NumPy Arrays vs Python Lists: Speed Advantage

Python with Machine Learning — Chapter 9 📘 Topic: Python Class 🔍 Today, we're diving into a core concept: the Python Class. Think of a class as a blueprint for creating objects. It helps us organize our code in a clean, reusable way—like a recipe for making cookies! 🍪 **Why it matters in real-world learning:** In machine learning and data science, classes help us structure complex models and data pipelines. They make our code modular and easier to debug. Learning this now builds a strong foundation for advanced topics later. You've got this! 💪 **Constructor: Your Object's First Step** A constructor is a special method inside a class that runs automatically when you create a new object. Its job is to set up the object's initial state—like adding ingredients when you bake a cookie. In Python, the constructor is always named `__init__`. Let's see a simple example: [CODE] class Cookie: def __init__(self, flavor, color): self.flavor = flavor # Attribute set by constructor self.color = color print(f"A new {self.color} {self.flavor} cookie is ready!") # Create a cookie object choco_cookie = Cookie("chocolate", "brown") [/CODE] Here, `__init__` takes parameters `flavor` and `color` and assigns them to the object's attributes using `self`. When we create `choco\_cookie`, the constructor runs and prints a welcome message. Key takeaway: Every class can have one `__init__` constructor to initialize objects. It's your go-to tool for setting up data. Practice this in your code! Try creating your own class. Share your thoughts or questions below—I'm here to guide you. 🚀 #Python #MachineLearning #Beginners #Coding

Python with Machine Learning — Chapter 10 📘 Topic: Python Class - Constructors 🔍 Hey there! 👋 Let’s talk about a special part of Python classes called *constructors*. They set up your objects when you first create them—like getting a new phone ready to use. Why does this matter? 🤔 Because starting objects with the right data helps your code work smoothly, especially in machine learning where every object needs correct values to make predictions. Now, let’s look at two types of constructors: 1. DEFAULT CONSTRUCTORS 🔄 - These have NO parameters (except self). - They set up basic values automatically. Example: [CODE] class Car: def __init__(self): self.color = "red" # default value print("Car created!") my_car = Car() print(f"My car is {my_car.color}") [/CODE] Here, every Car object starts as red—no extra info needed. 2. PARAMETERIZED CONSTRUCTORS 🎯 - These take parameters so you can give each object unique values. Example: [CODE] class Car: def __init__(self, color): self.color = color print(f"Car created: {self.color}") car1 = Car("blue") car2 = Car("green") [/CODE] Now each car can have its own color. Perfect for customizing objects! A QUICK NOTE: 📝 Unlike Java or C++, Python doesn’t allow “constructor overloading” (multiple versions of __init__). If you define more than one, only the LAST one works. So plan your parameters carefully! Remember, starting simple is okay. You’re doing great! 💪 Stay curious, Your coding mentor ✨

PYTHON On TUESDAY! Here are 15 most asked Python questions for Data Enginners - 1. How would you remove duplicates from a list while preserving the order? 2. How do you flatten a nested list without using recursion? 3. What’s the difference between a shallow copy and a deep copy in Python? Give an example. 4. How do you merge two dictionaries in Python without overwriting values for duplicate keys? 5. Write Python code to read a large CSV file in chunks and process each chunk without loading the entire file into memory. 6. How would you parse a large JSON file in Python efficiently? 7. Given a log file with millions of lines, how do you extract the top 10 most frequent error messages? 8. How do you read and process compressed files (like .gz or .zip) in Python? 9. Write code to convert a CSV file to Parquet format using Pandas. 10. How do you handle missing values in a Pandas DataFrame? Show examples for filling and dropping them. 11. How would you merge two DataFrames on multiple keys with an inner join? 12. Given a time-series DataFrame, how would you resample data to a weekly frequency and calculate the sum? 13. How do you detect and remove outliers from a dataset using Pandas or NumPy? 14. Write code to group data by a column and compute multiple aggregate functions in a single statement. 15. How do you implement a retry mechanism in Python when an API call fails during ETL processing? 𝗜 𝗵𝗮𝘃𝗲 𝗽𝗿𝗲𝗽𝗮𝗿𝗲𝗱 𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗲 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗣𝗿𝗲𝗽𝗮𝗿𝗮𝘁𝗶𝗼𝗻 𝗳𝗼𝗿 𝗚𝘂𝗶𝗱𝗲 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗻𝗲𝗿𝘀. 𝗚𝗲𝘁 𝘁𝗵𝗲 𝗚𝘂𝗶𝗱𝗲 𝗵𝗲𝗿𝗲 - https://lnkd.in/eQRtDTRq If you've read so far, do LIKE and RESHARE the post👍

