Augmenting Time Series Data with tsaug

Running Python checks inside Codex is fine until the main thread fills with uv, ruff, pytest, and ty logs—you lose context and still have to decode what mattered. I wrapped that work in a subagent-backed skill so checks run off-thread and return a short pass / warn / fail report, on top of the uv-first repo instructions I already use. Short write-up with install options (global vs per project): https://lnkd.in/dZub3E5S

Machine Learning Time Series Data using pyflux #machinelearning #datascience #timeseriesdata #pyflux PyFlux is a new library for time series analysis for Python. It brings together a vast array of time series models, including recent models such as score-driven models and variational state space models, as well a flexible choice of inference options, including black box variational inference. https://lnkd.in/gH-ZjwCb

Most learners do not explore beyond Python basics… the true beauty lies within👇 Today I dived deep into the following Python concepts: 🔹 Functional programming concepts ➡️ map(), filter(), lambda 🔹 Modules and Standard Library ➡️ built-in libraries of Python that make Python awesome. ➡️ I looked further into: 📦 The random module ➡️ generation of random data, simulations, sampling 📁 The os module ➡️ file handling and operating system path management 🧠 The array module ➡️ efficient memory usage for numeric data types  ✔ Regular Expressions (Re module)    → pattern-based text analysis  I created: ✔️  A Fake data generator(generates realistic fake user data) Link - https://lnkd.in/g69scMzy  ✔️ Log Analyzer(Parsed log files using regex)- Link - https://lnkd.in/gMiN3KF9 #Python #DataAnalytics #LearningInPublic #CodingJourney

# Basic Python Programs A collection of 15 beginner-friendly Python scripts. Each file demonstrates one core concept like loops, conditionals, functions, or basic I/O. ### Files and Descriptions | File | Description | | --- | --- | | `1_hello_world.py` | Prints "Hello, World!" to the console. Intro to `print()`. | | `2_add_two_numbers.py` | Takes two numbers from user input and prints their sum. Uses `input()` and type casting. | | `3_even_or_odd.py` | Checks if a user-entered integer is even or odd using the modulus operator `%`. | | `4_factorial.py` | Calculates factorial of a number using a `for` loop. | | `5_fibonacci.py` | Prints the first n terms of the Fibonacci sequence using multiple assignment. | | `6_prime_check.py` | Determines if a number is prime by checking divisibility up to its square root. | | `7_reverse_string.py` | Reverses a user-provided string using slicing `[::-1]`. | | `8_palindrome.py` | Checks if a word reads the same forwards and backwards. | | `9_largest_of_three.py` | Finds the largest among three numbers using `max()`. | | `10_calculator.py` | Simple calculator for +, -, *, / with basic `if-elif` logic and zero-division check. | | `11_multiplication_table.py` | Prints the multiplication table for a given number from 1 to 10. | | `12_sum_of_list.py` | Takes space-separated numbers, converts to a list, and prints the sum. | | `13_swap_variables.py` | Swaps two variables using Python tuple unpacking. | | `14_leap_year.py` | Checks if a given year is a leap year using standard leap year rules. | | `15_count_vowels.py` | Counts vowels in a string using a generator expression. | ### How to Run 1. Make sure you have Python 3 installed: `python --version` 2. Run any file from the terminal: `python 3_even_or_odd.py` 3. Follow the on-screen prompts for input. ### Notes - All programs use only the Python standard library — no external packages needed. - Input validation is minimal to keep the code beginner-friendly. #Python #SoftwareEngineering #Programming #ComputerScience #SoftwareDevelopment #TechEducation #OpenSource #Coding #EdTech #GitHub #DataScience #Engineering Thread Link :- https://lnkd.in/gzBu8UwY

Stop writing "clunky" Python. 🐍💻 I used to rely heavily on standard for loops for every data transformation. They work, but they can get messy fast. Today, I’m focusing on List Comprehensions to streamline my workflow. The Challenge: Calculate a 10% tax on prices, but only for items over $20. 🔴 The "Old" Way: 5 lines of code, an empty list, and an append function. 🟢 The Optimized Way: 1 clean, readable line. Less boilerplate, more efficiency. Professional-grade Python is all about writing code that is as readable as it is functional. #Python #DataScience #CodingTips #Pythonic #DataAnalytics #ContinuousLearning

🔁 Python Revision – Sets Continuing my Python fundamentals revision 🐍 In this session, I focused on: ✔️ Sets (creation and properties) ✔️ Unique elements and unordered nature ✔️ Set methods (add, remove, discard, etc.) ✔️ Set operations (union, intersection, difference) Practiced using sets to handle unique data and perform efficient operations like finding common or different elements between datasets. Documented my practice in a Jupyter Notebook and shared it as a PDF to track my progress. Understanding sets is helping me work better with data and avoid duplicates 📊 Next: dictionaries and real-world data handling 🚀 #Python #Revision #Sets #Programming #DataAnalytics #LearningJourney #Coding

Excel vs Python (Pandas) Same tasks. Same logic. Different tools. If you know Excel, you’re already closer to learning Pandas than you think Just replace clicks with code — that’s it. Stop learning tools. Start understanding logic. Team Excel or Team Python? #DataAnalytics #Python #Pandas #Excel #Upskill Credits to the Karina Samsonova

The moment you add a new condition to an if/else chain in your pipeline, you’ve modified existing code! That’s exactly the smell OCP is meant to catch. 🔍 Open for extension, closed for modification means adding new behavior by writing new code, not patching old one! In my latest article, I break down what OCP looks like in Python data projects: strategy patterns, abstract base classes, and the tradeoffs of when the extra structure is actually worth it. https://lnkd.in/eE9yicvC This is Part 2 of my SOLID series. What’s the most painful “just add another elif” you’ve ever had to maintain? 😅 #Python #SoftwareEngineering #SOLID #DataScience #CleanCode

If you work with Python, have you ever wondered:   • What are magic methods?   • Are they the same as special methods?   • What about “dunder methods”? First thing is, magic methods are not really magic.They are just special methods defined by the Python Data Model. And, guess what, magic methods, special methods and dunder methods are all the same thing. Amazing, right? Let’s look at a simple example: 𝗰𝗹𝗮𝘀𝘀 𝗠𝘆𝗖𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝗼𝗻:   𝗱𝗲𝗳 __𝗹𝗲𝗻__(𝘀𝗲𝗹𝗳):     𝗿𝗲𝘁𝘂𝗿𝗻 𝟰𝟮 Now, when you call: 𝗹𝗲𝗻(𝗼𝗯𝗷) You’re NOT calling your method directly, Python is. This is the key insight and it is very important as we are going to see on another post. Takeaway: “Magic methods” are not magic. They are contracts with the Python interpreter. Implementing such methods are going to open a lot of new doors and it is a very pythonic whay of implementing your code. #python #magicmethods #dundermethods #specialmethods

Introduction to NumPy NumPy is a Python library used for working with arrays. NumPy is a library consisting of multidimensional array objects and a collection of routines for processing the arrays In Python we have lists that serve the purpose of arrays, but they are slow to process. NumPy aims to provide an array object which works faster than traditional Python lists. NumPy arrays are stored at one continuous place in memory unlike lists, so processes can access and manipulate them very efficiently.