🐍 Python Pattern Challenge — Day 34

 

🐍 Python Pattern Challenge — Day 34 | Double Triangle Star Pattern

Welcome to Day 34 of the Python Pattern Challenge! 🚀

Today, we're exploring a simple yet interesting Double Triangle Star Pattern using Python. This pattern combines two triangular shapes side by side, creating a symmetrical design with stars and spaces.

Pattern printing is a great way to strengthen your logical thinking, understand loops, and learn how spacing affects the final output.

🎯 Today's Challenge

Write a Python program to print exactly the following pattern:

💻 Python Solution

n = 5

for i in range(1, n + 1):
    left = "* " * i
    spaces = "  " * (n - i)
    right = "* " * i

    print(left + spaces + right)

🧠 How It Works

Creating the rows

The for loop runs from 1 to 5, increasing the number of stars in each row.

Printing the left triangle

left = "* " * i

This repeats the star symbol according to the current row number.

Controlling the spaces

spaces = " " * (n - i)

This reduces the gap between the two triangles as the rows progress.

Printing the right triangle

right = "* " * i

This creates the second triangle, mirroring the first one.

Displaying the pattern

print(left + spaces + right)

This combines both triangles and the middle spacing into one row.

🚀 Challenge Yourself

• Increase the number of rows to 7.

• Replace * with # or another symbol.

• Create an inverted version of the pattern.

• Try printing the pattern using nested loops.

• Take the number of rows as input from the user.

Practice the code, experiment with the spacing, and see how small changes can create completely different patterns!

34 Days. 34 Patterns. Stronger Python Logic. 🐍🔥

Learn • Practice • Grow with CLCODING 💻

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Python Coding Challenge - Question with Answer (ID 251026)

 

Explanation:

1. Understanding False and True

In Python, bool is a subclass of int.

- False behaves numerically like 0.

- True behaves numerically like 1.

For example:

print(int(False))  # 0print(int(True))   # 1

Therefore, in arithmetic expressions, Python can use True and False as numeric values.

2. Understanding the Bitwise NOT Operator (~)

Consider this expression:

~False

Since False behaves like integer 0, this becomes:

~0

The bitwise NOT operator flips the bits of an integer. In Python, the result follows this identity:

\[

\sim x = -x - 1

\]

Therefore:

\[

\sim 0 = -0 - 1 = -1

\]

So:

~False

returns -1.

3. Understanding Multiplication

Now evaluate:

True * 3

Since True behaves like 1:

\[

1 \times 3 = 3

\]

The result is 3.

4. Understanding Operator Precedence

The original expression is:

~False + True * 3

Python evaluates multiplication before addition. The bitwise NOT applies to False before the addition as well.

The expression can be evaluated as:

(-1) + (1 * 3)

First, multiplication:

\[

1 \times 3 = 3

\]

Then addition:

\[

-1 + 3 = 2

\]

5. Understanding the print() Function

print(~False + True * 3)

The print() function displays the final calculated result.

Final output:

2

Books: Python for Chemistry from Fundamentals to Real-World Applications

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Python Coding challenge - Day 1289| What is the output of the following Python Code?

Code Explanation:

1. Importing lru_cache

from functools import lru_cache

- Imports lru_cache from Python’s built-in functools module.

- It allows a function to store previously computed results and reuse them when called with the same arguments.

2. Applying the Cache Decorator

@lru_cache(None)

- Applies caching to the function defined below it.

- None means there is no maximum limit on the number of cached results.

- If the function receives the same argument again, Python returns the cached result instead of executing the function body again.

3. Defining the Function

def f(x):

- Defines a function named f.

- The parameter x receives the value passed during the function call.

- In this example, x receives the value 3.

4. Printing Without a New Line

print(x, end="")

- Prints the value of x, which is 3.

- end="" prevents Python from adding a newline after printing.

- This statement executes only when the function body runs, not when a cached result is returned.

5. Returning the Calculated Value

return x * 2

- Multiplies x by 2.

- Since x = 3, the function returns 6.

- The caching decorator stores this result for the argument 3.

6. Calling the Function Twice

print(f(3) + f(3))

- The first f(3) executes the function, prints 3, and returns 6.

