Building a Simple SQLite-like Engine in Python

Your Python Code Consuming Too Much Memory? Today, I explored a fundamental concept in NumPy that many of us often overlook: manual data type (dtype) . While NumPy is naturally more efficient than standard Python arrays, the way we define our data plays a massive role in actual performance. I recently followed a lecture by Respected Sir Zafar Iqbal on this topic, and it changed how I look at memory management in Data Science/ML. Here are my three key takeaways from today's practice: 1. The "Default" Memory Waste When we create an array without specifying a data type, Python often assigns the maximum possible size, such as int64, by default. If your data consists of small numbers (like 1 to 100), using int64 is a waste of resources. By simply defining dtype=np.int8, you can perform the same operations while using significantly less memory. 2. The Out-of-Bounds Trap Every data type has a specific boundary. For instance, int8 can only store values between -128 and 127. If you try to store a number like 130 in an int8 array, you will encounter an "out of bounds" error. In such cases, moving to int16 or int32 provides the necessary range while still being more efficient than the 64-bit default. 3. The Cost of "Object" Flexibility NumPy allows us to mix different types, like strings, integers, and floats, by using dtype=object. While this offers flexibility, it comes at a price: you lose the famous speed advantage that makes NumPy so powerful. For high-performance computing, keeping your data homogeneous is essential. Pro Tip: When working with large datasets, always use the .nbytes attribute to check exactly how much memory your array is consuming. Making small adjustments to your data types can transform a heavy, slow program into a super-efficient one. I am curious to hear from other data professionals: Do you usually stick with the default settings, or do you prefer manual control over your memory usage? Let me know in the comments. #Python #DataScience #NumPy #CodingLife #LearningEveryday #MachineLearning #Efficiency

The biggest friction in learning SQL and Python isn't the concepts — it's the setup. Installing databases, configuring environments, debugging Docker containers, troubleshooting driver conflicts. Most beginners spend more time setting up than actually practicing. That's the problem In-Browser Practice on Let's Data Science eliminates entirely. 1,584 SQL and Python coding challenges run directly in your browser. Write your query or script, hit run, and get graded in milliseconds. No local installation, no environment configuration, no "it works on my machine" issues. What makes this different from a generic code playground: → 15 real industry datasets modeled after companies like Amazon, Google, Meta, Netflix, and LinkedIn — not contrived textbook examples → 4 difficulty levels from Easy to Expert, so you can progress at your own pace → Problems tagged by company name, letting you practice the exact style of questions asked at specific employers → Instant automated grading that checks your output against expected results — not just "does it run," but "is it correct" Whether you're preparing for a technical interview next week or building SQL fluency from scratch, the ability to open a browser tab and immediately start solving real-world problems removes every excuse between you and practice. Try any problem — many are free to attempt: https://lnkd.in/gYW7SyFH #DataScience #SQL #Python #LetsDataScience

DERA has published a set of Python code examples to make it easier for analysts, researchers, and developers to access and work with the SEC’s XBRL Financial Statement and Notes Data Sets: https://lnkd.in/gpWuXJZD   The GitHub repository walks through:  • Reading quarterly data into Pandas • Joining and analyzing numeric, dimensional, narrative, and custom facts • Visualizing results • Working with multiple datasets and exporting outputs   Code, notebooks, and setup instructions are all available in the link.

Great foundation from the SEC DERA team. I was able to modernize this in an afternoon, swapping Pandas for Polars with lazy evaluation, adding DuckDB for direct SQL queries on the TSV files, and a benchmark showing the speed difference on real XBRL data. 4.20.2026 1900 PST: This update improves integration with external data pipelines./Notes and R-based incremental downloader + DuckDB/Parquet workflow, which served as a strong reference point for data ingestion design patterns. Fork with improvements here: https://lnkd.in/g58ESerZ Happy to contribute anything back if useful. #Code #SEC #finance #data #AI #trading #Stockmarket #SQL #XBRL #fullstack #financialservices

While preparing a risk management module, I came across a very useful resource that deserves more visibility. This repository by the U.S. Securities and Exchange Commission (SEC) provides Python-based tools to work with structured financial datasets derived from company filings. What makes it valuable? Access to SEC Financial Statement datasets Structured data extracted from XBRL filings Ready-to-use Python workflows using Pandas Ideal for financial modeling, empirical research, and analytics For anyone working on financial research, sustainability reporting, valuation, or data-driven finance projects, this can significantly reduce the effort required to clean and structure raw filings. #Finance #FinancialModeling #DataAnalytics #Python #Research #SEC #XBRL #FinTech #OpenData

Good release from DERA. The broader point is not just access to data. It is making public market information more usable, more scalable, and easier to work with in modern analytical workflows. That is how transparency starts to compound.

