Data Structures: Lists in Python

---

Introduction to Lists

Lists are used to store multiple items in a single variable.

They are one of the main built-in collection data types in Python.

---

Key Characteristics of Lists

- Ordered → Items keep their position
- Changeable → Items can be modified
- Allow Duplicates → Same value can appear multiple times

---

Creating a List

thislist = ["apple", "banana", "cherry"]

print(thislist)

---

Accessing List Items

Lists use indexes.

thislist = ["apple", "banana", "cherry"]

print(thislist[0])
print(thislist[1])

Output:

apple
banana

---

List Indexing

"apple"   "banana"   "cherry"
   0          1           2
  -3         -2          -1

- Positive indexes start from the beginning
- Negative indexes start from the end

---

Negative Indexing

thislist = ["apple", "banana", "cherry"]

print(thislist[-1])

Output:

cherry

---

Range of Indexes

The start index is included, but the end index is excluded.

thislist = ["apple", "banana", "cherry", "orange", "kiwi"]

print(thislist[2:4])

Output:

['cherry', 'orange']

---

Changing List Items

Use the index number to modify an item.

thislist = ["apple", "banana", "cherry"]

thislist[1] = "blackcurrant"

print(thislist)

---

List Length

Use len() to count items.

print(len(thislist))

---

Essential List Methods

---

append()

Adds an item to the end.

fruits = ["apple", "banana"]

fruits.append("orange")

print(fruits)

---

insert()

Adds an item at a specific position.

fruits.insert(1, "cherry")

print(fruits)

---

remove()

Removes a specific item.

fruits.remove("banana")

print(fruits)

---

pop()

Removes the last item.

fruits.pop()

print(fruits)

---

Membership Test

Use in to check if an item exists.

if "apple" in fruits:
    print("Yes, it is in the list!")

---

Practice Exercises

1. Create a list of 5 favorite movies and print the third item.

2. Replace the second item in a list with a new value.

3. Add two new items to a list using:
- append()
- insert()

4. Check if "banana" exists in a list.

Advanced List Operations in Python

---

Adding List Items

Python provides multiple ways to add items to a list.

---

append()

Adds an item to the end of the list.

fruits = ["apple", "banana"]

fruits.append("orange")

print(fruits)

Output:

['apple', 'banana', 'orange']

---

insert()

Adds an item at a specific index.

fruits = ["apple", "banana"]

fruits.insert(1, "cherry")

print(fruits)

---

extend()

Adds elements from another iterable.

fruits = ["apple", "banana"]
tropical = ["mango", "pineapple"]

fruits.extend(tropical)

print(fruits)

---

Removing List Items

Python provides different methods for removing items.

---

remove()

Removes the first matching value.

fruits = ["apple", "banana", "banana"]

fruits.remove("banana")

print(fruits)

---

pop()

Removes an item by index.

fruits = ["apple", "banana", "cherry"]

fruits.pop(1)

print(fruits)

Without an index, pop() removes the last item.

---

del Keyword

Delete a specific item:

fruits = ["apple", "banana", "cherry"]

del fruits[0]

print(fruits)

Delete the entire list:

del fruits

---

clear()

Removes all items but keeps the list object.

fruits = ["apple", "banana", "cherry"]

fruits.clear()

print(fruits)

---

List Comprehension

A shorter and cleaner way to create lists.

Traditional way:

fruits = ["apple", "banana", "cherry", "kiwi", "mango"]

newlist = []

for x in fruits:
    if "a" in x:
        newlist.append(x)

print(newlist)

List comprehension way:

fruits = ["apple", "banana", "cherry", "kiwi", "mango"]

newlist = [x for x in fruits if "a" in x]

print(newlist)

---

Sorting Lists

Sort alphabetically:

fruits = ["orange", "mango", "kiwi", "banana"]

fruits.sort()

print(fruits)

Reverse sorting:

fruits.sort(reverse=True)

print(fruits)

---

Important Note

Sorting is case-sensitive.

Capital letters come before lowercase letters.

---

Copying Lists

Wrong way:

list2 = list1

This only creates a reference.