- The second f(3) uses the cached result 6, so the function body does not execute again.

- Python adds the returned values: 6 + 6 = 12.

- The outer print() displays 12 immediately after the previously printed 3.

Final Output

312

Books: 400 Days Python Coding Challenges with Explanation

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Can Python Websites Be Hacked? Critical Security Risks Every Website Owner Should Know in 2026

 

Introduction

Python powers thousands of websites, web applications, APIs, and online services. Frameworks such as Django, Flask, and FastAPI make it easier for developers to build scalable applications, but they do not automatically make those applications secure.

Recent warnings about critical vulnerabilities in PHP have raised an important question for website owners and developers:

Can Python-based websites also be affected by critical security vulnerabilities?

The answer is yes.

Although vulnerabilities targeting PHP do not automatically affect Python applications, Python websites can face their own security threats through vulnerable libraries, insecure configurations, outdated frameworks, exposed credentials, and weaknesses in server infrastructure.

For businesses that depend on Python-based websites, understanding these risks and applying security updates promptly is essential.

1. Can Python Websites Be Affected by Critical Security Flaws?

Yes. Python applications can contain security vulnerabilities that attackers may exploit to access sensitive data, disrupt services, or execute unauthorized operations.

The risks depend on the technologies used to build and operate the website.

For example:

• Django: Vulnerabilities may affect request processing, file handling, authentication, or other framework components.

• Flask: Insecure configurations, unsafe input handling, or vulnerable extensions can expose applications to attacks.

• FastAPI: Weak authentication, improper authorization, or vulnerable dependencies can put APIs at risk.

• Third-party packages: Outdated Python libraries may contain publicly documented vulnerabilities.

• Server infrastructure: Unpatched operating systems, databases, reverse proxies, and web servers can expose Python applications to additional threats.

A website does not need to be built with PHP to face a serious security incident. Every technology stack requires regular maintenance and security monitoring.

2. Common Security Risks for Python-Based Websites

Understanding common attack methods helps developers identify weaknesses before they become serious problems.

SQL Injection

SQL injection occurs when an application handles untrusted input in a way that allows an attacker to interfere with database queries.

Prevention: Use parameterized queries or trusted ORM features, validate input appropriately, and avoid constructing SQL queries through unsafe string concatenation.

Cross-Site Scripting (XSS)

Cross-site scripting can occur when an application displays untrusted content without appropriate output encoding or sanitization.

Prevention: Apply context-appropriate output encoding, use safe template features, and consider a Content Security Policy as an additional layer of protection.

Remote Code Execution

Remote code execution (RCE) can allow an attacker to execute code on a vulnerable server when a specific security flaw is successfully exploited.

Potential causes include vulnerable dependencies, unsafe deserialization, and dangerous handling of untrusted input.

Prevention: Patch affected components promptly, avoid unsafe execution of untrusted data, and restrict application permissions.

Insecure Debug Mode

Development settings can expose sensitive technical information.

For example, running a Django application with DEBUG=True in production can reveal detailed error pages and internal information.

Prevention: Disable debug mode in production, configure safe error handling, and keep secrets out of error messages and logs.

Broken Authentication and Access Control

Weak authentication and incorrect authorization checks can allow users to access resources or perform actions they should not be permitted to access.

Prevention: Enforce authorization on the server for every sensitive operation, use secure session management, and require multifactor authentication for privileged accounts where appropriate.

Vulnerable Third-Party Dependencies

Python projects frequently rely on external packages. A vulnerability in one dependency can expose an otherwise well-designed application.

Prevention: Maintain a dependency inventory, monitor security advisories, audit packages, and update affected dependencies to patched versions.

3. How to Check Your Python Project for Known Vulnerabilities

One useful tool is pip-audit, which checks Python package environments and dependency files for known vulnerabilities.

Install it using:

python -m pip install pip-audit

Audit the current Python environment:

python -m pip_audit

If your project maintains a requirements file, you can audit it directly:

python -m pip_audit -r requirements.txt

The tool compares package information with vulnerability data sources and reports known issues it identifies.

Important: A clean audit does not guarantee that your application is secure. It may not identify custom-code vulnerabilities, configuration errors, every newly disclosed issue, or vulnerabilities absent from its data sources.

