#python modules and libraries

[edujournalblogs]

Power of Python

We have seen a lot of python developers worldwide who are able to leverage the functionality of modules/libraries, that are built by people at the individual level and also with standard built-in libraries available in Python, to do lot of interesting projects. We are able to fast track our progress and do a lot of really good projects pretty quickly. The projects can be done by

Developing your own module

Utilize the Built-in Standard modules/libraries provided by python. You can import the library and use it. eg random() that generates random numbers and datetime() function that can do date and time details.

Python Community: There is a huge community worldwide for Python. For eg. The libraries that we use in Data Science regularly such as Pandas, Numpy, Matplotlib etc are not available instandard built-in Python library. They are developed by developers and shared within the community. You only need to use 'pip3 install ' command to to install it in your system and use the import it thereafter and include them in your code.

Check out our master program in Data Science and ASP.NET- Complete Beginner to Advanced course and boost your confidence and knowledge.

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[yoneda-emma]

This is sort of an indirect addition to this ask maddy got about learning rust, I haven't actually gotten to learning rust yet but I do want to talk about an incident I recently had with python (I think with C the reasons why you'd rather not use it are fairly obvious) So recently tried to implement a basic matrix class in python, and since I wanted to do a whole thing where I did my own implementations of numerical optimization and more general linear algebra stuff, I tried to do it "properly" and make it "robust" and everything, but without installing any external libraries.

And to me, that obviously involved making sure that the matrix is formatted sensibly, i.e. that a matrix contains m rows of n numbers each. This seemed like a very obvious thing you should do in any serious piece of code, since if the contents of a matrix are accidentally formatted in a weird way, then you might get errors, or, significantly worse, python might just decide that it "can handle them" anyways and do some really unintuitive dumb stuff that's really hard to debug. (See this older post of mine for an example of how the pythonic willingness to work with bad inputs leads to really weird unintuitive behavior).

Turns out this is not something you can do directly in python without installing external type checking libraries! And I didn't want to just loop through all the contents and check their type individually during object creation, since that felt incredibly slow, stupid and inefficient. It didnt help that my algorithms theory exam was coming up soon, which meant I was thinking about asymptotic runtimes all day.

And so I was like "well surely at least it's easy to check for a matrix being a 2D array with consistent row sizes". However, at this point, with dawning horror, I came to a realization:

and at this moment I could just feel pretty much all of my remaining "python is easy to work with" attitude turning into dust and soaring away in the wind. If anyone here knows a way to enforce a given argument being a 2D array of numbers with consistent row sizes that doesn't involve O(n*m) overhead during object creation and that can be implemented in python using only internal modules (no external type checkers that need to be installed manually first) please tell me lol

[utopicwork]

Big fan of this warning on primitives in the python cryptography library documentation

Transcription:

This is a “Hazardous Materials” module. You should ONLY use it if you’re 100% absolutely sure that you know what you’re doing because this module is full of land mines, dragons, and dinosaurs with laser guns.

End transcription

[sak-shi]

Python Libraries to Learn Before Tackling Data Analysis

To tackle data analysis effectively in Python, it's crucial to become familiar with several libraries that streamline the process of data manipulation, exploration, and visualization. Here's a breakdown of the essential libraries:

 1. NumPy

   - Purpose: Numerical computing.

   - Why Learn It: NumPy provides support for large multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays efficiently.

   - Key Features:

     - Fast array processing.

     - Mathematical operations on arrays (e.g., sum, mean, standard deviation).

     - Linear algebra operations.

 2. Pandas

   - Purpose: Data manipulation and analysis.

   - Why Learn It: Pandas offers data structures like DataFrames, making it easier to handle and analyze structured data.

   - Key Features:

     - Reading/writing data from CSV, Excel, SQL databases, and more.

     - Handling missing data.

     - Powerful group-by operations.

     - Data filtering and transformation.

 3. Matplotlib

   - Purpose: Data visualization.

   - Why Learn It: Matplotlib is one of the most widely used plotting libraries in Python, allowing for a wide range of static, animated, and interactive plots.