In Python, variables don't have types. Values do. The key concept: x = 25      # x is int x = 13.75   # x is float (changed!) x = "A"     # x is str (changed!) What this means: ✅ VALUES have types (25 is int, 3.14 is float) ❌ VARIABLES don't have fixed types ✅ Variables get their type from the VALUE assigned ✅ Variables can CHANGE type! Python vs Static Typing: # Static (C++/Java): int x = 25;     // Type fixed x = 13.75;      // ❌ Error! # Dynamic (Python): x = 25          // Type inferred x = 13.75       // ✅ OK! Type changed Key insights: Variables are references to values Type is determined at runtime Everything in Python is an object Use type() to check types I wrote a guide covering everything about Python's dynamic typing. Read it here: https://lnkd.in/gW5F2TkQ What's your experience with dynamic typing? 💭 #Python #PythonProgramming #Programming #Coding #LearnPython #PythonBasics #DynamicTyping #TechBlog

Hi! Want to analyze data? Good news: Python is the leading language in the data world. Libraries like NumPy and Pandas make it easy to load, clean, analyze, and visualize your data. But wait: If your colleagues aren't coders, how can they explore your data? The answer: A data dashboard, which uses UI elements (e.g., sliders, text fields, and checkboxes). Your colleagues get a custom, dynamic app, rather than static graphs, charts, and tables. One of the newest and hottest ways to create a data dashboard in Python is Marimo. Among other things, Marimo offers UI widgets, real-time updating, and easy distribution. This makes it a great choice for creating a data dashboard. In the upcoming (4th) cohort of HOPPy (Hands-On Projects in Python), you'll learn to create a data dashboard. You'll make all of the important decisions, from the data set to the design. But you'll do it all under my personal mentorship, along with a small community of other learners. The course starts on Sunday, February 1st, and will meet every Sunday for eight weeks. When you're done, you'll have a dashboard you can share with colleagues, or just add to your personal portfolio. If you've taken Python courses, but want to sink your teeth into a real-world project, then HOPPy is for you. Among other things: Go beyond classroom learning: You'll learn by doing, creating your own personal product Live instruction: Our cohort will meet, live, for two hours every Sunday to discuss problems you've had and provide feedback. You decide what to do: This isn't a class in which the instructor dictates what you'll create. You can choose whatever data set you want. But I'll be there to support and advise you every step of the way. Learn about Marimo: Get experience with one of the hottest new Python technologies. Learn about modern distribution: Use Molab and WASM to share your dashboard with others Want to learn more? Join me for an info session on Monday, January 26th. You can register here: https://lnkd.in/dC9crid8 Ready to join right now? Get full details, and sign up, at https://lnkd.in/di6QpzXT

GILs aren't just for fish. 🐟 Sometimes they're for snakes too. 🐍 Python's Global Interpreter Lock prevents parallelism. Python is slow. Python is single-threaded. So why does Python dominate big data? 🔻 Machine learning? PyTorch, TensorFlow (Python) 🔻 Data analysis? pandas, NumPy (Python) 🔻 Big data pipelines? PySpark, Dask (Python) This makes no sense! 😶🌫️ Big data demands massive parallelism, yet Python's GIL prevents exactly that. Here's the uncomfortable truth: Python doesn't process your data. NumPy, pandas, Polars, and PySpark do - and they don't have the GIL's limitations. When you write df.groupby('category').sum(), you're not running Python loops. You're calling optimized C/Rust code that releases the GIL and runs across all your CPU cores in parallel. 🗂️ What's inside: 🔹 How the GIL works (and why it exists) 🔹 The orchestration layer pattern 🔹 How NumPy, pandas, and Polars bypass the GIL 🔹 Fanout-on-write vs fanout-on-read strategies 🔹 When the GIL actually matters (and workarounds) 🔹 Python 3.13's experimental no-GIL mode  The pattern is simple: Python coordinates. C/Rust/JVM executes.  This isn't a workaround, it's architectural brilliance.  📚 Read the full article: https://lnkd.in/gWRuqg74  ❔ Have you encountered GIL-related performance issues in your Python projects? How did you solve them? ❔  #Python #DataEngineering #BigData #SoftwareEngineering #GIL #Performance #NumPy #Pandas #PySpark

🚀 Python Tip: Using default_factory in Dataclasses While working on a data quality framework in Python, I encountered an interesting scenario with timestamps. I wanted every new instance of my dataclass to have a fresh, current timestamp. At first, I tried this: from dataclasses import dataclass, field from datetime import datetime, timezone @dataclass class QualityCheckResult:   timestamp: datetime = datetime.now(timezone.utc) # ❌ The problem? Every instance got the same timestamp — Python evaluated it once when the class was defined. Not what I wanted! The solution: default_factory @dataclass class QualityCheckResult:   timestamp: datetime = field(default_factory=lambda: datetime.now(timezone.utc)) # ✅ Why this works: default_factory expects a callable (like a lambda or function) Python stores the callable, but doesn’t run it immediately Every time you create a new object, Python calls the lambda, producing a fresh timestamp 💡 Think of it as keeping a “recipe” instead of the finished product. Each object gets its own freshly baked “timestamp” instead of reusing the same one. This small tweak solves a subtle bug and ensures my data quality logs always reflect the exact creation time. Python’s dataclasses + default_factory = cleaner, bug-free defaults! ⚡

