#what is the use of numpy in python

[educationmore]

Python for Beginners: Launch Your Tech Career with Coding Skills

Are you ready to launch your tech career but don’t know where to start? Learning Python is one of the best ways to break into the world of technology—even if you have zero coding experience.

In this guide, we’ll explore how Python for beginners can be your gateway to a rewarding career in software development, data science, automation, and more.

Why Python Is the Perfect Language for Beginners

Python has become the go-to programming language for beginners and professionals alike—and for good reason:

Simple syntax: Python reads like plain English, making it easy to learn.

High demand: Industries spanning the spectrum are actively seeking Python developers to fuel their technological advancements.

Versatile applications: Python's versatility shines as it powers everything from crafting websites to driving artificial intelligence and dissecting data.

Whether you want to become a software developer, data analyst, or AI engineer, Python lays the foundation.

What Can You Do With Python?

Python is not just a beginner language—it’s a career-building tool. Here are just a few career paths where Python is essential:

Web Development: Frameworks like Django and Flask make it easy to build powerful web applications. You can even enroll in a Python Course in Kochi to gain hands-on experience with real-world web projects.

Data Science & Analytics: For professionals tackling data analysis and visualization, the Python ecosystem, featuring powerhouses like Pandas, NumPy, and Matplotlib, sets the benchmark.

Machine Learning & AI: Spearheading advancements in artificial intelligence development, Python boasts powerful tools such as TensorFlow and scikit-learn.

Automation & Scripting: Simple yet effective Python scripts offer a pathway to amplified efficiency by automating routine workflows.

Cybersecurity & Networking: The application of Python is expanding into crucial domains such as ethical hacking, penetration testing, and the automation of network processes.

How to Get Started with Python

Starting your Python journey doesn't require a computer science degree. Success hinges on a focused commitment combined with a thoughtfully structured educational approach.

Step 1: Install Python

Download and install Python from python.org. It's free and available for all platforms.

Step 2: Choose an IDE

Use beginner-friendly tools like Thonny, PyCharm, or VS Code to write your code.

Step 3: Learn the Basics

Focus on:

Variables and data types

Conditional statements

Loops

Functions

Lists and dictionaries

If you prefer guided learning, a reputable Python Institute in Kochi can offer structured programs and mentorship to help you grasp core concepts efficiently.

Step 4: Build Projects

Learning by doing is key. Start small:

Build a calculator

Automate file organization

Create a to-do list app

As your skills grow, you can tackle more complex projects like data dashboards or web apps.

How Python Skills Can Boost Your Career

Adding Python to your resume instantly opens up new opportunities. Here's how it helps:

Higher employability: Python is one of the top 3 most in-demand programming languages.

Better salaries: Python developers earn competitive salaries across the globe.

Remote job opportunities: Many Python-related jobs are available remotely, offering flexibility.

Even if you're not aiming to be a full-time developer, Python skills can enhance careers in marketing, finance, research, and product management.

If you're serious about starting a career in tech, learning Python is the smartest first step you can take. It’s beginner-friendly, powerful, and widely used across industries.

Whether you're a student, job switcher, or just curious about programming, Python for beginners can unlock countless career opportunities. Invest time in learning today—and start building the future you want in tech.

Globally recognized as a premier educational hub, DataMites Institute delivers in-depth training programs across the pivotal fields of data science, artificial intelligence, and machine learning. They provide expert-led courses designed for both beginners and professionals aiming to boost their careers.

Python Modules Explained - Different Types and Functions - Python Tutorial

[studyblrspace]

hi! I love your blog. What programming languages would you recommend learning if you wanted to get into astrophysics? I already know c++, but I heard somewhere that python is better for data analysis?

I'm so sorry anon, I drafted an answer and then completely forgot to post it 😭😭😭

the main programming languages I've seen are c++ and python. there's also julia (new), and fortran (🥴).

so its great you already know c++! personally I've used athena++ code for simulations if you wanted a simulation code to play with.

but python would be good to play around with if you're not familiar, its great for visualization / data analysis! I started by learning the syntax and about packages like numpy, matplotlib, and astropy. "Python for Astronomers" may be helpful if you need a resource for learning, it has a free textbook and some tutorials. part of my undergrad computational astrophysics course could was based on it! another fun package is yt, you can look up "python yt cookbook" or click here. this website also gives you sample data from a simulation run and lots of tutorials.

julia is not as widely used but its supposed to have the intuitiveness of python with the performance/speed of c++. it's a newer language, like 10 years old. I've heard that there is a (very slow) shift to this language in the astrophysics community instead of python. I don't have any resources because I haven't gotten around to learning it yet 😅

fortran is an older language, I can't say I'm familiar with it. I've only encountered it in a skeleton simulation code a postdoc was developing (and I was testing the code) so I just know basic syntax. you'd probably be fine not learning it, unless you want to develop your own simulation code soon

I'm only a couple years into (theoretical) astrophysics research so if anyone else has input, please let me know!!

[emexotechnologies]

Best Python Training in Marathahalli, Bangalore – Become a Python Expert & Launch a Future-Ready Career!

Python is one of the most powerful and versatile programming languages today, widely used in web development, data science, automation, artificial intelligence, and more. Whether you're aiming to break into the tech industry or looking to enhance your coding skills, eMexo Technologies offers the Best Python Training in Marathahalli, Bangalore—designed to help you succeed in today’s competitive job market.

Our Python Certification Course in Marathahalli, Bangalore is ideal for both beginners and experienced professionals who want to master Python from the ground up and land high-paying jobs in top companies.

🌟Who Should Join This Python Course in Marathahalli, Bangalore?

Our comprehensive Python Course in Marathahalli, Bangalore is ideal for:

Freshers and college students looking to build a solid foundation in programming

Software Developers and IT professionals aiming to upskill in Python

Data Analysts, Testers, and Automation Engineers entering the Python ecosystem

Professionals preparing for technical certifications or job transitions

Anyone passionate about coding, web development, or data science

📘 What You Will Learn in the Python Certification Course Marathahalli, Bangalore?

Our Python Certification Course in Marathahalli, Bangalore provides hands-on learning with a project-driven curriculum. You will learn:

Core Python Programming: Data types, operators, control flow, functions, OOP concepts

Advanced Python Topics: Modules, File Handling, Exception Handling, and Regular Expressions

Web Development with Django/Flask: Build dynamic websites and web apps

Data Analysis & Visualization: Learn NumPy, Pandas, and Matplotlib

Database Connectivity: Work with MySQL, PostgreSQL, and other databases

Live Projects: Create real-world applications, automation tools, and data dashboards

🚀 Why Choose eMexo Technologies – The Best Python Training Institute in Marathahalli, Bangalore?

eMexo Technologies is your career partner, not just a Python Training Center in Marathahalli, Bangalore. Here’s what makes us the Best Python Training Institute in Marathahalli, Bangalore:

✅ Industry-Certified Trainers with practical project experience ✅ Real-time training in fully equipped smart labs ✅ Individual mentorship and personalized learning plans ✅ Resume building, career guidance, and mock interview sessions ✅ Python Training Placement in Marathahalli, Bangalore with 100% placement assistance ✅ Flexible timings and learning modes: online & classroom training

We focus on skills that get you hired and help you build a strong professional portfolio.