   - Key Features:

     - Line plots, bar charts, histograms, scatter plots.

     - Customizable charts (labels, colors, legends).

     - Integration with Pandas for quick plotting.

 4. Seaborn

   - Purpose: Statistical data visualization.

   - Why Learn It: Built on top of Matplotlib, Seaborn simplifies the creation of attractive and informative statistical graphics.

   - Key Features:

     - High-level interface for drawing attractive statistical graphics.

     - Easier to use for complex visualizations like heatmaps, pair plots, etc.

     - Visualizations based on categorical data.

 5. SciPy

   - Purpose: Scientific and technical computing.

   - Why Learn It: SciPy builds on NumPy and provides additional functionality for complex mathematical operations and scientific computing.

   - Key Features:

     - Optimized algorithms for numerical integration, optimization, and more.

     - Statistics, signal processing, and linear algebra modules.

 6. Scikit-learn

   - Purpose: Machine learning and statistical modeling.

   - Why Learn It: Scikit-learn provides simple and efficient tools for data mining, analysis, and machine learning.

   - Key Features:

     - Classification, regression, and clustering algorithms.

     - Dimensionality reduction, model selection, and preprocessing utilities.

 7. Statsmodels

   - Purpose: Statistical analysis.

   - Why Learn It: Statsmodels allows users to explore data, estimate statistical models, and perform tests.

   - Key Features:

     - Linear regression, logistic regression, time series analysis.

     - Statistical tests and models for descriptive statistics.

 8. Plotly

   - Purpose: Interactive data visualization.

   - Why Learn It: Plotly allows for the creation of interactive and web-based visualizations, making it ideal for dashboards and presentations.

   - Key Features:

     - Interactive plots like scatter, line, bar, and 3D plots.

     - Easy integration with web frameworks.

     - Dashboards and web applications with Dash.

 9. TensorFlow/PyTorch (Optional)

   - Purpose: Machine learning and deep learning.

   - Why Learn It: If your data analysis involves machine learning, these libraries will help in building, training, and deploying deep learning models.

   - Key Features:

     - Tensor processing and automatic differentiation.

     - Building neural networks.

 10. Dask (Optional)

   - Purpose: Parallel computing for data analysis.

   - Why Learn It: Dask enables scalable data manipulation by parallelizing Pandas operations, making it ideal for big datasets.

   - Key Features:

     - Works with NumPy, Pandas, and Scikit-learn.

     - Handles large data and parallel computations easily.

Focusing on NumPy, Pandas, Matplotlib, and Seaborn will set a strong foundation for basic data analysis.

[willcodehtmlforfood]

The README file for the project instruct the victim first to execute the malicious password manager application ('PasswordManager.py') on their system and then start looking for the errors and fixing them.

That file triggers the execution of a base64 obfuscated module hidden in the'_init_.py' files of the 'pyperclip' and 'pyrebase' libraries.

The obfuscated string is a malware downloader that contacts a command and control (C2) server and awaits for commands. Fetching and running additional payloads is within its capabilities.

[depressed-undergrad]

21 August, 2024

Weird day today. I woke up at my usual time and I walked for less time than usual but somehow I still didn't get time to study and was functioning without any buffer. I had an 8:30 lab today so that was the first bit of studying I got for the day. Got back and spent 1hr on Coursera. Went for lunch. Had three lectures back-to-back but the last got cancelled so I headed to the library with a friend. Spent about 1.5 hour studying. Took a 30 minute break to eat, before going back to the library for another 1.5 hours. Spent some of the library time on Coursera and some revising electrostats. Did more electrostats than I'd set a target for and finished the entirety of module 2. I skipped dinner and revised what we studied throughout the day. Did some night reading and daily chores and went to bed in the pretty pyjamas.

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[bharatpatel1061]

Static Typing in Dynamic Languages: A Modern Safety Net

Content: Traditionally, dynamic languages like Python and JavaScript traded compile-time type safety for speed and flexibility. But today, optional static typing—via tools like TypeScript for JavaScript or Python’s typing module—brings the best of both worlds.