---

Correct Ways to Copy

Using .copy():

new_copy = fruits.copy()

Using `list():

``python
another_copy = list(fruits)

---

Joining Two Lists

Using `+` operator:

python
list1 = ["a", "b"]
list2 = [1, 2]

list3 = list1 + list2

print(list3)

Using a loop:

python
for x in list2:
list1.append(x)

print(list1)
`

---

Practice Exercises

1. Use list comprehension to convert all items in a list to uppercase.

2. Sort a list based on how close numbers are to 50.

3. Reverse a list using `.reverse()`.

Basic Python Course
Data Structures: Tuples

By Yehya Oumer
(Certificate from Mizan Institute of Technology)

Issued: 2026

---

1. Introduction to Tuples

Tuples are used to store multiple items in a single variable.

They are one of the 4 built-in collection data types in Python.

---

Characteristics of Tuples

- Ordered → Items keep their position
- Unchangeable (Immutable) → Items cannot be modified after creation
- Allow Duplicates → Multiple items can have the same value

---

Creating a Tuple

thistuple = ("apple", "banana", "cherry")

print(thistuple)

---

One-Item Tuple Trick

To create a tuple with only one item, you must include a comma.

Correct:

thistuple = ("apple",)

Wrong:

thistuple = ("apple")

Without the comma, Python treats it as a string.

---

2. Updating Tuples (The Workaround)

Tuples are immutable, meaning they cannot be changed directly.

However, there is a common workaround:

1. Convert tuple to list
2. Modify the list
3. Convert back to tuple

Example:

x = ("apple", "banana", "cherry")

y = list(x)      # Convert to list
y[1] = "kiwi"   # Modify the list

x = tuple(y)    # Convert back to tuple

print(x)

---

3. Unpacking Tuples

Packing means storing values into a tuple.

Unpacking means extracting tuple values into variables.

fruits = ("apple", "banana", "cherry")

(green, yellow, red) = fruits

print(green)

Output:

apple

---

Using the Asterisk (*)

If variables are fewer than values, use * to collect the remaining values.

fruits = ("apple", "banana", "cherry", "orange")

(green, yellow, *others) = fruits

print(others)

Output:

['cherry', 'orange']

---

4. Join and Multiply Tuples

Join Tuples

Use + to combine tuples.

tuple1 = ("a", "b")
tuple2 = (1, 2)

tuple3 = tuple1 + tuple2

print(tuple3)

---

Multiply Tuples

Use * to repeat tuple content.

fruits = ("apple", "banana")

print(fruits * 2)

Output:

('apple', 'banana', 'apple', 'banana')

---

5. Why Use Tuples?

You may ask:
Why use tuples if lists already exist?

Here are two important reasons:

---

Performance

Tuples are slightly faster than lists because they are immutable.

---

Data Safety

Tuples protect data from accidental modification.

Useful for:
- Coordinates
- Days of the week
- Months of the year
- Fixed configuration data

---

Practice Exercises

1. Reverse this tuple:

mytuple = ("apple", "banana", "cherry")

2. Check if "banana" exists inside the tuple using in.

3. Use .count() to count how many times a value appears in a tuple.

Basic Python Course
Data Structures: Sets

By Yehya Oumer
(Certificate from Mizan Institute of Technology)

Issued: 2026

---

1. Understanding Sets

Sets are used to store multiple items in a single variable.

They are written using curly brackets {}.

---

Characteristics of Sets

- Unordered → Items do not have a fixed order
- Unchangeable → Items themselves cannot be modified directly
- Unindexed → No index numbers like lists
- No Duplicates → Duplicate values are automatically removed

---

Example

thisset = {"apple", "banana", "cherry", "apple"}

print(thisset)

Output:

{'apple', 'banana', 'cherry'}

Notice:
"apple" appears only once.

---

2. Modifying a Set

Adding One Item

Use .add():

thisset = {"apple", "banana", "cherry"}

thisset.add("orange")

print(thisset)

---

Adding Multiple Items

Use .update():

thisset.update(["mango", "kiwi"])

print(thisset)

---

Removing Items

Using remove():

thisset.remove("banana")

Important:
remove() raises an error if the item does not exist.

---

Using discard()

thisset.discard("banana")

Difference:
discard() does NOT raise an error if the item is missing.

---

3. Set Operations (Logic)

Sets are powerful because they support mathematical operations.

