, 6] 
nums.remove(3)       # remove by value 
popped = nums.pop()  # remove and return last item 
nums.sort()          # sort in place 
nums.reverse()       # reverse in place 

nums[0] = 99         # update by index

Dictionaries update similarly:

person = {"name": "Alice"} 
person["age"] = 30               # add a new key 
person["name"] = "Jack"          # update existing key 
person.update({"city": "Nairobi", "age": 31})    # bulk update 
del person["city"]               # remove a key

Use list for an ordered, changeable collection; a tuple for fixed data that shouldn't change (e.g., coordinates); a dictionary for labeled data accessed by key; a set when you need uniqueness or fast membership testing.

3. List Comprehension

A compact way to build a list from an iterable.

squares = [n ** 2 for n in range(10)] 
evens = [n for n in range(20) if n % 2 == 0] 
upper_names = [name.upper() for name in ["ana", "bo", "cy"]]

Dictionary and set comprehensions work the same way:

square_map = {n: n ** 2 for n in range(5)} 
unique_lengths = {len(word) for word in ["hi", "bye", "ok"]}

4. Set Operations: Intersection, Union, and Unpacking

a = {1, 2, 3, 4} 
b = {3, 4, 5, 6} 

print(a | b)          # union: {1, 2, 3, 4, 5, 6}
print(a.union(b)) 

print(a & b)          # intersection: {3, 4} 
print(a.intersection(b)) 

print(a - b)          # difference: {1, 2} 
print(a ^ b)          # symmetric difference: {1, 2, 5, 6}

Unpacking

Assigning multiple variables at once from an iterable, including with * to capture "the rest."

point = (1, 2, 3) 
x, y, z = point 

first, *middle, last = [1, 2, 3, 4, 5] 
print(first, middle, last)      # 1 [2, 3, 4] 5 

def add(a, b, c): 
    return a + b +c 

values = (1, 2, 3) 
print(add(*values))          # unpack tuple as arguments

5. isnumeric() and Type-Checking Functions

"123".isnumeric()         # True 
"12.5".isnumeric()        # False (decimal point isn't numeric) 
"abc".isnumeric()         # False 

"abc".isalpha()           # True, all are alphabetic characters 
"abc123".isalnum()        # True, letters and/or numbers only 
" ".isspace()             # True, all whitespace 
"Hello World".istitle()   # True, title-cased 
"HELLO".isupper()         # True 
"hello".islower()         # True

These are commonly used to validate user input before conversion, e.g.,if age_str.isnumeric(): age = int(age_str).

6. String Methods, Concatenation, and Repetition

greeting = "Hello" + " " + "Wendy"    # concatenation -> "Hello Wendy" 
laugh = "ha" * 3                  # repetition -> "hahaha" 

text = "Python is Fun" 
print(text.upper())               # PYTHON IS FUN 
print(text.lower())               # python is fun 
print(text.title())               # Python Is Fun

7. Standardizing Text

Before comparing or storing text, it's common to normalize its case and formatting so comparisons are consistent.

user_input = "  Hello WORLD " 
standardized = user_input.strip().lower() 
print(standardized)           # "hello world"

8. Removing Extra Spaces: strip, lstrip, rstrip

text = "  padded text  " 
print(text.strip())         # "padded text" - removes both sides 
print(text.lstrip())        # "padded text "- removes left only 
print(text.rstrip())        # " padded text"- removes right only 

# _can also strip specific characters _
print("---data---".strip("-"))   # "data"

9. Splitting Text Apart

.split() breaks a string into a list based on a separator.
.splitlines() breaks a multi-line string into a list of lines.

sentence = "the quick brown fox" 
words = sentence.split()          # ['the', 'quick', 'brown', 'fox'] 

csv_line = "a,b,c,d 
fields = csv_line.split(",")      # ['a', 'b', 'c', 'd'] 

paragraph = "line one\nline two\nline three" 
lines = paragraph.splitlines()    # ['line one', 'line two', 'line three']

10. Putting Text Back Together: .join()

.join() is the inverse of .split(), it combines a list of strings using a separator string

words = ["the", "quick", "brown", "fox"] 
sentence = " ".join(words)     # "the quick brown fox" 

fields = ["a", "b", "c"] 
csv_line = ",".join(fields)    # "a,b,c" 

lines = ["line one", "line two"]
text_block = "\n".join(lines)

11. Validation Methods: count, replace, and Checking Text Kind

text = "banana" 
print(text.count("a"))                # 3 

text2 = "I like dogs" 
print(text2.replace("dogs", "cats"))   # "I like cats" 

# checking kind of text (combines with isnumeric/isalpha etc.) 
def validate_username(name):
    if not name.isalnum(): 
        return "Username must be letters/numbers only" 
    return "Valid"

12. Prefixes, Suffixes, and Professional Formatting

filename = "report_final.pdf 
print(filename.startswith("report"))   # True 
print(filename.endswith(".pdf"))       # True

plaintext

professional formatting example

name = "john thuto" 
formatted = name.title()             # "John Thuto" 

price = 1234.5 
print(f"ksh {price:,.2f})            # "Ksh 1,234.50"

plaintext

13. File Handling

Opening and closing a file

file = open("data.txt", "r")    # modes: 'r' read, 'w' write, 'a' append, 'x' create 
content = file.read() 
file.close()       # must manually close to free the resource

plaintext
Forgetting to close a file can leak resources or leave writes unflushed, this is why the with statement is preferred.