Static types improve code readability, tooling (like autocompletion), and catch potential errors early. They also make refactoring safer and large-scale collaboration easier.

TypeScript’s popularity showcases how adding types to JavaScript empowers developers to manage growing codebases with greater confidence. Similarly, using Python’s type hints with tools like mypy can improve code robustness without sacrificing Python’s simplicity.

For teams prioritizing long-term maintainability, adopting static typing early pays dividends. Organizations, including Software Development, advocate for using typing disciplines to future-proof projects without overcomplicating development.

Static typing is not about perfection; it’s about increasing predictability and easing future changes.

Start by adding types to critical parts of your codebase—public APIs, core data models, and utility libraries—before expanding to the entire project.

[izicodes]

Any good python modules I can learn now that I'm familiar with the basics?

Hiya 💗

Yep, here's a bunch you can import them into your program to play around with!

math: Provides mathematical functions and constants.

random: Enables generation of random numbers, choices, and shuffling.

datetime: Offers classes for working with dates and times.

os: Allows interaction with the operating system, such as file and directory manipulation.

sys: Provides access to system-specific parameters and functions.

json: Enables working with JSON (JavaScript Object Notation) data.

csv: Simplifies reading and writing CSV (Comma-Separated Values) files.

re: Provides regular expression matching operations.

requests: Allows making HTTP requests to interact with web servers.

matplotlib: A popular plotting library for creating visualizations.

numpy: Enables numerical computations and working with arrays.

pandas: Provides data structures and analysis tools for data manipulation.

turtle: Allows creating graphics and simple games using turtle graphics.

time: Offers functions for time-related operations.

argparse: Simplifies creating command-line interfaces with argument parsing.

How to actually import to your program?

Just in case you don't know, or those reading who don't know:

Use the 'import' keyword, preferably at the top of the page, and the name of the module you want to import. OPTIONAL: you could add 'as [shortname you want to name it in your program]' at the end to use the shortname instead of the whole module name

Hope this helps, good luck with your Python programming! 🙌🏾

[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.

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Earn an industry-recognized course completion certificate.

For more details:

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Benefits of Diploma in Computer Application (DCA)

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Rapidly develop feature-rich applications using Python's built-in statements, functions, and collection types. Structure code with classes, modules, and packages that leverage object-oriented features. Create multiple data accessors to manage various data storage formats. Access additional features with library modules and packages.

Python for Web Development – Flask Flask is a popular Python API that allows experts to build web applications. Python 2.6 and higher variants must install Flask, and you can import Flask on any Python IDE from the Flask package. This section of the course will help you install Flask and learn how to use the Python Flask Framework.

Subjects covered in Python for Web development using Flask:

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Some other factors that affect the cost of a Python course are its duration, course syllabus, number of practical sessions, institute reputation and location, trainers’ expertise, etc. What is the duration of a Python course? The duration of a basic Python course is generally between 3 month to 6 months, and advanced courses can be 1 year . However, some courses extend up to 1 year and more when they combine multiple other courses or include internship programs.

Advantages of Python Python is easy to learn and put into practice. … Functions are defined. … Python allows for quick coding. … Python is versatile. … Python understands compound data types. … Libraries in data science have Python interfaces. … Python is widely supported.

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[pandeypankaj]

How do I learn Python in depth?

Improving Your Python Skills

  Writing Python Programs Basics: Practice the basics solidly. 

  Syntax and Semantics: Make sure you are very strong in variables, data types, control flow, functions, and object-oriented programming. 

 Data Structures: Be able to work with lists, tuples, dictionaries, and sets, and know when to use which. 

 Modules and Packages: Study how to import and use built-in and third-party modules. 

Advanced Concepts

Generators and Iterators: Know how to develop efficient iterators and generators for memory-efficient code. 

Decorators: Learn how to dynamically alter functions using decorators. 

Metaclasses: Understand how classes are created and can be customized. 

Context Managers: Understand how contexts work with statements. 

Project Practice 

 Personal Projects: You will work on projects that you want to, whether building a web application, data analysis tool, or a game.