Review the findings, confirm which components are affected, and follow the relevant security advisories before applying updates.

Official project: https://github.com/pypa/pip-audit

4. Keep Django, Flask, and FastAPI Updated

Developers should monitor the official security announcements for their frameworks.

Django

Django publishes security releases and advisories describing affected versions and recommended fixes.

Official website: https://www.djangoproject.com/

Security information: https://docs.djangoproject.com/en/stable/releases/security/

Flask

Flask applications depend on Flask itself, its extensions, and other packages. Check the official documentation and relevant security advisories before upgrading.

Official website: https://flask.palletsprojects.com/

FastAPI

FastAPI applications should keep FastAPI, Starlette, Pydantic, and other relevant dependencies updated according to their compatibility requirements and security advisories.

Official website: https://fastapi.tiangolo.com/

Avoid assuming that the newest version of one package is compatible with every other package in your application. Test updates in a staging environment before deploying them to production whenever practical.

5. Essential Security Checklist for Python Website Owners

Use the following checklist to strengthen your website's security.

• Identify all applications and services that use Python.

• Record the Python version, web framework, dependencies, and server software.

• Check official advisories for affected versions and available fixes.

• Upgrade vulnerable packages and frameworks to supported, patched releases.

• Run dependency audits regularly.

• Disable debug mode in production.

• Store passwords, API keys, and secret keys securely.

• Apply appropriate authentication and authorization controls.

• Restrict database, file-system, and operating-system permissions.

• Enable HTTPS and configure secure cookies and sessions.

• Back up important data and periodically test restoration.

• Monitor application logs, server activity, and unusual network connections.

• Establish a process for applying emergency security patches.

Security is not a one-time activity. It requires continuous maintenance as applications, dependencies, and threats evolve.

6. What Should You Do If a Python Website Is Vulnerable?

If you discover that your website uses an affected component, take the following steps.

Step 1: Confirm the affected version.

Compare the installed version with the official security advisory. Determine whether your configuration and usage make the application vulnerable.

Step 2: Prioritize exposed systems.

Internet-facing applications, authentication services, APIs, and systems containing sensitive information should receive particular attention.

Step 3: Apply the appropriate security update.

Install the vendor-recommended patched version and test the application for compatibility.

Step 4: Reduce exposure if immediate patching is impossible.

If a critical vulnerability is actively exploited and no effective mitigation is available, consider temporarily disabling the affected service or restricting access until the risk is addressed.

Step 5: Investigate suspicious activity.

Look for unexpected processes, unusual outbound connections, unauthorized file modifications, suspicious login attempts, and unexplained changes to application behavior.

Step 6: Respond to potential compromise.

If there are credible signs of an intrusion, isolate affected systems as appropriate, preserve relevant logs and evidence, investigate the incident, and involve qualified security professionals.

Updating a vulnerable package alone may not be sufficient if attackers have already gained access to the system.

7. Why Python Security Matters for Businesses

For businesses, a compromised website can lead to data exposure, service interruptions, financial losses, reputational damage, and recovery costs.

The consequences can affect more than the website itself. An insecure application may also expose databases, internal services, cloud resources, or credentials that provide access to other systems.

Small businesses and independent website owners should not assume they are unlikely to be targeted. Automated scanning tools can discover exposed services and vulnerable software without attackers knowing anything about a particular business beforehand.

A practical security strategy combines timely updates, secure coding practices, restricted permissions, reliable backups, and active monitoring.

Conclusion

Python is a powerful technology for building modern websites, APIs, and business applications. However, no programming language or web framework is completely immune to security vulnerabilities.

The PHP security warnings highlight a broader lesson for every website operator: keeping software updated and responding quickly to security advisories are essential parts of running a secure website.

If you maintain a Python website built with Django, Flask, FastAPI, or another framework, review your dependencies, verify your configuration, monitor official security announcements, and address known vulnerabilities promptly.

Taking these precautions can significantly reduce risk and help protect your users, business data, and online services.

Have you audited your Python website's dependencies recently? Run a security audit, review the results, and make vulnerability management a regular part of your development workflow.