---

Union (Combine All Items)

Method:

set1.union(set2)

Operator:

set1 | set2

Example:

set1 = {"a", "b"}
set2 = {1, 2}

print(set1 | set2)

---

Intersection (Common Items Only)

Method:

set1.intersection(set2)

Operator:

set1 & set2

---

Difference (Items in Set1 but NOT in Set2)

Method:

set1.difference(set2)

Operator:

set1 - set2

---

Symmetric Difference

Keeps items that exist in either set, but NOT both.

Method:

set1.symmetric_difference(set2)

Operator:

set1 ^ set2

---

Boolean Note

In Python sets:

True == 1
False == 0

So these are treated as duplicate values.

Example:

myset = {True, 1, False, 0}

print(myset)

Output:

{False, True}

---

4. Why Use Sets?

Removing Duplicates Instantly

Sets are perfect for cleaning repeated data.

Example:

names = ["Ali", "Sara", "Ali", "Hana", "Sara"]

unique_names = set(names)

print(unique_names)

Output:

{'Ali', 'Sara', 'Hana'}

---

Practice Exercises

1. Disjoint Check

Write a program to check if two sets have no common items without using built-in functions.

---

2. Unique Counter

Given a list of student names, count the number of unique names.

---

3. Symmetric Difference

Use the ^ operator to find items that exist in either Set A or Set B, but not both.

Basic Python Course
Data Structures: Sets

By Yehya Oumer
(Certificate from Mizan Institute of Technology)

Issued: 2026

---

1. Understanding Sets

A Set is a collection used to store multiple unique items in a single variable.

Sets are written using curly brackets {}.

Example:

thisset = {"apple", "banana", "cherry"}

print(thisset)

---

Characteristics of Sets

- Unordered → Items do not keep a fixed order
- Unindexed → No index numbers
- No Duplicates → Repeated values are automatically removed
- Mutable → You can add or remove items

---

Duplicate Values are Ignored

thisset = {"apple", "banana", "cherry", "apple"}

print(thisset)

Output:

{'apple', 'banana', 'cherry'}

Even though "apple" appears twice, it is stored only once.

---

2. Modifying a Set

Adding One Item

Use .add():

thisset = {"apple", "banana"}

thisset.add("orange")

print(thisset)

---

Adding Multiple Items

Use .update():

thisset.update(["mango", "kiwi"])

print(thisset)

---

Removing Items

Using remove():

thisset.remove("banana")

Important:
If the item does not exist, Python raises an error.

---

Using discard()

thisset.discard("banana")

Difference:
discard() does not raise an error if the item is missing.

---

3. Set Operations (Logic)

Sets support mathematical operations.

These operations are extremely useful in programming and data analysis.

---

Union

Combines all unique values from both sets.

Method:

set1.union(set2)

Operator:

set1 | set2

Example:

set1 = {"a", "b"}
set2 = {1, 2}

print(set1 | set2)

---

Intersection

Returns only common values.

Method:

set1.intersection(set2)

Operator:

set1 & set2

Example:

set1 = {"apple", "banana"}
set2 = {"banana", "orange"}

print(set1 & set2)

Output:

{'banana'}

---

Difference

Returns values in the first set but not in the second set.

Method:

set1.difference(set2)

Operator:

set1 - set2

---

Symmetric Difference

Returns values that are NOT shared between both sets.

Method:

set1.symmetric_difference(set2)

Operator:

set1 ^ set2

---

Boolean Note

In Python sets:

True == 1
False == 0

So Python treats them as duplicate values.

Example:

myset = {True, 1, False, 0}

print(myset)

Output:

{False, True}

---

4. Why Use Sets?

Removing duplicates instantly.

Example:

names = ["Ali", "Sara", "Ali", "Hana", "Sara"]

unique_names = set(names)

print(unique_names)

Output:

{'Ali', 'Sara', 'Hana'}

---

Real-World Uses of Sets

- Removing duplicate usernames
- Finding common interests between users
- Comparing datasets
- Membership testing

---

Practice Exercises

1. Disjoint Check

Write a program to check if two sets have zero common items without using built-in functions.

---

2. Unique Counter

Given a list of student names, count how many unique names exist.

---

3. Symmetric Difference

Use the ^ operator to find items that exist in either Set A or Set B, but not both.

Basic Python Course
Data Structures: Dictionaries

By Yehya Oumer
(Certificate from Mizan Institute of Technology)

Issued: 2026

---

1. Understanding Dictionaries

Dictionaries are used to store data in key:value pairs.