The with statement

with automatically closes the file when the block ends, even if an error occurs.

with open("data.text", "r") as file: 
     content = file.read() 
# file is automatically closed here

plaintext

Reading from a file

with open("data.txt", "r") as file: 
     content = file.read()           # entire file as one string 

with open("data.txt", "r") as file: 
    for line in file:                # iterate line by line 
        print(line.strip()) 

with open("data.txt", "r") as file: 
    lines = file.readlines()         # list of lines, icluding \n

plaintext

Writing to a file

with open("output.txt"z, "w") as file: 
    file.write("Hello, World!\n") 

with open("output.txt", "a") as file:    # append mode adds without erasing 
    file.write("Another line.\n")

Python Fundamentals: Variables, Control Flow, and Functions

abiud kipngetich — Fri, 11 Sep 2026 10:46:06 +0000

1. Variables and Naming Conventions

A variable is a name bound to a value in memory. Python is dynamically typed, so you don't declare a type, the type is inferred from the value assigned .
age = 25 price = 19

Snake_case

Python's official style guide (PEP 8) recommends snake_case for variable and functions names: lowercase words separated by underscores.
first_name = "Jonah" total_price = 50
Constants are typically written in ALL_CAPS:
PI = 3.14159

2. input() and f-strings

input() pauses execution and reads a line of text from the user as a string
name = input("What is your name? ") age = int(input("what is your age? "))# convert to int explicitly
f-strings(formatted string literals) let you embed expressions directly inside a string using {}

name = "Bud" 
age = 30 
print(f"{name} is {age} years old."

3. Operators and Conditionals

Operator-Meaning

== equal to
!= not equal to
>,< greater/less than
>=,<= greater/less than or equal

if, elif, else

score = 82 
if score >= 90: 
    grade = "A" 
elif score >= 80: 
    grade = "B" 
elif score >= 70: 
    grade = "C" 
else:
    grade = "F" 
print(grade)    #  B

Logical operators

and, or, not combine or invert boolean conditions.

temperature = 55 
is_sunny = True 
if temperature > 50 and is_sunny: 
    print("Great beach day") 
if temperature < 30 or temperature > 70: 
    print("Extreme weather") 
if not is_sunny: 
    print("Bring an umbrella")

4. Loops

for loop

Iterates over a sequence (list, string, range, etc.)

fruits = ["apple", "banana", "cherry"] 
for fruit in fruits: 
    print(fruit)

range()

Generates a sequence of numbers, commonly used with for.

for i in range(5):     # 0, 1, 2, 3, 4
    print(i) 
for i in range(2,10,2):     # _start, stop, step -> 2,4,6,8_
    print(i)

while loop

Repeats as long as a condition is true.

count = 0 
while count < 5: 
    print(count) 
    count += 1

while True

An infinite loop that must be broken out of explicitly, useful for menus or input validation.

while True: 
    answer = input("Type 'quit' to exit: " 
    if answer == "quit": 
        break

Accumulators

A pattern where a variable collects a running total or result across loop iterations.

total = 0 
for number in [10, 20, 30]: 
    total += number 
print(total)    # 60 

# Accumulating into a list 
squares = [] 
for n in range(5): 
    squares.append(n ** 2) 
print(squares)    # [0, 1, 4, 9, 16]

5. break and continue

break exits the loop immediately.
continue skips to the next iteration.

for n in range(10): 
    if n == 5: 
        break     # stop entirely once n hits 5 
    print(n) 
for n in range (10): 
    if n % 2 == 0: 
        continue      # skip even numbers 
    print(n)

6. enumerate()

Gives you both the index and the value while looping, cleaner than manually tracking a counter.

colors = ["red", "green", "blue"] 
for index, color in enumerate(colors): 
    print(index, color) 
# 0 red 
# 1 green 
# 2 blue 

for index, color in enumerate(colors, start=1): 
    print(f"{index}. {color}")

7. Functions and return

Functions bundle reusable logic under a name.
return sends a value back to the caller and ends the function.

def square(x): 
    return x ** 2 
result = square(4)v 
print(result)           # 16 

def greet(name): 
    return f"Hello, {name}!" 

def add_and_multiply(a,b, factor): 
    total = (a + b) * factor 
    return total

Afunction without an explicit return returns None

8. Parameters, Arguments, and Scope

Parameters are the names listed in a function's definition. Arguments are the actual values passed in when calling it.

def introduce(name, age):     # name, age are parameters 
    print(f"{name} is {age}") 

introduce("Sam", 22)          # "Sam", 22 are arguments

Functions can have default parameter values and keyword arguments:

def power(base, exponent=2): 
    return base ** exponent 

print(power(5))        # 25 (uses default exponent)
print(power(5, 3))     # 125 
print(power(base=2, exponent=10))     # 1024

Local vs global variables

A local variable is defined inside a function and only exists there. A global variable is defined at the top level of a script and accessible everywhere (read access by default).

counter = 0    # global variable 
def increment(): 
    local_value = 1      # local variable, disappears after the function runs
    print(local_value) 

increment() 
# print(local_value)   # Error: local_value doesn't exist here 

def modify_global() 
    global counter     # explicitly declare intent to modify the global 

modify_global() 
print(counter)     # 1

Without the global keyword, assigning to a variable inside a function creates a new local variable instead of modifying the outer one.

Power BI Dashboard on Sales & Performance Logistics

abiud kipngetich — Wed, 08 Jul 2026 16:38:45 +0000

I recently built a Power BI dashboard analyzing sales and delivery performance for a car logistics company as part of a class project. It covers revenue by branch, sales rep performance, payment methods, and delivery status tracking: Check it out here