---

Recommended resources

• Python security: https://www.python.org/dev/security/

• pip-audit: https://github.com/pypa/pip-audit

• Django security releases: https://docs.djangoproject.com/en/stable/releases/security/

• OWASP Web Application Security: https://owasp.org/www-project-top-ten/

• Python Packaging User Guide: https://packaging.python.org/

Disclaimer: This article provides general cybersecurity information, not a guarantee that any application is secure. Always verify affected versions and remediation steps using official vendor advisories.

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Python Coding Challenge - Question with Answer (ID 241026)

 

1. Understanding the Comparison Operators

Python uses comparison operators to compare values.

- > means greater than.

- == means equal to.

- < means less than.

This expression contains multiple comparisons connected together. Python calls this a chained comparison.

2. Understanding the First Comparison

4 > 3

Python checks whether 4 is greater than 3.

Since the condition is true, the result is:

True

3. Understanding the Second Comparison

3 == 3

Python checks whether both values are equal.

Both values are 3, so the result is:

True

4. Understanding the Third Comparison

3 < 5

Python checks whether 3 is less than 5.

This condition is also true.

True

5. How Python Evaluates the Chained Comparison

Python evaluates:

4 > 3 == 3 < 5

as if it were:

(4 > 3) and (3 == 3) and (3 < 5)

Each comparison must be true for the entire expression to return True.

Comparison Result

4 > 3 True

3 == 3 True

3 < 5 True

Since all three comparisons are true, the complete expression returns True.

6. Understanding the print() Function

print(4 > 3 == 3 < 5)

The print() function displays the final Boolean result on the screen.

Output:

True

Books: 🐍 50 Python Mistakes Everyone Makes ❌

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Python Coding challenge - Day 1288| What is the output of the following Python Code?

 

Code Explanation:

1. The try Block

try:

- The try block contains code that Python attempts to execute.

- If an exception occurs inside this block, Python looks for a matching except block to handle it.

- If no exception occurs, Python continues to the else block, if one is provided.

2. Floor Division

x = 10 // 2

- The // operator performs floor division, returning the quotient rounded down to the nearest integer.

- Here, 10 // 2 equals 5.

- The value 5 is assigned to the variable x.

- No exception occurs because the divisor is not zero.

3. The except Block

except ZeroDivisionError:

- This block handles a ZeroDivisionError, which occurs when a number is divided by zero.

- Python executes this block only if that specific exception occurs in the try block.

4. Printing the Error Message

print("Error")

- This statement prints Error.

- It executes only if a ZeroDivisionError is raised in the try block.

- In this example, division by zero does not occur, so this statement is skipped.

5. The else Block

else:

- The else block executes when the try block completes successfully without raising an exception.

- It does not execute if an exception is raised and handled by the except block.

6. Printing the Final Calculation

print(x + 3)

- The variable x contains 5.

- Python calculates 5 + 3, which equals 8.

- The result is printed because the try block completed successfully.

Final Output

8

Book: 500 Days Python Coding Challenges with Explanation

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Python Coding challenge - Day 1287| What is the output of the following Python Code?

 

Code Explanation:

1. nums = [2, 3, 4, 5, 6, 7] — Creating a List

nums = [2, 3, 4, 5, 6, 7]

 Creates a list named nums.

- The list contains six integers: 2, 3, 4, 5, 6, 7.

- These numbers will be checked one by one in the comprehension.

2. result = { ... } — Creating a Set Comprehension

result = {    x % 4    for x in nums    if x % 2 == 0}

- The curly braces {} create a set comprehension in this context.

- A set stores only unique values, so duplicate results are automatically removed.

- The comprehension filters the numbers and calculates a result for each number that passes the condition.

3. for x in nums — Iterating Through the List

for x in nums

- The for loop takes each number from nums.

- The variable x represents the current number.

- The values checked are 2, 3, 4, 5, 6, 7.

4. if x % 2 == 0 — Checking Even Numbers

if x % 2 == 0

- The modulo operator % returns the remainder after division.

- If a number divided by 2 has remainder 0, it is even.

- Only even numbers are allowed into the calculation.

x x % 2 Condition

2 0 True

3 1 False

4 0 True

5 1 False

6 0 True

7 1 False

Therefore, the numbers that pass the condition are 2, 4, 6.