Think of a real dictionary:
- Word → Key
- Meaning → Value

Example:

thisdict = {
    "brand": "Ford",
    "model": "Mustang",
    "year": 1964
}

print(thisdict["brand"])

Output:

Ford

---

Characteristics of Dictionaries

- Ordered → Python keeps the insertion order
- Changeable → Items can be updated or removed
- No Duplicate Keys → Duplicate keys overwrite old values

Example:

thisdict = {
    "brand": "Ford",
    "brand": "BMW"
}

print(thisdict)

Output:

{'brand': 'BMW'}

---

2. Managing Data

Accessing Items

Using square brackets:

print(thisdict["model"])

Using `.get():

``python
print(thisdict.get("year"))

Difference:
`.get()` avoids errors if the key does not exist.

---

Changing and Adding Items

Update an existing value:

python
thisdict["year"] = 2020

Add a new key:value pair:

python
thisdict["color"] = "red"

Example:

python
print(thisdict)

---

3. Looping Through Dictionaries

Loop Through Keys

python
for x in thisdict:
print(x)

---

Loop Through Values

python
for x in thisdict.values():
print(x)

---

Loop Through Both Keys and Values

python
for x, y in thisdict.items():
print(x, y)

---

4. Nested Dictionaries

A dictionary can contain other dictionaries.

This is useful for:
- User profiles  
- Databases  
- Structured data  

Example:

python
myfamily = {
"child1": {
"name": "Emil",
"year": 2004
},

"child2": {
"name": "Tobias",
"year": 2007
}
}

Access nested data:

python
print(myfamily["child2"]["name"])

Output:

python
Tobias

---

5. View Objects (Added Value)

The `.keys()` and `.values()` methods return dynamic view objects.

This means if the dictionary changes, the views update automatically.

Example:

python
x = thisdict.keys()

thisdict["color"] = "white"

print(x)

---

Dictionary Methods Summary

---

.pop(key)

Removes a specific item.

python
thisdict.pop("model")

---

.clear()

Removes all items.

python
thisdict.clear()

---

.update()

Updates the dictionary with another dictionary.

python
thisdict.update({"color": "blue"})

---

Real-World Uses of Dictionaries

- User account information  
- Product databases  
- API responses  
- Student records  
- Configuration settings  

---

Practice Exercises

1. Inventory Update

Create a dictionary of store items and prices. Increase the price of one item by 10%.

---

2. Nested Access

From the `myfamily` dictionary, print only the name of `"child2"`.

---

3. Key Checker

Check if `"model"` exists inside `thisdict`.

Example:

python
if "model" in thisdict:
print("Yes, model exists")
`

Basic Python Course
Introduction to NumPy

By Yehya Oumer
(Certificate from Mizan Institute of Technology)

Issued: 2026

---

1. What is NumPy?

NumPy stands for Numerical Python.

It is a powerful Python library used for:
- Numerical Computing
- Arrays and Matrices
- Scientific Calculations
- Data Analysis
- Artificial Intelligence and Machine Learning

NumPy is one of the most important libraries in the Python ecosystem.

---

Why NumPy is Important

Speed:
NumPy arrays are much faster than normal Python lists.

Memory Efficiency:
Arrays use less memory compared to standard Python lists.

Advanced Mathematics:
Supports matrix operations, statistics, linear algebra, and more.

Foundation of AI:
Libraries like Pandas, TensorFlow, and Scikit-learn rely heavily on NumPy.

---

2. Importing NumPy

Before using NumPy, it must be imported.

import numpy as np

`np` is simply a shorter alias for NumPy.

---

3. Creating Arrays

Arrays are the core structure of NumPy.

import numpy as np

arr = np.array([1, 2, 3, 4, 5])

print(arr)

Output:

[1 2 3 4 5]

---

4. Array Dimensions

NumPy supports multiple dimensions.

| Dimension | Description |
|-----------|-------------|
| 0-D | Single value |
| 1-D | Simple array |
| 2-D | Matrix/Table |
| 3-D | Collection of matrices |

---

2-D Array Example

arr2 = np.array([
    [1, 2, 3],
    [4, 5, 6]
])

print(arr2)

---

Checking Dimensions

Use .ndim:

print(arr2.ndim)

Output:

2

---

5. Array Shape

The .shape attribute returns the size of each dimension.

print(arr2.shape)

Output:

(2, 3)

Meaning:
- 2 rows
- 3 columns

---

6. Slicing Arrays

Slicing works similarly to Python lists.