🎯 Secure Your Future with the Leading Python Training in Marathahalli, Bangalore

Python is powering the future of tech—from automation to AI and data science. By joining our Python Certification Course in Marathahalli, Bangalore, you’ll gain in-demand skills, real-world exposure, and the confidence to take on job roles across various domains.

At eMexo Technologies, our goal is your career success. Learn from the best and become a job-ready Python professional.

📞 Call or WhatsApp: +91-9513216462 📧 Email: info@emexotechnologies.com 🌐 Website: https://www.emexotechnologies.com/courses/python-training-in-marathahalli-bangalore/

🚀 Seats are limited – Enroll today at the most trusted Python Training Institute in Marathahalli, Bangalore and take the first step toward a rewarding IT career.

🔖 Popular Hashtags:

[emexotechnologie]

Best Python Training in Marathahalli, Bangalore – Become a Python Expert & Launch a Future-Ready Career!

Python is one of the most powerful and versatile programming languages today, widely used in web development, data science, automation, artificial intelligence, and more. Whether you're aiming to break into the tech industry or looking to enhance your coding skills, eMexo Technologies offers the Best Python Training in Marathahalli, Bangalore—designed to help you succeed in today’s competitive job market.

Our Python Certification Course in Marathahalli, Bangalore is ideal for both beginners and experienced professionals who want to master Python from the ground up and land high-paying jobs in top companies.

🌟Who Should Join This Python Course in Marathahalli, Bangalore?

Our comprehensive Python Course in Marathahalli, Bangalore is ideal for:

Freshers and college students looking to build a solid foundation in programming

Software Developers and IT professionals aiming to upskill in Python

Data Analysts, Testers, and Automation Engineers entering the Python ecosystem

Professionals preparing for technical certifications or job transitions

Anyone passionate about coding, web development, or data science

📘 What You Will Learn in the Python Certification Course Marathahalli, Bangalore?

Our Python Certification Course in Marathahalli, Bangalore provides hands-on learning with a project-driven curriculum. You will learn:

Core Python Programming: Data types, operators, control flow, functions, OOP concepts

Advanced Python Topics: Modules, File Handling, Exception Handling, and Regular Expressions

Web Development with Django/Flask: Build dynamic websites and web apps

Data Analysis & Visualization: Learn NumPy, Pandas, and Matplotlib

Database Connectivity: Work with MySQL, PostgreSQL, and other databases

Live Projects: Create real-world applications, automation tools, and data dashboards

🚀 Why Choose eMexo Technologies – The Best Python Training Institute in Marathahalli, Bangalore?

eMexo Technologies is your career partner, not just a Python Training Center in Marathahalli, Bangalore. Here’s what makes us the Best Python Training Institute in Marathahalli, Bangalore:

✅ Industry-Certified Trainers with practical project experience ✅ Real-time training in fully equipped smart labs ✅ Individual mentorship and personalized learning plans ✅ Resume building, career guidance, and mock interview sessions ✅ Python Training Placement in Marathahalli, Bangalore with 100% placement assistance ✅ Flexible timings and learning modes: online & classroom training

We focus on skills that get you hired and help you build a strong professional portfolio.

🎯 Secure Your Future with the Leading Python Training in Marathahalli, Bangalore

Python is powering the future of tech—from automation to AI and data science. By joining our Python Certification Course in Marathahalli, Bangalore, you’ll gain in-demand skills, real-world exposure, and the confidence to take on job roles across various domains.

At eMexo Technologies, our goal is your career success. Learn from the best and become a job-ready Python professional.

📞 Call or WhatsApp: +91-9513216462 📧 Email: info@emexotechnologies.com 🌐 Website: https://www.emexotechnologies.com/courses/python-training-in-marathahalli-bangalore/

🚀 Seats are limited – Enroll today at the most trusted Python Training Institute in Marathahalli, Bangalore and take the first step toward a rewarding IT career.

🔖 Popular Hashtags:

[assignmentoc]

What is Python?

A Language for Everyone – From Beginners to Pros!

Python is a high-level, versatile programming language renowned for its clean syntax and powerful capabilities. It's designed to be beginner-friendly, making it an excellent choice for new programmers, yet it remains robust enough for building complex systems. Its versatility and ease of use have propelled it to the forefront of the programming world.

Python Programming Language

Why Learn Python?

Python offers a myriad of benefits for learners and professionals alike, making it a worthwhile endeavor for anyone interested in programming. Here are some compelling reasons to learn Python:

Simple & Easy to Learn: Python's clean and readable syntax allows beginners to pick up programming concepts without the steep learning curve associated with many other languages.

Powerful for Data Science & Machine Learning: Python's extensive libraries, such as Pandas, NumPy, and TensorFlow, make it a powerhouse in the realms of data science and machine learning, enabling complex data analysis and predictive modeling.

Web Development with Django & Flask: Python provides powerful frameworks like Django and Flask, which streamline the process of building dynamic web applications with ease and speed.

Game Development & Automation: With libraries like Pygame, Python makes creating simple games accessible, while its scripting capabilities excel in automating repetitive tasks.

Scientific Computing: Python is a staple in scientific research, offering tools like SciPy and Matplotlib for scientific computing and data visualization.

AI & Deep Learning: Python's integration with AI and deep learning libraries such as Keras and PyTorch makes it a preferred language for developing cutting-edge AI systems.

App & Software Prototyping: Its simplicity allows developers to quickly prototype applications, making it an excellent choice for testing ideas and building MVPs.

Why Learn Python

Where is Python Used?

Python's versatility and power have led to its wide adoption across numerous industries and applications. Here are some areas where Python shines:

Companies: Major players such as Google, Netflix, NASA, Facebook, and IBM rely on Python for various aspects of their technology stack, demonstrating its reliability and scalability.

Fields: Python is indispensable in fields such as AI, web development, data science, cybersecurity, and automation, showcasing its adaptability to diverse challenges.

Education: Python holds the title of the #1 language taught in universities globally, reflecting its importance in modern computer science education and its role in nurturing the next generation of programmers.