 Contributing to Open Source: Contribute to open-source projects in order to learn from senior developers. Get exposed to real-life code. 

 Online Challenges: Take part in coding challenges on HackerRank, LeetCode, or Project Euler. 

 Learn Various Libraries and Frameworks

 Scientific Computing: NumPy, SciPy, Pandas

 Data Visualization: Matplotlib, Seaborn

 Machine Learning: Scikit-learn, TensorFlow, PyTorch

 Web Development: Django, Flask

Data Analysis: Dask, Airflow

Read Pythonic Code

 Open Source Projects: Study the source code of a few popular Python projects. Go through their best practices and idiomatic Python. 

 Books and Tutorials: Read all the code examples in books and tutorials on Python. 

 Conferences and Workshops

  Attend conferences and workshops that will help you further your skills in Python. PyCon is an annual Python conference that includes talks, workshops, and even networking opportunities. Local meetups will let you connect with other Python developers in your area. 

Learn Continuously

 Follow Blogs and Podcasts: Keep reading blogs and listening to podcasts that will keep you updated with the latest trends and developments taking place within the Python community.

Online Courses: Advanced understanding in Python can be acquired by taking online courses on the subject.

 Try It Yourself: Trying new techniques and libraries expands one's knowledge.

Other Recommendations

 Readable-Clean Code: For code writing, it's essential to follow the style guide in Python, PEP 

Naming your variables and functions as close to their utilization as possible is also recommended.

 Test Your Code: Unit tests will help in establishing the correctness of your code.

 Coding with Others: Doing pair programming and code reviews would provide you with experience from other coders.

 You are not Afraid to Ask for Help: Never hesitate to ask for help when things are beyond your hand-on areas, be it online communities or mentors.

These steps, along with consistent practice, will help you become proficient in Python development and open a wide range of possibilities in your career.

[kells-little-life]

Finishing Module 1 today and submitting that for both my classes!! Classes started today but I wanted to finish things early since I’m also working full-time. Ordered some Python books from my library to read up on while studying :)

Courses I’m taking:

Introduction to Scripting

Discrete Mathematics

[shemsuji432]

Exploring Python: Features and Where It's Used

Python is a versatile programming language that has gained significant popularity in recent times. It's known for its ease of use, readability, and adaptability, making it an excellent choice for both newcomers and experienced programmers. In this article, we'll delve into the specifics of what Python is and explore its various applications.

What is Python?

Python is an interpreted programming language that is high-level and serves multiple purposes. Created by Guido van Rossum and released in 1991, Python is designed to prioritize code readability and simplicity, with a clean and minimalistic syntax. It places emphasis on using proper indentation and whitespace, making it more convenient for programmers to write and comprehend code.

Key Traits of Python :

Simplicity and Readability: Python code is structured in a way that's easy to read and understand. This reduces the time and effort required for both creating and maintaining software.

Python code example: print("Hello, World!")

Versatility: Python is applicable across various domains, from web development and scientific computing to data analysis, artificial intelligence, and more.

Python code example: import numpy as np

Extensive Standard Library: Python offers an extensive collection of pre-built libraries and modules. These resources provide developers with ready-made tools and functions to tackle complex tasks efficiently.

Python code example: import matplotlib.pyplot as plt

Compatibility Across Platforms: Python is available on multiple operating systems, including Windows, macOS, and Linux. This allows programmers to create and run code seamlessly across different platforms.

Strong Community Support: Python boasts an active community of developers who contribute to its growth and provide support through online forums, documentation, and open-source contributions. This community support makes Python an excellent choice for developers seeking assistance or collaboration.

Where is Python Utilized?

Due to its versatility, Python is utilized in various domains and industries. Some key areas where Python is widely applied include:

Web Development: Python is highly suitable for web development tasks. It offers powerful frameworks like Django and Flask, simplifying the process of building robust web applications. The simplicity and readability of Python code enable developers to create clean and maintainable web applications efficiently.

