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

---

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.

---

7. Introduction to Data Cleaning

Real-world datasets are often messy.

Pandas provides tools to clean them.

---

Removing Empty Rows

new_df = df.dropna()

This removes rows with missing values.

---

Replacing Missing Values

df.fillna(130, inplace=True)

This replaces empty cells with 130.

---

Checking Duplicates

print(df.duplicated())

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

---

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.

---

Practice Exercises

1. Load and Inspect

Load data.csv and print the last 10 rows.

---

2. Data Summary

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

---

3. Dictionary to DataFrame

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

Basic Python Course
Data Visualization with Matplotlib

By Yehya Oumer
(Certificate from Mizan Institute of Technology)

Issued: 2026

---

1. Introduction to Matplotlib

Matplotlib is one of the most popular Python libraries for data visualization.

It helps transform raw numbers into:
- Charts
- Graphs
- Visual insights

Most plotting features are available inside the pyplot module.

---

Importing Matplotlib

import matplotlib.pyplot as plt
import numpy as np

---

2. Basic Line Plot

A line plot connects data points using lines.

Example:

import matplotlib.pyplot as plt
import numpy as np

xpoints = np.array([0, 6])
ypoints = np.array([0, 250])

plt.plot(xpoints, ypoints)

plt.show()

---

What plt.show() Does

plt.show() displays the graph window.

Without it, the graph may not appear.

---

3. Markers and Line Styles

Markers highlight data points.

Common markers:

| Marker | Meaning |
|--------|----------|
| 'o' | Circle |
| '*' | Star |
| '.' | Point |

---

Example with Marker and Style

ypoints = np.array([3, 8, 1, 10])

plt.plot(
    ypoints,
    marker='*',
    linestyle='dotted',
    color='r'
)

plt.show()

---

Understanding the Parameters

- marker='*' → Star marker
- linestyle='dotted' → Dotted line
- color='r' → Red color

---

4. Labels and Titles

Graphs become meaningful when labels are added.

---

Example

x = np.array([80, 85, 90, 95])
y = np.array([240, 250, 260, 270])

plt.plot(x, y)

plt.title("Sports Watch Data")
plt.xlabel("Average Pulse")
plt.ylabel("Calorie Burnage")

plt.grid()

plt.show()

---

Why Grids Matter

Grids make charts easier to read and compare.

---

5. Plotting Multiple Lines

Matplotlib allows multiple datasets on one graph.

Example:

y1 = np.array([3, 8, 1, 10])
y2 = np.array([6, 2, 7, 11])

plt.plot(y1, label='Dataset A')
plt.plot(y2, label='Dataset B')

plt.legend()

plt.show()

---

What legend() Does

plt.legend() shows labels for each line.

Useful when comparing datasets.

---

6. Visualizing CSV Data

Matplotlib works perfectly with Pandas DataFrames.

Example:

import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_csv('data4.csv')

df.plot(
    kind='scatter',
    x='Pulse',
    y='Calories'
)

plt.title("Relationship between Pulse and Calories")

plt.show()

---

What is a Scatter Plot?

A scatter plot displays relationships between two variables.

Useful for:
- Trend analysis
- Data patterns
- Correlation discovery

---

7. Why Visualization Matters

Humans understand visuals faster than raw numbers.

Visualization helps:
- Discover patterns
- Detect mistakes
- Compare data
- Explain results clearly

---

Real-World Uses of Matplotlib

- Data Science
- AI & Machine Learning
- Business Analytics
- Financial Charts
- Scientific Research

---

Practice Exercises

1. The Star Plot

Create a line plot for:

[2, 10, 4, 8]

Use:
- Red star markers
- Dashed line

---

2. Grid Challenge

Recreate the "Sports Watch" graph and add a grid only on the Y-axis.

Hint:

plt.grid(axis='y')

---

3. Data Insight

Load data4.csv and create a line plot showing how "Maxpulse" changes over time.

Hello everyone! 👋

​You might have noticed a temporary pause in our regular posts recently. I wanted to let you all know that I have paused posting in celebration of Eid.

​Regular educational content and updates will resume right after the holiday! Enjoy the break, take some time to review the previous material, and get ready for more exciting programming and development content coming your way very soon.

​Thank you for your understanding, and see you all after Eid! ✨

Eid Mubarek ✨✨

Basic Python Course
Matplotlib Visualization Lab

By Yehya Oumer
(Certificate from Mizan Institute of Technology)

Issued: 2026

---

Introduction

Visualization helps transform raw numbers into meaningful insights.

In this lab, we will create charts using Matplotlib and NumPy to understand patterns, trends, and comparisons.

---

Exercise 1: Plotting a Linear Function

A linear function has the general form:

y = mx + b

Where:
- m = slope
- b = y-intercept

For this exercise:

y = 2x + 3

---

Code

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(-10, 10, 100)

y = 2 * x + 3

plt.plot(
    x,
    y,
    color='green',
    label='y = 2x + 3'
)

plt.xlabel("X Values")
plt.ylabel("Y Values")

plt.title("Simple Line Plot")

plt.grid(True)

plt.legend()

plt.show()

---

Explanation

np.linspace(-10, 10, 100)

Creates 100 evenly spaced numbers between -10 and 10.

---

y = 2 * x + 3

Calculates the corresponding Y values.

---

plt.plot()

Draws the graph.

---

plt.grid(True)

Displays grid lines for easier reading.

---

plt.legend()

Displays the graph label.

---

Expected Result

You will see:
- A straight green line
- Crossing the Y-axis at 3
- Increasing continuously because the slope is positive

---

Exercise 2: Comparing Multiple Data Series

Businesses often compare sales between different stores or years.

In this example, we compare product sales between Store A and Store B.