Implementation Of Python

Conclusion

Python's combination of simplicity, power, and versatility makes it an essential language for anyone looking to delve into programming, whether starting from scratch or expanding their existing skill set.

HOME

[xaltius]

Unlocking the Power of Data: Essential Skills to Become a Data Scientist

In today's data-driven world, the demand for skilled data scientists is skyrocketing. These professionals are the key to transforming raw information into actionable insights, driving innovation and shaping business strategies. But what exactly does it take to become a data scientist? It's a multidisciplinary field, requiring a unique blend of technical prowess and analytical thinking. Let's break down the essential skills you'll need to embark on this exciting career path.

1. Strong Mathematical and Statistical Foundation:

At the heart of data science lies a deep understanding of mathematics and statistics. You'll need to grasp concepts like:

Linear Algebra and Calculus: Essential for understanding machine learning algorithms and optimizing models.

Probability and Statistics: Crucial for data analysis, hypothesis testing, and drawing meaningful conclusions from data.

2. Programming Proficiency (Python and/or R):

Data scientists are fluent in at least one, if not both, of the dominant programming languages in the field:

Python: Known for its readability and extensive libraries like Pandas, NumPy, Scikit-learn, and TensorFlow, making it ideal for data manipulation, analysis, and machine learning.

R: Specifically designed for statistical computing and graphics, R offers a rich ecosystem of packages for statistical modeling and visualization.

3. Data Wrangling and Preprocessing Skills:

Raw data is rarely clean and ready for analysis. A significant portion of a data scientist's time is spent on:

Data Cleaning: Handling missing values, outliers, and inconsistencies.

Data Transformation: Reshaping, merging, and aggregating data.

Feature Engineering: Creating new features from existing data to improve model performance.

4. Expertise in Databases and SQL:

Data often resides in databases. Proficiency in SQL (Structured Query Language) is essential for:

Extracting Data: Querying and retrieving data from various database systems.

Data Manipulation: Filtering, joining, and aggregating data within databases.

5. Machine Learning Mastery:

Machine learning is a core component of data science, enabling you to build models that learn from data and make predictions or classifications. Key areas include:

Supervised Learning: Regression, classification algorithms.

Unsupervised Learning: Clustering, dimensionality reduction.

Model Selection and Evaluation: Choosing the right algorithms and assessing their performance.

6. Data Visualization and Communication Skills:

Being able to effectively communicate your findings is just as important as the analysis itself. You'll need to:

Visualize Data: Create compelling charts and graphs to explore patterns and insights using libraries like Matplotlib, Seaborn (Python), or ggplot2 (R).

Tell Data Stories: Present your findings in a clear and concise manner that resonates with both technical and non-technical audiences.

7. Critical Thinking and Problem-Solving Abilities:

Data scientists are essentially problem solvers. You need to be able to:

Define Business Problems: Translate business challenges into data science questions.

Develop Analytical Frameworks: Structure your approach to solve complex problems.

Interpret Results: Draw meaningful conclusions and translate them into actionable recommendations.

8. Domain Knowledge (Optional but Highly Beneficial):

Having expertise in the specific industry or domain you're working in can give you a significant advantage. It helps you understand the context of the data and formulate more relevant questions.

9. Curiosity and a Growth Mindset:

The field of data science is constantly evolving. A genuine curiosity and a willingness to learn new technologies and techniques are crucial for long-term success.

10. Strong Communication and Collaboration Skills:

Data scientists often work in teams and need to collaborate effectively with engineers, business stakeholders, and other experts.

Kickstart Your Data Science Journey with Xaltius Academy's Data Science and AI Program:

Acquiring these skills can seem like a daunting task, but structured learning programs can provide a clear and effective path. Xaltius Academy's Data Science and AI Program is designed to equip you with the essential knowledge and practical experience to become a successful data scientist.

Key benefits of the program:

Comprehensive Curriculum: Covers all the core skills mentioned above, from foundational mathematics to advanced machine learning techniques.

Hands-on Projects: Provides practical experience working with real-world datasets and building a strong portfolio.

Expert Instructors: Learn from industry professionals with years of experience in data science and AI.

Career Support: Offers guidance and resources to help you launch your data science career.

Becoming a data scientist is a rewarding journey that blends technical expertise with analytical thinking. By focusing on developing these key skills and leveraging resources like Xaltius Academy's program, you can position yourself for a successful and impactful career in this in-demand field. The power of data is waiting to be unlocked – are you ready to take the challenge?

[datasciencewithmohsin]

Understanding Outliers in Machine Learning and Data Science

In machine learning and data science, an outlier is like a misfit in a dataset. It's a data point that stands out significantly from the rest of the data. Sometimes, these outliers are errors, while other times, they reveal something truly interesting about the data. Either way, handling outliers is a crucial step in the data preprocessing stage. If left unchecked, they can skew your analysis and even mess up your machine learning models.

In this article, we will dive into:

1. What outliers are and why they matter.

2. How to detect and remove outliers using the Interquartile Range (IQR) method.

3. Using the Z-score method for outlier detection and removal.

4. How the Percentile Method and Winsorization techniques can help handle outliers.

This guide will explain each method in simple terms with Python code examples so that even beginners can follow along.

1. What Are Outliers?

An outlier is a data point that lies far outside the range of most other values in your dataset. For example, in a list of incomes, most people might earn between $30,000 and $70,000, but someone earning $5,000,000 would be an outlier.

Why Are Outliers Important?

Outliers can be problematic or insightful:

Problematic Outliers: Errors in data entry, sensor faults, or sampling issues.

Insightful Outliers: They might indicate fraud, unusual trends, or new patterns.

Types of Outliers

1. Univariate Outliers: These are extreme values in a single variable.

Example: A temperature of 300°F in a dataset about room temperatures.

2. Multivariate Outliers: These involve unusual combinations of values in multiple variables.

Example: A person with an unusually high income but a very low age.

3. Contextual Outliers: These depend on the context.

Example: A high temperature in winter might be an outlier, but not in summer.

2. Outlier Detection and Removal Using the IQR Method

The Interquartile Range (IQR) method is one of the simplest ways to detect outliers. It works by identifying the middle 50% of your data and marking anything that falls far outside this range as an outlier.

Steps:

1. Calculate the 25th percentile (Q1) and 75th percentile (Q3) of your data.

2. Compute the IQR:

{IQR} = Q3 - Q1

Q1 - 1.5 \times \text{IQR}

Q3 + 1.5 \times \text{IQR} ] 4. Anything below the lower bound or above the upper bound is an outlier.