Data Science and Machine Learning: Python has become the go-to language for data scientists and machine learning practitioners. Its extensive libraries such as NumPy, Pandas, and SciPy, along with specialized libraries like TensorFlow and PyTorch, facilitate a seamless workflow for data analysis, modeling, and implementing machine learning algorithms.

Scientific Computing: Python is extensively used in scientific computing and research due to its rich scientific libraries and tools. Libraries like SciPy, Matplotlib, and NumPy enable efficient handling of scientific data, visualization, and numerical computations, making Python indispensable for scientists and researchers.

Automation and Scripting: Python's simplicity and versatility make it a preferred language for automating repetitive tasks and writing scripts. Its comprehensive standard library empowers developers to automate various processes within the operating system, network operations, and file manipulation, making it popular among system administrators and DevOps professionals.

Game Development: Python's ease of use and availability of libraries like Pygame make it an excellent choice for game development. Developers can create interactive and engaging games efficiently, and the language's simplicity allows for quick prototyping and development cycles.

Internet of Things (IoT): Python's lightweight nature and compatibility with microcontrollers make it suitable for developing applications for the Internet of Things. Libraries like Circuit Python enable developers to work with sensors, create interactive hardware projects, and connect devices to the internet.

Python's versatility and simplicity have made it one of the most widely used programming languages across diverse domains. Its clean syntax, extensive libraries, and cross-platform compatibility make it a powerful tool for developers. Whether for web development, data science, automation, or game development, Python proves to be an excellent choice for programmers seeking efficiency and user-friendliness. If you're considering learning a programming language or expanding your skills, Python is undoubtedly worth exploring.

[softssolutionservice]

Python Development Course: Empowering the Future with Softs Solution Service

Python, a high-level programming language, has emerged as a favorite among developers worldwide due to its emphasis on readability and efficiency. Originating in the late 1980s, Python was conceived by Guido van Rossum as a successor to the ABC language. Its design philosophy, encapsulated by the phrase "Beautiful is better than ugly", reflects a commitment to aesthetic code and functionality. 

What sets Python apart is its versatile nature. It supports multiple programming paradigms, including procedural, object-oriented, and functional programming. This flexibility allows developers to use Python for a wide range of applications, from web development and software engineering to scientific computing and artificial intelligence. 

Python’s standard library is another of its strengths, offering a rich set of modules and tools that enable developers to perform various tasks without the need for additional installations. This extensive library, combined with Python’s straightforward syntax, makes it an excellent language for rapid application development. 

One of Python's most significant contributions to the tech world is its role in data science and machine learning. Its easy-to-learn syntax and powerful libraries, like NumPy, Pandas, and Matplotlib, make it an ideal language for data analysis and visualization. Furthermore, frameworks like TensorFlow and PyTorch have solidified Python's position in the development of machine learning models. 

Education in Python programming has become crucial due to its growing demand in the industry. Recognizing this, institutions like Softs Solution Service, IT training institute in Ahmedabad, have stepped up to provide comprehensive Python Development Training. Their Online Python Development Course is tailored to meet the needs of both beginners and seasoned programmers. This course offers an in-depth exploration of Python's capabilities, covering everything from basic syntax to advanced programming concepts. 

The course structure usually begins with an introduction to Python's basic syntax and programming concepts. It then progressively moves into more complex topics, such as data structures, file operations, error and exception handling, and object-oriented programming principles. Participants also get to work on real-life projects, which is vital for understanding how Python can be applied in practical scenarios. 

A significant advantage of online courses like the one offered by Softs Solution Service is their accessibility. Students can learn at their own pace, with access to a wealth of resources and support from experienced instructors. Additionally, these courses often provide community support, where learners can interact with peers, share knowledge, and collaborate on projects. 

Python's future seems bright as it continues to evolve with new features and enhancements. Its growing popularity in various fields, including web development, data analytics, artificial intelligence, and scientific research, ensures that Python developers will remain in high demand. 

In summary, Python is not just a programming language; it's a tool that opens a world of possibilities for developers, data scientists, and tech enthusiasts. With resources like the Online Python Development Course from Softs Solution Service, mastering Python has become more accessible than ever, promising exciting opportunities in the ever-evolving world of technology.