Check it out: Automate Peer Benchmarking: Instantly extract and compare financial KPIs across entire industries to see how competitors stack up without manual data entry. Uncover Footnote Insights: Search thousands of narrative disclosures simultaneously to flag "hidden" risks like litigation, supply chain shifts, or aggressive accounting. Build Data-Driven Dashboards: Transform raw SEC filings into clean, visual trends to identify long-term sector shifts and high-growth opportunities.

🧠 Python Concept: dataclasses (Clean Data Models) Write less boilerplate code 😎 ❌ Traditional Class class User: def __init__(self, name, age): self.name = name self.age = age def __repr__(self): return f"User(name={self.name}, age={self.age})" 👉 More boilerplate 👉 Repetitive code ✅ Pythonic Way (dataclass) from dataclasses import dataclass @dataclass class User: name: str age: int 👉 Automatically generates: __init__ __repr__ __eq__ 🧒 Simple Explanation Think of it like a shortcut ➡️ You define data ➡️ Python builds the rest 💡 Why This Matters ✔ Cleaner code ✔ Less boilerplate ✔ Easier to maintain ✔ Used in real-world apps ⚡ Bonus Example @dataclass class User: name: str age: int = 18 👉 Default values supported 😎 🧠 Real-World Use ✨ API models ✨ Config objects ✨ Data handling 🐍 Write less code 🐍 Let Python do the work #Python #AdvancedPython #CleanCode #SoftwareEngineering #BackendDevelopment #Programming #DeveloperLife

Day 10/365: Building a List from User Input & Finding Basic Stats 🔢📥 Today I wrote a Python program that takes numbers from the user, stores them in a list, and then calculates some basic statistics: sum, average, minimum, and maximum. What the code does step by step: First, I ask the user how many elements they want to enter and store that in n. I create an empty list l and a variable total to keep track of the sum. Using a for loop, I take n inputs from the user: Each number is added to the list using append(). At the same time, I keep adding each number to total to calculate the sum. After the loop: I print the full list. I print the sum using the total variable. Then I calculate the average as total / n and print it. To find the minimum and maximum: I start by assuming both min and max are the first element of the list. I loop through the list and update min if I find a smaller value. Similarly, I update max if I find a larger value. In the end, I print the minimum and maximum numbers in the list. What I learned from this exercise: How to take multiple inputs from a user and store them in a list. How to maintain a running sum while taking inputs. How to manually compute average, minimum, and maximum without using built‑in functions like sum(), min(), or max(). How loops and variables can work together to build simple but useful statistics — a basic idea used a lot in data analysis. Day 10 done ✅ 355 more to go. If you have ideas like extending this to find median, mode, or standard deviation, send them to me — I’d love to try them next. #100DaysOfCode #365DaysOfCode #Python #LogicBuilding #Lists #UserInput #CodingJourney #LearnInPublic #AspiringDeveloper

🧠 Python Concept: contextlib (Custom Context Managers) Write your own with logic 😎 ❌ Without Context Manager file = open("data.txt", "w") try: file.write("Hello") finally: file.close() 👉 More boilerplate 👉 Easy to forget cleanup ✅ Pythonic Way (Custom Context Manager) from contextlib import contextmanager @contextmanager def open_file(name, mode): f = open(name, mode) try: yield f finally: f.close() with open_file("data.txt", "w") as f: f.write("Hello") 🧒 Simple Explanation Think of it like a helper 🤖 ➡️ Handles setup ➡️ Runs your code ➡️ Cleans up automatically 💡 Why This Matters ✔ Cleaner resource handling ✔ Avoid memory leaks ✔ Reusable logic ✔ Used in production systems ⚡ Real-World Use ✨ Database connections ✨ File handling ✨ API sessions ✨ Locks & threading 🐍 Don’t repeat try-finally 🐍 Automate cleanup smartly #Python #PythonTips #CleanCode #AdvancedPython #BackendDevelopment #Programming #DeveloperLife