5. x % 4 — Calculating the Remainder

x % 4

- Calculates the remainder when each selected even number is divided by 4.

- The calculation is performed only for numbers that satisfy the condition.

x Calculation Result

2 2 % 4 2

4 4 % 4 0

6 6 % 4 2

The generated values are 2, 0, 2.

Because this is a set, the duplicate value 2 is stored only once.

So, the resulting set contains {0, 2}.

6. print(sorted(result)) — Sorting and Printing

print(sorted(result))

- sorted() arranges the set's elements in ascending order.

- It returns a list containing the sorted values.

- print() displays that list on the screen.

Final output:

[0, 2]

Books: 100 Python Programs for Beginner with explanation

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Python, SQL, Tableau for Data Science Professional Certificate

 

Introduction

The Python, SQL, Tableau for Data Science Professional Certificate on Coursera is a 5-course, beginner-level program designed to build a practical foundation in data science. The program brings together some of the most useful tools for modern data professionals: Python, SQL, Tableau, Excel, GitHub, and AWS Machine Learning. 

The main focus is not just learning individual tools. Instead, the certificate follows a broader workflow:

Data → Cleaning → Analysis → Visualization → Machine Learning → Deployment → Real-World Project

This makes it useful for learners who want to develop a practical data science skillset from the ground up.

---

🐍 1. Python for Data Science

Python is one of the main foundations of the certificate.

The Python course focuses on using programming for practical data analysis rather than learning Python only as a general-purpose programming language.

You work with libraries such as:

• Pandas
• NumPy
• Matplotlib
• Seaborn
• Plotly

The course covers importing, cleaning, transforming, analyzing, and visualizing data. It also introduces statistical analysis and A/B testing.

Why Python Matters

Python allows analysts to automate repetitive work and handle datasets much more efficiently than manual spreadsheet-based workflows.

---

🗄️ 2. SQL for Data Science

SQL is another major component of the program.

Data professionals frequently work with information stored inside relational databases, so knowing how to retrieve and manipulate that information is an essential skill.

The SQL course progresses from basic queries toward more advanced techniques, including:

• Data filtering
• Data cleaning
• Joins
• Subqueries
• CTEs
• Window functions
• Database design
• Performance tuning
• Data transformation

It also introduces Git and GitHub for version control.

Practical Project

The course includes a TechMart retail dataset capstone where learners clean data, perform analysis, create advanced queries, optimize queries, and produce an analysis report.

---

📊 3. Tableau and Microsoft Excel

Data analysis is not complete until the results can be communicated clearly.

The third course focuses on Tableau and Microsoft Excel for data visualization and business intelligence.

Learners work with:

• Interactive dashboards
• Charts
• Calculations
• Business metrics
• Pivot tables
• Data storytelling
• Dashboard optimization
• Real-time updates

Tableau helps transform complex datasets into interactive visual experiences, while Excel remains an important tool for analysis and business reporting.

---

🎨 4. Data Storytelling

A good data analyst does more than create charts.

The analyst needs to communicate:

What happened?

Why does it matter?

What should the audience understand from the data?

The certificate therefore includes data storytelling and stakeholder presentation skills.

This is particularly valuable for analysts working with business teams because technical results need to be converted into understandable insights.

---

🤖 5. Machine Learning

The program also moves beyond traditional data analysis into Machine Learning.

The advanced section introduces learners to techniques for:

• Model development
• Model evaluation
• Feature engineering
• Time-series analysis
• Unsupervised learning
• Dimensionality reduction
• Predictive modeling

This gives learners an introduction to the machine learning side of data science without making the entire certificate exclusively about ML.

---

☁️ 6. AWS and Cloud Machine Learning

One interesting feature of this certificate is the inclusion of AWS.

The advanced course uses AWS SageMaker and related cloud services to introduce cloud-based machine learning workflows.

Learners explore:

• AWS SageMaker
• Amazon S3
• Model training
• Model evaluation
• Feature engineering
• Model deployment
• Cloud-based ML pipelines

This adds an important engineering and cloud component to the otherwise analytics-focused program.

---

🔄 7. End-to-End Data Science Workflow

The certificate is structured around the complete data science workflow.