"Python Mini-Series Wrap-Up: What writing production-ready Python really looks like" Over the last few posts, I shared a short Python mini-series focused on how Python is actually used in analytics and data engineering — beyond tutorials and toy examples. The core idea across the series was simple: Python becomes valuable when it’s structured, trusted, and built to scale. Here’s what I covered: • Post 1 – Structure: Treat Python work like a pipeline, not a one-off notebook • Post 2 – Unstructured data: Turning PDFs and messy text into structured datasets with regex • Post 3 – Trust: Making data quality a first-class citizen through validation and checks • Post 4 – Scale: Writing faster, more memory-efficient code with vectorization and smart data types • Post 5 – Maturity: Early mistakes that taught me why reproducibility and structure matter None of this is flashy — and that’s the point. These are the habits that turn Python scripts into workflows teams can rely on, and analyses into outputs stakeholders actually trust. If you’re early in your data career, you don’t need advanced tricks to stand out. Focus on writing Python that is: ✔ reproducible ✔ configurable ✔ readable by someone else ✔ safe to run more than once That’s what moves your work closer to production. I’ll be shifting next into SQL, using the same practical, real-world lens. 👉 Follow along — more coming soon.

🚀 How Python Handles Data Better Than I Expected (Python Learning Journey - Day 17) When I started learning Python, I thought data was just numbers and text. Store it. Use it. Move on. But Python showed me there’s more depth to it. 👉 How data is stored matters 👉 How data is accessed matters 👉 How data is structured changes everything That realization came slowly. 🌿 What Python Taught Me About Data Python doesn’t treat data as raw values. It treats data as meaning. Lists group related items. Tuples protect fixed information. Dictionaries explain data through keys. Each structure exists for a reason. Each one communicates intent. Instead of forcing one approach everywhere, Python asks you to choose wisely. What kind of data is this? Will it change? Does it need a name? That question-first approach changed my mindset. ✔️ Data isn’t just stored → it’s designed ✔️ Structure affects clarity ✔️ Clear data leads to clear logic Once I respected data structures, my code felt calmer. Fewer guesses. Fewer errors. More confidence. 🙌 Why It Matters Most problems are data problems at their core. If data is messy, logic becomes messy. If data is clear, solutions appear faster. This lesson goes beyond Python. How we organize information shapes how we think. Python didn’t just teach me syntax. It taught me to respect data. 🔗 Now Your Turn When solving problems, do you think first about the data or the logic? #PythonLearning #Day17 #DeveloperJourney #Python #CodingMindset #DataHandling

Today’s Python focus was 𝗗𝗶𝗰𝘁𝗶𝗼𝗻𝗮𝗿𝗶𝗲𝘀 and 𝗧𝘂𝗽𝗹𝗲𝘀. I spent time understanding how Python handles structured data using key value pairs and fixed collections, and how this differs from lists. 𝗪𝗵𝗮𝘁 𝗜 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝗱 𝘁𝗼𝗱𝗮𝘆: • Creating dictionaries to store related data using meaningful keys • Accessing values using keys and using get() to avoid runtime errors • Updating existing values and adding new key value pairs • Deleting entries and checking for key existence • Iterating through dictionaries using keys and items() • Extracting only keys and only values when needed • Working with nested dictionaries to represent structured data • Iterating through nested dictionaries for multi level data • Using dictionaries to model real examples like contact details and revenue by region 𝗞𝗲𝘆 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀: • Dictionaries store data as key value pairs, making lookups fast and clear • Dictionaries are mutable, so values can be updated without recreating the structure • get() is safer than direct key access when keys may not exist • Nested dictionaries are useful for representing hierarchical data • Iterating through dictionaries helps process structured datasets efficiently I also revisited 𝘁𝘂𝗽𝗹𝗲𝘀 conceptually and understood where they fit: • Tuples are ordered and immutable • They are useful when data should not change • Often used for fixed records, configuration values, or safe data grouping Working with dictionaries made it clear how real world data like contacts, configurations, and reports are represented in Python. If you are learning Python as well, which data structure are you currently focusing on? #Python #PythonLearning #DictionariesInPython #TuplesInPython #ProgrammingBasics #LearningInPublic #DataAnalytics #Upskilling