Python Example:

import pandas as pd

# Sample dataset

data = {'Values': [12, 14, 18, 22, 25, 28, 32, 95, 100]}

df = pd.DataFrame(data)

# Calculate Q1, Q3, and IQR

Q1 = df['Values'].quantile(0.25)

Q3 = df['Values'].quantile(0.75)

IQR = Q3 - Q1

# Define the bounds

lower_bound = Q1 - 1.5 * IQR

upper_bound = Q3 + 1.5 * IQR

# Identify and remove outliers

outliers = df[(df['Values'] < lower_bound) | (df['Values'] > upper_bound)]

print("Outliers:\n", outliers)

filtered_data = df[(df['Values'] >= lower_bound) & (df['Values'] <= upper_bound)]

print("Filtered Data:\n", filtered_data)

Key Points:

The IQR method is great for univariate datasets.

It works well when the data isn’t skewed or heavily distributed.

3. Outlier Detection and Removal Using the Z-Score Method

The Z-score method measures how far a data point is from the mean, in terms of standard deviations. If a Z-score is greater than a certain threshold (commonly 3 or -3), it is considered an outlier.

Formula:

Z = \frac{(X - \mu)}{\sigma}

 is the data point,

 is the mean of the dataset,

 is the standard deviation.

Python Example:

import numpy as np

# Sample dataset

data = {'Values': [12, 14, 18, 22, 25, 28, 32, 95, 100]}

df = pd.DataFrame(data)

# Calculate mean and standard deviation

mean = df['Values'].mean()

std_dev = df['Values'].std()

# Compute Z-scores

df['Z-Score'] = (df['Values'] - mean) / std_dev

# Identify and remove outliers

threshold = 3

outliers = df[(df['Z-Score'] > threshold) | (df['Z-Score'] < -threshold)]

print("Outliers:\n", outliers)

filtered_data = df[(df['Z-Score'] <= threshold) & (df['Z-Score'] >= -threshold)]

print("Filtered Data:\n", filtered_data)

Key Points:

The Z-score method assumes the data follows a normal distribution.

It may not work well with skewed datasets.

4. Outlier Detection Using the Percentile Method and Winsorization

Percentile Method:

In the percentile method, we define a lower percentile (e.g., 1st percentile) and an upper percentile (e.g., 99th percentile). Any value outside this range is treated as an outlier.

Winsorization:

Winsorization is a technique where outliers are not removed but replaced with the nearest acceptable value.

Python Example:

from scipy.stats.mstats import winsorize

import numpy as np

Sample data

data = [12, 14, 18, 22, 25, 28, 32, 95, 100]

Calculate percentiles

lower_percentile = np.percentile(data, 1)

upper_percentile = np.percentile(data, 99)

Identify outliers

outliers = [x for x in data if x < lower_percentile or x > upper_percentile]

print("Outliers:", outliers)

# Apply Winsorization

winsorized_data = winsorize(data, limits=[0.01, 0.01])

print("Winsorized Data:", list(winsorized_data))

Key Points:

Percentile and Winsorization methods are useful for skewed data.

Winsorization is preferred when data integrity must be preserved.

Final Thoughts

Outliers can be tricky, but understanding how to detect and handle them is a key skill in machine learning and data science. Whether you use the IQR method, Z-score, or Wins

orization, always tailor your approach to the specific dataset you’re working with.

By mastering these techniques, you’ll be able to clean your data effectively and improve the accuracy of your models.

[digitaldetoxworld]

Python Programming Language: A Comprehensive Guide

 Python is one of the maximum widely used and hastily growing programming languages within the world. Known for its simplicity, versatility, and great ecosystem, Python has become the cross-to desire for beginners, professionals, and organizations across industries.

What is Python used for

🐍 What is Python?

Python is a excessive-stage, interpreted, fashionable-purpose programming language.  The language emphasizes clarity, concise syntax, and code simplicity, making it an excellent device for the whole lot from web development to synthetic intelligence.

Its syntax is designed to be readable and easy, regularly described as being near the English language. This ease of information has led Python to be adopted no longer simplest through programmers but also by way of scientists, mathematicians, and analysts who may not have a formal heritage in software engineering.

📜 Brief History of Python

Late Nineteen Eighties: Guido van Rossum starts work on Python as a hobby task.

1991: Python zero.9.0 is released, presenting classes, functions, and exception managing.

2000: Python 2.Zero is launched, introducing capabilities like list comprehensions and rubbish collection.

2008: Python 3.Zero is launched with considerable upgrades but breaks backward compatibility.

2024: Python three.12 is the modern day strong model, enhancing performance and typing support.

⭐ Key Features of Python

Easy to Learn and Use:

Python's syntax is simple and similar to English, making it a high-quality first programming language.

Interpreted Language:

Python isn't always compiled into device code; it's far done line by using line the usage of an interpreter, which makes debugging less complicated.

Cross-Platform:

Python code runs on Windows, macOS, Linux, and even cell devices and embedded structures.

Dynamic Typing:

Variables don’t require explicit type declarations; types are decided at runtime.

Object-Oriented and Functional:

Python helps each item-orientated programming (OOP) and practical programming paradigms.

Extensive Standard Library:

Python includes a rich set of built-in modules for string operations, report I/O, databases, networking, and more.

Huge Ecosystem of Libraries:

From data technological know-how to net development, Python's atmosphere consists of thousands of programs like NumPy, pandas, TensorFlow, Flask, Django, and many greater.

📌 Basic Python Syntax

Here's an instance of a easy Python program:

python

Copy

Edit

def greet(call):

    print(f"Hello, call!")

greet("Alice")

Output:

Copy

Edit

Hello, Alice!

Key Syntax Elements:

Indentation is used to define blocks (no curly braces  like in different languages).

Variables are declared via task: x = 5

Comments use #:

# This is a remark

Print Function:

print("Hello")

📊 Python Data Types

Python has several built-in data kinds:

Numeric: int, go with the flow, complicated

Text: str

Boolean: bool (True, False)

Sequence: listing, tuple, range

Mapping: dict

Set Types: set, frozenset

Example:

python

Copy

Edit

age = 25             # int

name = "John"        # str

top = 5.Nine         # drift

is_student = True    # bool

colors = ["red", "green", "blue"]  # listing

🔁 Control Structures

Conditional Statements:

python

Copy

Edit

if age > 18:

    print("Adult")

elif age == 18:

    print("Just became an person")

else:

    print("Minor")

Loops:

python

Copy

Edit

for color in hues:

    print(coloration)

while age < 30:

    age += 1

🔧 Functions and Modules

Defining a Function:

python

Copy

Edit

def upload(a, b):

    return a + b

Importing a Module:

python

Copy

Edit

import math

print(math.Sqrt(sixteen))  # Output: four.0

🗂️ Object-Oriented Programming (OOP)

Python supports OOP functions such as lessons, inheritance, and encapsulation.