A simplified version looks like:

Collect Data
↓
Clean Data
↓
Analyze Data
↓
Visualize Data
↓
Build ML Models
↓
Evaluate Models
↓
Deploy Solutions
↓
Communicate Results

This is one of the most useful aspects of the program because learners can see how different tools fit together.

---

🧩 8. AI-Assisted Data Workflows

The program also incorporates AI tools into several parts of the learning experience.

Learners explore AI-assisted approaches for:

• SQL optimization
• Data analysis
• Visualization
• Documentation
• Machine learning workflows
• Data processing

This makes the certificate more aligned with modern data workflows, where analysts increasingly use AI assistants alongside traditional programming and analytics tools.

---

🛠️ 9. GitHub and Version Control

Another useful component is GitHub.

Version control is important when working on real data projects because it allows you to track changes and maintain organized project workflows.

The SQL and Python courses incorporate GitHub practices alongside technical work.

For someone building a data science portfolio, this is especially useful because projects can be documented and maintained in a professional repository.

---

📁 10. Real-World Projects

The certificate emphasizes hands-on projects rather than only watching lectures.

Examples include:

SQL Project

Analyze retail data and create an actionable business report.

Python Project

Clean, analyze, visualize, and interpret real-world datasets.

Tableau Project

Build interactive dashboards and communicate business insights.

AWS ML Project

Develop and deploy machine learning solutions using cloud services.

Final Capstone

Combine SQL, cloud machine learning, visualization, and stakeholder communication into an end-to-end data science solution.

---

📚 11. Course Structure

The Professional Certificate contains five courses:

1. SQL for Data Science and Version Control with GitHub

Focuses on SQL, databases, data cleaning, advanced queries, Git, and GitHub.

2. Python for Data Science and Version Control with GitHub

Covers Python, Pandas, NumPy, visualization, statistics, A/B testing, and automation.

3. Data Visualization with Tableau and Microsoft Excel

Focuses on Tableau, Excel, dashboards, visualization, and data storytelling.

4. Advanced Data Science Techniques with AWS Integration

Introduces machine learning, feature engineering, forecasting, unsupervised learning, and AWS SageMaker.

5. Building a Real-World Data Science Solution

Brings the skills together through an end-to-end project involving analytics, cloud ML, dashboards, and automated pipelines.

---

🎯 Skills You Can Learn

The certificate covers a broad set of practical skills, including:

• Python
• SQL
• Pandas
• NumPy
• Matplotlib
• Seaborn
• Plotly
• Tableau
• Excel
• GitHub
• Data Analysis
• Data Visualization
• Exploratory Data Analysis
• A/B Testing
• Feature Engineering
• Machine Learning
• Unsupervised Learning
• Time-Series Analysis
• AWS SageMaker
• Data Storytelling
• Database Management

Coursera currently lists these among the program's skills and tools.

---

👨‍💻 Who Should Take This Certificate?

This certificate is particularly suitable for:

• Beginners in data science
• Aspiring data analysts
• Aspiring data scientists
• Excel users moving toward programming
• Business analysts
• Python beginners
• SQL learners
• BI professionals
• Students building a data portfolio

Coursera describes the program as a beginner-friendly starting point and notes that strong spreadsheet experience and an analytical mindset are useful, while advanced programming or statistics experience is not required.

---

⭐ Strengths

✅ Complete Toolset

Python, SQL, Tableau, Excel, GitHub, and AWS provide a broad practical toolkit.

✅ Beginner-Friendly

The program is designed to take learners from foundational concepts toward more advanced data science work.

✅ Hands-On Projects

Projects provide opportunities to apply the skills instead of learning everything only through theory.

✅ Strong Analytics Foundation

SQL + Python + Tableau creates a useful combination for data analyst and data science workflows.

✅ Machine Learning + Cloud

The AWS component gives learners exposure to ML deployment and cloud-based data science.

✅ Portfolio Development

The projects can provide useful material for a GitHub-based data science portfolio.

---

⚠️ Things to Know Before Starting

Although the program is beginner level, it covers a large number of technologies.

You will eventually encounter:

• Python
• SQL
• Tableau
• Excel
• GitHub
• Machine Learning
• AWS
• SageMaker
• Data Pipelines

So learners should expect to spend consistent time practicing rather than simply completing videos.