Example:

print(arr2[0, 1:3])

Explanation:
- Row index → 0
- Column indexes → 1 to 2

Output:

[2 3]

---

7. Reshaping Arrays

Reshaping changes the structure without changing the data.

arr = np.array([1, 2, 3, 4, 5, 6, 7, 8])

newarr = arr.reshape(2, 4)

print(newarr)

Output:

[[1 2 3 4]
 [5 6 7 8]]

---

8. Searching Arrays

Use np.where() to find indexes.

arr = np.array([1, 2, 3, 4, 5, 6])

x = np.where(arr == 4)

print(x)

Output:

(array([3]),)

Meaning:
The value 4 exists at index 3.

---

9. Vectorized Operations

NumPy can perform operations on entire arrays without loops.

arr = np.array([1, 2, 3])

arr = arr + 1

print(arr)

Output:

[2 3 4]

Every element increases by 1 instantly.

---

10. Why NumPy is Powerful

Without NumPy:

numbers = [1, 2, 3]

new_numbers = []

for x in numbers:
    new_numbers.append(x + 1)

print(new_numbers)

With NumPy:

arr = np.array([1, 2, 3])

print(arr + 1)

Cleaner, faster, and more efficient.

---

Real-World Uses of NumPy

- Artificial Intelligence
- Machine Learning
- Data Science
- Image Processing
- Scientific Research
- Game Development

---

Practice Exercises

1. Create a 5-D array using ndmin.

2. Find indexes of all even numbers:

arr = np.array([1, 2, 3, 4, 5, 6])

3. Convert a 1-D array with 8 elements into a 2x4 matrix using .reshape().

Basic Python Course
NumPy Part 2: Randomness & Math Functions

By Yehya Oumer
(Certificate from Mizan Institute of Technology)

Issued: 2026

---

1. Random Numbers in NumPy

In programming, random numbers are usually pseudo-random numbers.

This means:
- They are generated using algorithms
- They appear random
- But they are not truly random like natural randomness

NumPy provides powerful tools for generating random data.

Import random module:

from numpy import random

---

Random Integers

Use randint() to generate random integers.

from numpy import random

x = random.randint(100)

print(x)

Output example:

57

This generates a random integer from:
- 0 → 99

---

Random Floats

Use rand() to generate random decimal values between 0 and 1.

y = random.rand(5)

print(y)

Example output:

[0.24 0.81 0.55 0.91 0.13]

---

Random Choice

Use choice() to randomly select values from an array.

arr = [3, 5, 7, 9]

x = random.choice(arr)

print(x)

Possible output:

7

---

2. NumPy Arithmetic (Universal Functions)

NumPy uses Universal Functions (ufuncs) to perform fast mathematical operations on arrays.

These operations are:
- Faster than loops
- Cleaner to write
- Optimized internally

---

Example Arrays

import numpy as np

arr1 = np.array([10, 20, 30])
arr2 = np.array([3, 5, 7])

---

Addition

add_arr = np.add(arr1, arr2)

print(add_arr)

Output:

[13 25 37]

---

Subtraction

sub_arr = np.subtract(arr1, arr2)

print(sub_arr)

---

Multiplication

mult_arr = np.multiply(arr1, arr2)

print(mult_arr)

---

Division

div_arr = np.divide(arr1, arr2)

print(div_arr)

---

3. Rounding Decimal Numbers

NumPy provides multiple functions for handling decimals.

---

trunc() / fix()

Removes decimal values toward zero.

arr = np.trunc([-3.7, 3.7])

print(arr)

Output:

[-3.  3.]

---

around()

Rounds to the nearest decimal place.

arr = np.around(3.1666, 2)

print(arr)

Output:

3.17

---

floor()

Rounds downward.

arr = np.floor([3.7])

print(arr)

Output:

[3.]

---

ceil()

Rounds upward.

arr = np.ceil([3.2])

print(arr)

Output:

[4.]

---

4. Trigonometry and Logarithms

NumPy can perform advanced mathematics easily.