Python

Copy

Edit

elegance Animal:

    def __init__(self, call):

        self.Call = name

def communicate(self):

        print(f"self.Call makes a valid")

dog = Animal("Dog")

dog.Speak()  # Output: Dog makes a legitimate

🧠 Applications of Python

Python is used in nearly each area of era:

1. Web Development

Frameworks like Django, Flask, and FastAPI make Python fantastic for building scalable web programs.

2. Data Science & Analytics

Libraries like pandas, NumPy, and Matplotlib permit for data manipulation, evaluation, and visualization.

Three. Machine Learning & AI

Python is the dominant language for AI, way to TensorFlow, PyTorch, scikit-research, and Keras.

4. Automation & Scripting

Python is extensively used for automating tasks like file managing, device tracking, and data scraping.

Five. Game Development

Frameworks like Pygame allow builders to build simple 2D games.

6. Desktop Applications

With libraries like Tkinter and PyQt, Python may be used to create cross-platform computing device apps.

7. Cybersecurity

Python is often used to write security equipment, penetration trying out scripts, and make the most development.

📚 Popular Python Libraries

NumPy: Numerical computing

pandas: Data analysis

Matplotlib / Seaborn: Visualization

scikit-study: Machine mastering

BeautifulSoup / Scrapy: Web scraping

Flask / Django: Web frameworks

OpenCV: Image processing

PyTorch / TensorFlow: Deep mastering

SQLAlchemy: Database ORM

💻 Python Tools and IDEs

Popular environments and tools for writing Python code encompass:

PyCharm: Full-featured Python IDE.

VS Code: Lightweight and extensible editor.

Jupyter Notebook: Interactive environment for statistics technological know-how and studies.

IDLE: Python’s default editor.

🔐 Strengths of Python

Easy to study and write

Large community and wealthy documentation

Extensive 0.33-birthday celebration libraries

Strong support for clinical computing and AI

Cross-platform compatibility

⚠️ Limitations of Python

Slower than compiled languages like C/C++

Not perfect for mobile app improvement

High memory usage in massive-scale packages

GIL (Global Interpreter Lock) restricts genuine multithreading in CPython

🧭 Learning Path for Python Beginners

Learn variables, facts types, and control glide.

Practice features and loops.

Understand modules and report coping with.

Explore OOP concepts.

Work on small initiatives (e.G., calculator, to-do app).

Dive into unique areas like statistics technological know-how, automation, or web development.

[shalu620]

Why Python Will Thrive: Future Trends and Applications

Python has already made a significant impact in the tech world, and its trajectory for the future is even more promising. From its simplicity and versatility to its widespread use in cutting-edge technologies, Python is expected to continue thriving in the coming years. Considering the kind support of Python Course in Chennai Whatever your level of experience or reason for switching from another programming language, learning Python gets much more fun.

Let's explore why Python will remain at the forefront of software development and what trends and applications will contribute to its ongoing dominance.

1. Artificial Intelligence and Machine Learning

Python is already the go-to language for AI and machine learning, and its role in these fields is set to expand further. With powerful libraries such as TensorFlow, PyTorch, and Scikit-learn, Python simplifies the development of machine learning models and artificial intelligence applications. As more industries integrate AI for automation, personalization, and predictive analytics, Python will remain a core language for developing intelligent systems.

2. Data Science and Big Data

Data science is one of the most significant areas where Python has excelled. Libraries like Pandas, NumPy, and Matplotlib make data manipulation and visualization simple and efficient. As companies and organizations continue to generate and analyze vast amounts of data, Python’s ability to process, clean, and visualize big data will only become more critical. Additionally, Python’s compatibility with big data platforms like Hadoop and Apache Spark ensures that it will remain a major player in data-driven decision-making.

3. Web Development

Python’s role in web development is growing thanks to frameworks like Django and Flask, which provide robust, scalable, and secure solutions for building web applications. With the increasing demand for interactive websites and APIs, Python is well-positioned to continue serving as a top language for backend development. Its integration with cloud computing platforms will also fuel its growth in building modern web applications that scale efficiently.

4. Automation and Scripting

Automation is another area where Python excels. Developers use Python to automate tasks ranging from system administration to testing and deployment. With the rise of DevOps practices and the growing demand for workflow automation, Python’s role in streamlining repetitive processes will continue to grow. Businesses across industries will rely on Python to boost productivity, reduce errors, and optimize performance. With the aid of Best Online Training & Placement Programs, which offer comprehensive training and job placement support to anyone looking to develop their talents, it’s easier to learn this tool and advance your career.

5. Cybersecurity and Ethical Hacking

With cyber threats becoming increasingly sophisticated, cybersecurity is a critical concern for businesses worldwide. Python is widely used for penetration testing, vulnerability scanning, and threat detection due to its simplicity and effectiveness. Libraries like Scapy and PyCrypto make Python an excellent choice for ethical hacking and security professionals. As the need for robust cybersecurity measures increases, Python’s role in safeguarding digital assets will continue to thrive.

6. Internet of Things (IoT)

Python’s compatibility with microcontrollers and embedded systems makes it a strong contender in the growing field of IoT. Frameworks like MicroPython and CircuitPython enable developers to build IoT applications efficiently, whether for home automation, smart cities, or industrial systems. As the number of connected devices continues to rise, Python will remain a dominant language for creating scalable and reliable IoT solutions.

7. Cloud Computing and Serverless Architectures

The rise of cloud computing and serverless architectures has created new opportunities for Python. Cloud platforms like AWS, Google Cloud, and Microsoft Azure all support Python, allowing developers to build scalable and cost-efficient applications. With its flexibility and integration capabilities, Python is perfectly suited for developing cloud-based applications, serverless functions, and microservices.

8. Gaming and Virtual Reality

Python has long been used in game development, with libraries such as Pygame offering simple tools to create 2D games. However, as gaming and virtual reality (VR) technologies evolve, Python’s role in developing immersive experiences will grow. The language’s ease of use and integration with game engines will make it a popular choice for building gaming platforms, VR applications, and simulations.

9. Expanding Job Market

As Python’s applications continue to grow, so does the demand for Python developers. From startups to tech giants like Google, Facebook, and Amazon, companies across industries are seeking professionals who are proficient in Python. The increasing adoption of Python in various fields, including data science, AI, cybersecurity, and cloud computing, ensures a thriving job market for Python developers in the future.

10. Constant Evolution and Community Support

Python’s open-source nature means that it’s constantly evolving with new libraries, frameworks, and features. Its vibrant community of developers contributes to its growth and ensures that Python stays relevant to emerging trends and technologies. Whether it’s a new tool for AI or a breakthrough in web development, Python’s community is always working to improve the language and make it more efficient for developers.