Also, because the certificate covers many areas, it may not provide the same depth as a specialized course dedicated entirely to advanced machine learning, deep learning, or statistics.

---

Join Now: Python, SQL, Tableau for Data Science Professional Certificate

🏆 Final Verdict

The Python, SQL, Tableau for Data Science Professional Certificate provides a broad and practical pathway into data science.

Its biggest advantage is the combination of programming, databases, visualization, business intelligence, machine learning, cloud computing, and real-world projects in one structured program.

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🌺 Python Turtle The Colorful Spirograph Bloom

 

Code:

import turtle import time s = turtle.Screen() s.setup(750, 750) s.bgcolor("#100719") s.title("Spirograph Flower") s.tracer(1) t = turtle.Turtle() t.hideturtle() t.speed(0) t.pensize(2) colors = ["#FF4D9D", "#A855F7", "#00E5FF", "#FFD166"] # Layered curved petals for layer in range(3): radius = 100 + layer * 35 for i in range(24): t.penup() t.goto(0, 0) t.setheading(i * 15 + layer * 5) t.pendown() t.pencolor(colors[(i + layer) % 4]) t.circle(radius, 70) t.left(110) t.circle(radius, 70) s.update() time.sleep(0.035) # Decorative center t.penup() t.goto(0, -18) t.pendown() t.pencolor("#FFD166") t.fillcolor("#FF4D9D") t.begin_fill() t.circle(18) t.end_fill() # Tiny outer dots for i in range(36): t.penup() t.goto(0, 0) t.setheading(i * 10) t.forward(245) t.dot(5, colors[i % 4]) s.update() time.sleep(0.025) turtle.done()

Explanation:

1. Import Libraries

import turtleimport time

- turtle draws the flower design.

- time adds pauses to the animation.

2. Create the Screen

s = turtle.Screen()s.setup(750, 750)s.bgcolor("#100719")s.title("Spirograph Flower")s.tracer(1)

- Creates a 750 × 750 window.

- Sets a dark purple background.

- Adds the title “Spirograph Flower”.

- Enables screen updates during drawing.

3. Configure the Turtle

t = turtle.Turtle()t.hideturtle()t.speed(0)t.pensize(2)

- Creates the drawing turtle.

- Hides the cursor.

- Sets the fastest drawing speed.

- Sets the line thickness to 2.

4. Define Neon Colors

colors = ["#FF4D9D", "#A855F7", "#00E5FF", "#FFD166"]

Defines four colors: pink, purple, cyan, and golden yellow.

5. Create Three Flower Layers

for layer in range(3):    radius = 100 + layer * 35

- Creates three layers of petals.

- Increases the radius for each layer: 100, 135, and 170.

6. Set Petal Positions

for i in range(24):    t.penup()    t.goto(0, 0)    t.setheading(i * 15 + layer * 5)    t.pendown()

- Draws 24 curved patterns per layer.

- Moves the turtle to the center.

- Rotates each pattern by 15° plus a small layer offset.

- Puts the pen down to start drawing.

7. Draw Curved Petals

t.pencolor(colors[(i + layer) % 4])t.circle(radius, 70)t.left(110)t.circle(radius, 70)

- Changes colors for a multicolored effect.

- Draws a 70° curved arc.

- Turns left by 110°.

- Draws another arc to form a petal-like shape.

8. Animate the Flower

s.update()time.sleep(0.035)

- Refreshes the screen.

- Adds a short pause so the drawing animation is visible.

9. Draw the Center

t.penup()t.goto(0, -18)t.pendown()t.pencolor("#FFD166")t.fillcolor("#FF4D9D")t.begin_fill()t.circle(18)t.end_fill()

- Moves to the center area.

- Sets a golden outline and pink fill.

- Draws a filled circle of radius 18.

10. Add Decorative Outer Dots

for i in range(36):    t.penup()    t.goto(0, 0)    t.setheading(i * 10)    t.forward(245)    t.dot(5, colors[i % 4])

- Creates 36 decorative dots.

- Rotates by 10° for each dot.

- Moves 245 units from the center.

- Uses different neon colors for the dots.

11. Finish the Program

turtle.done()

Keeps the graphics window open after the drawing finishes.

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