---

Code

import matplotlib.pyplot as plt

products = [
    'Laptop',
    'Mouse',
    'Keyboard',
    'Monitor'
]

sales_v1 = [5, 15, 10, 8]
sales_v2 = [7, 12, 11, 9]

plt.bar(
    products,
    sales_v1,
    color='blue',
    alpha=0.5,
    label='Store A'
)

plt.bar(
    products,
    sales_v2,
    color='red',
    alpha=0.5,
    label='Store B'
)

plt.title("Sales Comparison by Product")

plt.legend()

plt.show()

---

Explanation

Products:

products = [
    'Laptop',
    'Mouse',
    'Keyboard',
    'Monitor'
]

Categories displayed on the X-axis.

---

Sales Data

sales_v1 = [5, 15, 10, 8]
sales_v2 = [7, 12, 11, 9]

Represent sales numbers for two stores.

---

Transparency

alpha = 0.5

Makes bars semi-transparent so overlapping bars remain visible.

---

Legend

plt.legend()

Helps identify which color belongs to which store.

---

Expected Result

A bar chart showing:

- Blue bars → Store A
- Red bars → Store B

This makes comparison quick and easy.

---

Why These Visualizations Matter

Line Charts:
- Show trends
- Show growth or decline
- Useful for predictions

Bar Charts:
- Compare categories
- Compare performance
- Compare products, teams, or departments

---

Real-World Applications

- Business dashboards
- AI model performance analysis
- Student grade comparison
- Financial reports
- Scientific experiments

---

Practice Exercises

1. Modify the line graph and plot:

y = 3x - 5

---

2. Create a bar chart comparing the marks of five students.

---

3. Add:
- X-axis label
- Y-axis label
- Grid
- Legend

to all charts.

---

4. Create a chart comparing:
- Python learners
- Java learners

across four different schools.

Basic Python Course
Statistical Visualization with Seaborn

By Yehya Oumer
(Certificate from Mizan Institute of Technology)

Issued: 2026

---

1. Introduction to Seaborn

Seaborn is a powerful Python library built on top of Matplotlib.

It is specifically designed for:
- Statistical Visualization
- Data Exploration
- Pattern Discovery
- Correlation Analysis

Seaborn works extremely well with Pandas DataFrames and requires much less code than Matplotlib for advanced visualizations.

---

Why Use Seaborn?

Advantages:

- Beautiful default chart styles
- Easier syntax
- Built-in statistical plots
- Excellent integration with Pandas
- Commonly used in Data Science and AI

---

Importing Seaborn

import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd

Example dataset:

tips = sns.load_dataset("tips")

The `tips dataset is included with Seaborn and is commonly used for learning and practice.

---

2. Line Plots and Semantic Grouping

Line plots help visualize trends over time.

Seaborn can automatically separate groups using colors with the hue parameter.

---

Example

``python
sns.lineplot(
data=df,
x="Year",
y="Sales",
hue="Region"
)

plt.show()

---

Understanding `hue`

The `hue` parameter automatically assigns different colors to categories.

Example:

python
hue = "Region"

Different regions will appear as separate colored lines.

---

Applications

- Sales growth analysis
- Population trends
- Weather tracking
- Business dashboards

---

3. Scatter Plots and Correlation

Scatter plots are used to discover relationships between numerical variables.

---

Example

python
sns.scatterplot(
x="Age",
y="Weight",
data=my_data
)

plt.title("Age vs Weight Correlation")

plt.show()

---

Why Scatter Plots Matter

They help identify:

- Positive correlation
- Negative correlation
- No correlation
- Outliers

---

Example Interpretation

If points move upward from left to right:

text
Age ↑ → Weight ↑

This indicates a positive correlation.

---

4. Box Plots (Five Number Summary)

A box plot summarizes the distribution of data.

It highlights:

- Minimum value
- First Quartile (Q1)
- Median
- Third Quartile (Q3)
- Maximum value

It also helps detect outliers.

---

Example

python
sns.boxplot(
x="day",
y="total_bill",
data=tips
)

plt.show()

---

Why Box Plots Are Useful

They quickly show:

- Data spread
- Skewness
- Median values
- Unusual observations

---

5. Heatmaps and Correlation Matrices

Heatmaps visually represent relationships between variables.

They are extremely important in:

- Machine Learning
- AI Engineering
- Data Analysis

---

Example

python
correlation_matrix = df.corr()

sns.heatmap(
correlation_matrix,
annot=True,
cmap='coolwarm'
)

plt.show()

---

Understanding the Parameters

python
annot=True

Displays the correlation values inside each cell.

---

python
cmap='coolwarm'

Uses a color gradient:

- Blue → Weak or Negative Relationship
- Red → Strong Positive Relationship

---

Why Heatmaps Matter

They help answer questions like:

- Which variables affect sales?
- Which features influence predictions?
- Which columns are strongly related?

---

Real-World Applications of Seaborn

- Data Science
- Artificial Intelligence
- Business Intelligence
- Finance
- Healthcare Analytics
- Scientific Research

---

Comparison: Matplotlib vs Seaborn

| Feature | Matplotlib | Seaborn |
|----------|----------|----------|
| Ease of Use | Moderate | Easy |
| Statistical Charts | Limited | Excellent |
| Default Appearance | Basic | Modern |
| DataFrame Support | Good | Excellent |

---

Statistical Practice Exercises

1. Tips Analysis

Load the `tips` dataset and create a boxplot showing how `total_bill` changes between Lunch and Dinner.

---

2. Hue Challenge

Create a scatter plot showing:

text
total_bill vs tip
`

Color points based on whether the customer is a smoker.

---

3. Clean and Plot

Using your cleaned sales dataset:

Create a Seaborn line plot showing:

Price over Date