Conclusion

Python’s future is bright, with its presence continuing to grow in AI, data science, automation, web development, and beyond. As industries become increasingly data-driven, automated, and connected, Python’s simplicity, versatility, and strong community support make it an ideal choice for developers. Whether you are a beginner looking to start your coding journey or a seasoned professional exploring new career opportunities, learning Python offers long-term benefits in a rapidly evolving tech landscape.

[sindhu14]

What is Python, How to Learn Python?

What is Python?

Python is a high-level, interpreted programming language known for its simplicity and readability. It is widely used in various fields like: ✅ Web Development (Django, Flask) ✅ Data Science & Machine Learning (Pandas, NumPy, TensorFlow) ✅ Automation & Scripting (Web scraping, File automation) ✅ Game Development (Pygame) ✅ Cybersecurity & Ethical Hacking ✅ Embedded Systems & IoT (MicroPython)

Python is beginner-friendly because of its easy-to-read syntax, large community, and vast library support.

How Long Does It Take to Learn Python?

The time required to learn Python depends on your goals and background. Here’s a general breakdown:

1. Basics of Python (1-2 months)

If you spend 1-2 hours daily, you can master:

Variables, Data Types, Operators

Loops & Conditionals

Functions & Modules

Lists, Tuples, Dictionaries

File Handling

Basic Object-Oriented Programming (OOP)

2. Intermediate Level (2-4 months)

Once comfortable with basics, focus on:

Advanced OOP concepts

Exception Handling

Working with APIs & Web Scraping

Database handling (SQL, SQLite)

Python Libraries (Requests, Pandas, NumPy)

Small real-world projects

3. Advanced Python & Specialization (6+ months)

If you want to go pro, specialize in:

Data Science & Machine Learning (Matplotlib, Scikit-Learn, TensorFlow)

Web Development (Django, Flask)

Automation & Scripting

Cybersecurity & Ethical Hacking

Learning Plan Based on Your Goal

📌 Casual Learning – 3-6 months (for automation, scripting, or general knowledge) 📌 Professional Development – 6-12 months (for jobs in software, data science, etc.) 📌 Deep Mastery – 1-2 years (for AI, ML, complex projects, research)

Scope @ NareshIT:

At NareshIT’s Python application Development program you will be able to get the extensive hands-on training in front-end, middleware, and back-end technology.

It skilled you along with phase-end and capstone projects based on real business scenarios.

Here you learn the concepts from leading industry experts with content structured to ensure industrial relevance.

An end-to-end application with exciting features

Earn an industry-recognized course completion certificate.

For more details:

[thesafewaystobereckless]

Oldschool determinants & floating points

Forget those easypeasy 2x2, 3x3, and 4x4 (if you're feeling spicy) formulas for matrix determinants that you forced yourself to memorize in linear algebra. Real ones know that you can get the determinant of any matrix through LU decomposition.

Fun fact though, because Python floating point numbers aren't really the same as floating point numbers in C (python does everything with infinite precision), I was getting different answers for determinants I computed with my LU algorithm versus what NumPy was getting with the algorithm it uses from LAPACK.

For instant with a 99x99 random matrix, the absolute difference in our determinants was over 1 trillion.

BUT

Each determinant was on the order of 10^25 (because the matrices were filled with values from a uniform random distribution and to oversimplify these don't really play nice with det algorithms).

This was my value: -1.3562741025533489e+25

This was NumPy's value: -1.3562741025534902e+25

I bolded the digits where they differed in value. The actual percentage difference between them is basically 0, ~10^-10

I'm sure in some applications this would matter, but for me it doesn't really.

[education43]

What Are the Qualifications for a Data Scientist?

In today's data-driven world, the role of a data scientist has become one of the most coveted career paths. With businesses relying on data for decision-making, understanding customer behavior, and improving products, the demand for skilled professionals who can analyze, interpret, and extract value from data is at an all-time high. If you're wondering what qualifications are needed to become a successful data scientist, how DataCouncil can help you get there, and why a data science course in Pune is a great option, this blog has the answers.

The Key Qualifications for a Data Scientist

To succeed as a data scientist, a mix of technical skills, education, and hands-on experience is essential. Here are the core qualifications required:

1. Educational Background

A strong foundation in mathematics, statistics, or computer science is typically expected. Most data scientists hold at least a bachelor’s degree in one of these fields, with many pursuing higher education such as a master's or a Ph.D. A data science course in Pune with DataCouncil can bridge this gap, offering the academic and practical knowledge required for a strong start in the industry.

2. Proficiency in Programming Languages

Programming is at the heart of data science. You need to be comfortable with languages like Python, R, and SQL, which are widely used for data analysis, machine learning, and database management. A comprehensive data science course in Pune will teach these programming skills from scratch, ensuring you become proficient in coding for data science tasks.

3. Understanding of Machine Learning

Data scientists must have a solid grasp of machine learning techniques and algorithms such as regression, clustering, and decision trees. By enrolling in a DataCouncil course, you'll learn how to implement machine learning models to analyze data and make predictions, an essential qualification for landing a data science job.

4. Data Wrangling Skills

Raw data is often messy and unstructured, and a good data scientist needs to be adept at cleaning and processing data before it can be analyzed. DataCouncil's data science course in Pune includes practical training in tools like Pandas and Numpy for effective data wrangling, helping you develop a strong skill set in this critical area.

5. Statistical Knowledge

Statistical analysis forms the backbone of data science. Knowledge of probability, hypothesis testing, and statistical modeling allows data scientists to draw meaningful insights from data. A structured data science course in Pune offers the theoretical and practical aspects of statistics required to excel.

6. Communication and Data Visualization Skills

Being able to explain your findings in a clear and concise manner is crucial. Data scientists often need to communicate with non-technical stakeholders, making tools like Tableau, Power BI, and Matplotlib essential for creating insightful visualizations. DataCouncil’s data science course in Pune includes modules on data visualization, which can help you present data in a way that’s easy to understand.

7. Domain Knowledge

Apart from technical skills, understanding the industry you work in is a major asset. Whether it’s healthcare, finance, or e-commerce, knowing how data applies within your industry will set you apart from the competition. DataCouncil's data science course in Pune is designed to offer case studies from multiple industries, helping students gain domain-specific insights.

Why Choose DataCouncil for a Data Science Course in Pune?

If you're looking to build a successful career as a data scientist, enrolling in a data science course in Pune with DataCouncil can be your first step toward reaching your goals. Here’s why DataCouncil is the ideal choice:

Comprehensive Curriculum: The course covers everything from the basics of data science to advanced machine learning techniques.

Hands-On Projects: You'll work on real-world projects that mimic the challenges faced by data scientists in various industries.

Experienced Faculty: Learn from industry professionals who have years of experience in data science and analytics.