---

Sine Function

import numpy as np

val = np.sin(np.pi / 2)

print(val)

Output:

1.0

---

Logarithm Base 2

arr = np.array([1, 2, 4, 8])

print(np.log2(arr))

Output:

[0. 1. 2. 3.]

Explanation:
- log₂(1) = 0
- log₂(2) = 1
- log₂(4) = 2
- log₂(8) = 3

---

5. Data Distribution

In Machine Learning and AI, we often need data that follows a distribution.

One of the most common is the Normal Distribution (Bell Curve).

---

Normal Distribution Example

from numpy import random

x = random.normal(size=(2, 3))

print(x)

This generates:
- Random numbers
- Following a Gaussian distribution
- Inside a 2x3 matrix

---

Why Distributions Matter

Distributions are heavily used in:
- Artificial Intelligence
- Machine Learning
- Statistics
- Simulations
- Data Science

---

Practice Exercises

1. Random Choice

Create an array of 5 names and use random.choice() to select a winner.

---

2. Ceiling & Floor

Create this array:

arr = np.array([1.2, 2.8, 3.5])

Print:
- Floor values
- Ceiling values

---

3. Random Matrix

Generate a 3x5 matrix of random integers between 0 and 50.

Basic Python Course
Data Analysis with Pandas

By Yehya Oumer
(Certificate from Mizan Institute of Technology)

Issued: 2026

---

1. Introduction to Pandas

Pandas is one of the most powerful and popular Python libraries for data analysis.

It helps programmers:
- Analyze data
- Clean messy datasets
- Organize information
- Discover patterns

Pandas is built on top of NumPy, making it fast and efficient.

---

Why Use Pandas?

Data Cleaning:
Fix missing or incorrect values.

Data Exploration:
Understand what exists inside your dataset.

Speed:
Handles large files efficiently.

Real-World Usage:
Used heavily in:
- Data Science
- Artificial Intelligence
- Business Analytics
- Machine Learning

---

2. Importing Pandas

Before using Pandas:

import pandas as pd

`pd` is simply a short alias for Pandas.

---

3. Creating a DataFrame

A DataFrame is like a spreadsheet or table.

Example:

import pandas as pd

data = {
    "calories": [420, 380, 390],
    "duration": [50, 40, 45]
}

df = pd.DataFrame(data)

print(df)

Output:

   calories  duration
0       420        50
1       380        40
2       390        45

---

4. Series vs DataFrame

Pandas mainly uses two structures.

---

Series

A one-dimensional structure.

Example:

series = pd.Series([1, 2, 3])

print(series)

---

DataFrame

A multi-dimensional table.

Example:

df = pd.DataFrame(data)

Think of:
- Series → One column
- DataFrame → Entire spreadsheet

---

5. Reading CSV Files

CSV stands for Comma Separated Values.

Most real-world datasets are stored as CSV files.

Example:

df = pd.read_csv("data.csv")

print(df.to_string())

---

Useful Tip

By default, Pandas only shows part of large datasets.

Increase visible rows:

pd.options.display.max_rows = 9999

---

6. Inspecting Data

---

head()

Shows the first rows.

print(df.head())

---

tail()

Shows the last rows.

print(df.tail())

---

info()

Displays important dataset information.

print(df.info())

This shows:
- Column names
- Data types
- Non-null values
- Memory usage

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What Does "Non-Null" Mean?

Non-null means:
A cell contains actual data and is NOT empty.

Example:

Age
21
18
NULL
25

Only 3 rows are non-null.

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7. Introduction to Data Cleaning

Real-world datasets are often messy.

Pandas provides tools to clean them.

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Removing Empty Rows

new_df = df.dropna()

This removes rows with missing values.

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Replacing Missing Values

df.fillna(130, inplace=True)

This replaces empty cells with 130.

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Checking Duplicates

print(df.duplicated())

Returns:
- True → Duplicate row
- False → Unique row

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8. Why Pandas is Important

Pandas is heavily used in:
- AI
- Machine Learning
- Financial Analysis
- Data Visualization
- Scientific Research

Almost every data scientist uses Pandas daily.

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Practice Exercises

1. Load and Inspect

Load data.csv and print the last 10 rows.

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2. Data Summary

Use .info() and explain what "Non-Null" means.

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3. Dictionary to DataFrame

Create a dictionary of 5 students and their grades, then convert it into a DataFrame.