100% Placement Support: DataCouncil provides job assistance to help you land a data science job in Pune or anywhere else, making it a great investment in your future.

Flexible Learning Options: With both weekday and weekend batches, DataCouncil ensures that you can learn at your own pace without compromising your current commitments.

Conclusion

Becoming a data scientist requires a combination of technical expertise, analytical skills, and industry knowledge. By enrolling in a data science course in Pune with DataCouncil, you can gain all the qualifications you need to thrive in this exciting field. Whether you're a fresher looking to start your career or a professional wanting to upskill, this course will equip you with the knowledge, skills, and practical experience to succeed as a data scientist.

Explore DataCouncil’s offerings today and take the first step toward unlocking a rewarding career in data science! Looking for the best data science course in Pune? DataCouncil offers comprehensive data science classes in Pune, designed to equip you with the skills to excel in this booming field. Our data science course in Pune covers everything from data analysis to machine learning, with competitive data science course fees in Pune. We provide job-oriented programs, making us the best institute for data science in Pune with placement support. Explore online data science training in Pune and take your career to new heights!

[govindhtech]

AI Frameworks Help Data Scientists For GenAI Survival

AI Frameworks: Crucial to the Success of GenAI

Develop Your AI Capabilities Now

You play a crucial part in the quickly growing field of generative artificial intelligence (GenAI) as a data scientist. Your proficiency in data analysis, modeling, and interpretation is still essential, even though platforms like Hugging Face and LangChain are at the forefront of AI research.

Although GenAI systems are capable of producing remarkable outcomes, they still mostly depend on clear, organized data and perceptive interpretation areas in which data scientists are highly skilled. You can direct GenAI models to produce more precise, useful predictions by applying your in-depth knowledge of data and statistical techniques. In order to ensure that GenAI systems are based on strong, data-driven foundations and can realize their full potential, your job as a data scientist is crucial. Here’s how to take the lead:

Data Quality Is Crucial

The effectiveness of even the most sophisticated GenAI models depends on the quality of the data they use. By guaranteeing that the data is relevant, AI tools like Pandas and Modin enable you to clean, preprocess, and manipulate large datasets.

Analysis and Interpretation of Exploratory Data

It is essential to comprehend the features and trends of the data before creating the models. Data and model outputs are visualized via a variety of data science frameworks, like Matplotlib and Seaborn, which aid developers in comprehending the data, selecting features, and interpreting the models.

Model Optimization and Evaluation

A variety of algorithms for model construction are offered by AI frameworks like scikit-learn, PyTorch, and TensorFlow. To improve models and their performance, they provide a range of techniques for cross-validation, hyperparameter optimization, and performance evaluation.

Model Deployment and Integration

Tools such as ONNX Runtime and MLflow help with cross-platform deployment and experimentation tracking. By guaranteeing that the models continue to function successfully in production, this helps the developers oversee their projects from start to finish.

Intel’s Optimized AI Frameworks and Tools

The technologies that developers are already familiar with in data analytics, machine learning, and deep learning (such as Modin, NumPy, scikit-learn, and PyTorch) can be used. For the many phases of the AI process, such as data preparation, model training, inference, and deployment, Intel has optimized the current AI tools and AI frameworks, which are based on a single, open, multiarchitecture, multivendor software platform called oneAPI programming model.

Data Engineering and Model Development:

To speed up end-to-end data science pipelines on Intel architecture, use Intel’s AI Tools, which include Python tools and frameworks like Modin, Intel Optimization for TensorFlow Optimizations, PyTorch Optimizations, IntelExtension for Scikit-learn, and XGBoost.

Optimization and Deployment

For CPU or GPU deployment, Intel Neural Compressor speeds up deep learning inference and minimizes model size. Models are optimized and deployed across several hardware platforms including Intel CPUs using the OpenVINO toolbox.

You may improve the performance of your Intel hardware platforms with the aid of these AI tools.

Library of Resources

Discover collection of excellent, professionally created, and thoughtfully selected resources that are centered on the core data science competencies that developers need. Exploring machine and deep learning AI frameworks.

What you will discover:

Use Modin to expedite the extract, transform, and load (ETL) process for enormous DataFrames and analyze massive datasets.

To improve speed on Intel hardware, use Intel’s optimized AI frameworks (such as Intel Optimization for XGBoost, Intel Extension for Scikit-learn, Intel Optimization for PyTorch, and Intel Optimization for TensorFlow).

Use Intel-optimized software on the most recent Intel platforms to implement and deploy AI workloads on Intel Tiber AI Cloud.

How to Begin

Frameworks for Data Engineering and Machine Learning

Step 1: View the Modin, Intel Extension for Scikit-learn, and Intel Optimization for XGBoost videos and read the introductory papers.

Modin: To achieve a quicker turnaround time overall, the video explains when to utilize Modin and how to apply Modin and Pandas judiciously. A quick start guide for Modin is also available for more in-depth information.

Scikit-learn Intel Extension: This tutorial gives you an overview of the extension, walks you through the code step-by-step, and explains how utilizing it might improve performance. A movie on accelerating silhouette machine learning techniques, PCA, and K-means clustering is also available.

Intel Optimization for XGBoost: This straightforward tutorial explains Intel Optimization for XGBoost and how to use Intel optimizations to enhance training and inference performance.

Step 2: Use Intel Tiber AI Cloud to create and develop machine learning workloads.

On Intel Tiber AI Cloud, this tutorial runs machine learning workloads with Modin, scikit-learn, and XGBoost.

Step 3: Use Modin and scikit-learn to create an end-to-end machine learning process using census data.

Run an end-to-end machine learning task using 1970–2010 US census data with this code sample. The code sample uses the Intel Extension for Scikit-learn module to analyze exploratory data using ridge regression and the Intel Distribution of Modin.

Deep Learning Frameworks

Step 4: Begin by watching the videos and reading the introduction papers for Intel’s PyTorch and TensorFlow optimizations.

Intel PyTorch Optimizations: Read the article to learn how to use the Intel Extension for PyTorch to accelerate your workloads for inference and training. Additionally, a brief video demonstrates how to use the addon to run PyTorch inference on an Intel Data Center GPU Flex Series.

Intel’s TensorFlow Optimizations: The article and video provide an overview of the Intel Extension for TensorFlow and demonstrate how to utilize it to accelerate your AI tasks.

Step 5: Use TensorFlow and PyTorch for AI on the Intel Tiber AI Cloud.

In this article, it show how to use PyTorch and TensorFlow on Intel Tiber AI Cloud to create and execute complicated AI workloads.

Step 6: Speed up LSTM text creation with Intel Extension for TensorFlow.

The Intel Extension for TensorFlow can speed up LSTM model training for text production.

Step 7: Use PyTorch and DialoGPT to create an interactive chat-generation model.

Discover how to use Hugging Face’s pretrained DialoGPT model to create an interactive chat model and how to use the Intel Extension for PyTorch to dynamically quantize the model.

Read more on Govindhtech.com

[blubberquark]

New Year's Resolutions

Hey everybody. It's a new year. Happy New Year!

Gamedev Blogging

Last year I have fallen behind on posting gamedev stuff. It's mostly because there is no good way to format code listings in the new editor. So this year, I am not even going to try eith gamedev tutorials on tumblr. I might post them elsewhere and just link them. I have already taken a look at Cohost, but It doesn't have the features I need. Wouldn't it be cook if you could post pico-8 carts on cohost? Or source code listings? Or LaTeX? I might as well write the HTML by hand and host it somewhere. But that won't be the focus of this blog in 2024.

Instead I'm going to do more tumblr posting about game design, just less on the code side. It will be more on the screenshot side. First thing will be about my 2023 Game Of The Year. It will probably surprise you. I did not expect it to be this good. You can also expect something about some of my old prototypes. Over the years I have started and abandoned game prototypes after either concluding that the idea won't work and can't be made to work, or after learning what I needed to learn. What did I learn? Wait and find out!

Computer Litaracy

I'll also attempt to write more about general computing and "computer literacy" topics. I have two particular "series" or "categories" in mind already. Almost Good: Technologies that sound great when you hear abut them, but that don't work as well as you might think when you try them out. Harmful Assumptions About Computing: Non-technical people often have surprising ideas about how computers work. As a technically inclined person, you don't even realise how far these unspoken assumptions about computers can reach.

Usability of computers and software seems to have gotten worse rather than better in many aspects, while computers have become entrenched in every workplace, our private lives, and in our interactions with corporations and government services. Computer literacy has also become worse in certain ways, and I think I know some reasons why.

There will also be some posts about forum moderation and community management. It's rather basic and common-sense stuff, but I want to spell it out.

Actual Game Development

I am going to release a puzzle game in 2024. You will be able to buy it for money. You can hold me to it. This is my biggest New Year's Resolution.

I will continue to work on two games of mine. One will be the game I just mentioned. The other is Wyst. I put the project on ice because I was running out of inspiration for a while, but I think I am sufficiently inspired now. I will pick it up again and add two more worlds to the game, and get it into a "complete" state. I'll also have to do a whole lot of playtesting. This may be the last time I touch Unity3d.

I will try out two new engines and write one or two proof-of-concept games in each of them, maybe something really simple like "Flappy Bird", and one game jam "warm-up" thing, with the scope of a Ludum Dare compo game. Maybe that means I'll write Tetris or Pong multiple times. I probably won't put the "Pong in Godot" on itch.io page next to a "Pong in Raylib" and "Pong in Bevy", but I'll just put the code on my GitHub. The goal is to have more options for a game jam, so I can decide to use Godot if it is a better fit for the jam topic.

In the past, I have always reached for PyGame by default, because Python is the language that has flask and Django and sqlalchemy and numpy and pyTorch, and because I mostly want to make games in 2D. I want to get out of my comfort zone. In addition to the general-purpose game engines, I will try to develop something in bitsy, AGS, twine, pico-8 or Ren'Py. I want to force myself to try a different genre this way. Maybe I'll make an archaeologist dating simulator.

All in all, this means I will so significant work on two existing projects, revisit some old failed prototypes to do a postmortem, I'll write at least six new prototypes, and two new jam games, plus some genre/narrative experiments. That's a lot already. So here's an anti-resolution: I won't even try to develop any of my new prototypes into full releases. I will only work on existing projects from 2023 or before if I develop anything into playable demo versions or full games. I won't get sidetracked by the next Ludum Dare game, I promise. After the jam is over, I'll put down the project, at least until 2025.

[clumsysprings]

Working on a Python project for weeks and I'm adamant we use Numpy. I'm new to numpy, and my partner knows no Python. I'm convinced this'll be super efficient because Numpy is good.

We can't get Numpy to work, we spend weeks getting everything working and nothing is working.

We end up getting rid of all of it and using standard Python arrays.

Make more progress in one night than we did in weeks.

I was a gigantic asshat for insisting we do something a certain way. Legitimately detrimental to doing what we need to do. Now that the professor and my partner are working on it because this went from a class assignment to a potential research paper. (Worth noting that we couldn't have succeeded without assistance from the professor so he's cool) everything I contributed has been removed at the end of the day except wasting weeks of sleepless nights and it's specifically and unequivocally at my hands.

I feel stupid and asshattish and as if I owe someone something. I'm acting defensive and I shouldn't because they're right when they say that this should be done in the easy straightforward way.

I feel like lashing out and I can't do that. I'm aware I'm in the wrong. I was wrong and I'm angry that it feels like I'm a prick

[mr-abhishek-kumar]

Morning python study log 03-11-2023

So these days I have started to stream my code study.

So today morning I learnt:

How to take absolute value. Found some anomaly in the system lol. Basically it was not taking abs() but fabs() however my python was the latest version

I studied how to sort three numbers in python, although I have done this in other language since the syntax of python is still foreign to me I had difficulty sorting them in ascending order and also descending order using the built in function sorted() and also making my own implementation

I understood what is range function and how to use it with for loops, had a bit of hit and miss while understanding how it really worked but google's bard helped, I also learnt about reverse sorting

I learnt what is interning while trying to understand the difference between identity operators and equality operators. Found some anomaly in my system again, that my computer's range of interning is much larger than what is documented ?

I learnt what is keyword argument when with using reverse built in sort, yeah so I was amazed that the order of arguments didn't mattered for keyword argument.

I was also confusing syntax of python with javascript since that is what is what recently code in.

Learnt about what does len() function does, like properly rather than just guessing about what it does.

understood about control statements such as if, else and elif

learnt about break and continue in loops in python which is same as java script.

learnt about how to check the divisibility of a number. I didn't knew that it was separate topic in my syllabus I just thought it was something people would knew.

Learnt the basics about on how to make a READ , EVAL PRINT LOOP, REPL

Learnt about stupid pattern program in python, I don't know why the heck they still teach these things and put it in syllabus. There is no real world use of it as far as I can see. I still have to post the notes about it in my blogs and store it my cloud drive.

Learnt how to do a summation of series, using and not using numpy.

figured out how to do a factorial of a number

was trying to make an short algorithm on how to do the fibonacci series but well, I was so sleepy that my mind didn't worked as it should, I took the hint from bard then felt bad that I was directly looking at the solution when rather I should sleep and approach the problem from afresh in next study stream. So stopped my study stream.