Python Database for Trading and Investment Projects

🚀 Simple Python Project: Contact Feeding Record Book I recently worked on a small project to strengthen my fundamentals in Python and database handling. 📌 Project Overview: Developed a Contact Feeding Record Book using Python, PyMySQL, and dictionary data structures. This application allows users to store, manage, and retrieve contact records efficiently. 🔧 Key Features: • Add and store contact details • Update and delete records • Retrieve contact information quickly • Integration with MySQL database using PyMySQL • Use of Python dictionaries for structured data handling 💡 What I Learned: This project helped me understand how Python interacts with databases, improved my CRUD operation skills, and reinforced the use of dictionaries for data organization. 🎯 A simple project, but a solid step toward building more advanced database-driven applications. #Python #MySQL #PyMySQL #BeginnerProject #Programming #SoftwareDevelopment #LearningJourney

🐍🔖 Python Database Tutorials — This section contains all of our tutorials that are related to working with databases in Python https://lnkd.in/gcDxzS6

Machine Learning Graph Data using python igraph #machinelearning #datascience #graphdata #pythonigraph igraph is a fast open source tool to manipulate and analyze graphs or networks. It is primarily written in C. python-igraph is igraph’s interface for the Python programming language. python-graph includes functionality for graph plotting and conversion from/to networkx. Python interface of igraph, a fast and open source C library to manipulate and analyze graphs (aka networks). It can be used to: Create, manipulate, and analyze networks. Convert graphs from/to networkx, graph-tool and many file formats. Plot networks using Cairo, matplotlib, and plotly. https://lnkd.in/gzzzK7eU

Storing 10,000 Numbers in Python — List vs Generator shows a surprising memory difference. When working with large datasets in Python, choosing the right data structure can directly impact memory efficiency and performance. I compared memory usage between a List and a Generator while storing the same range of numbers using sys.getsizeof(). Here is what happens behind the scenes: A List stores all values in memory at once. - When we create a list using list comprehension, Python generates and stores every element immediately. This makes data easily accessible but increases memory consumption. A Generator works differently. - Instead of storing all values, it produces elements one at a time only when required. This concept is called lazy evaluation, which helps reduce memory usage significantly. Observations: • Lists store complete data in memory. • Generators generate values only when needed. • Memory difference becomes huge as dataset size increases. Understanding this helps in writing memory-efficient and scalable Python applications. Note: Memory values may vary depending on system architecture and Python version. (Outpput in bytes)

As a long-time Java engineer, I continue to be impressed by how much Python has evolved. What once felt like a simple scripting language has grown into a remarkably capable ecosystem: C-backed libraries like NumPy, performance-oriented tooling in Rust, native coroutine support with async and await, and multiple concurrency models for very different workloads. One thing I find especially interesting is Python’s concurrency toolbox. Choosing the right model usually comes down to one question: What is your code actually waiting on? If your program is mostly waiting on the network, a database, or disk, you are likely dealing with an I/O-bound problem. In that case, asyncio can be a strong fit when the surrounding stack is async-native. If your program spends most of its time computing, parsing, or transforming data, you are likely dealing with a CPU-bound problem. In standard CPython, threads usually do not speed up pure Python CPU work because of the GIL. For that, multiprocessing is often the better fit. A few practical rules I keep in mind: • asyncio for high-concurrency I/O with async-native libraries • threads for blocking libraries or simpler concurrency • multiprocessing for CPU-heavy pure Python workloads • threads with native libraries when heavy work runs in C or Rust A good example is PyArrow and PyIceberg. PyArrow’s Parquet reader supports multi-threaded reads. That means you can get parallelism without rewriting everything around asyncio, because the heavy work happens in native code rather than Python bytecode. PyIceberg builds on this ecosystem. From the Python caller’s point of view, the workflow is still synchronous, while file access and data processing can benefit from native parallelism underneath. The key lesson for me: Not every high-performance I/O workflow in Python needs asyncio. If the underlying engine is native and already parallelizes efficiently, threads can be the right tool. If the stack is async-native, asyncio becomes much more compelling. A simple mental model: Async-native I/O → asyncio Native libraries parallelizing outside Python → threads CPU-heavy pure Python → multiprocessing Not sure → profile first That mindset is often more useful than memorizing any specific framework. #Python #Java #Concurrency #AsyncIO #Threading #Multiprocessing #Performance #SoftwareEngineering #DataEngineering

DAY 2 of Python- DATA TYPES(Data Structures) 1. We can say, Data types in python are its data structures (entities that store a collection of data). 2. In Python, we have, Strings, Lists, Tuples, Sets, Dictionaries. You can try google their definition. 3. On such a data structure you can always perform operations using different functions. But as every data structure is different and has its own properties such as mutable, immutable, ordered, or unordered, it is mandatory to remember the exceptional cases of performing any operation on a data structure. Some exceptions are mentioned below: a. Immutable Data Structures Tuples (Python): You cannot modify, append, or remove elements once created. Strings: Strings are immutable; concatenation creates a new string instead of modifying the original. b. Unordered Data Structures Sets: Do not support indexing or slicing because they are unordered. Dictionaries (before Python 3.7):Order of keys was not guaranteed, so relying on index-based access could lead to unpredictable results. Key Constraints in Dictionaries: Keys must be hashable (immutable types like strings, numbers, tuples without mutable elements). Lists / Tuples: Accessing an index that does not exist raises an IndexError. Modifying a list or dictionary while iterating over it can cause unexpected behavior or runtime errors.

Understanding Python Encapsulation Encapsulation is a fundamental concept in object-oriented programming that restricts direct access to certain attributes or methods. In Python, this is achieved using private attributes, which are designated by a preceding double underscore (e.g., `__balance`). This convention indicates that the attribute should not be accessible outside the class, promoting data hiding and ensuring better control over how the data can be modified. In the provided code, the `BankAccount` class demonstrates encapsulation. The `__balance` attribute is a private variable, ensuring that it cannot be accessed directly from outside the class. Instead, public methods like `get_balance()`, `deposit()`, and `withdraw()` are provided to interact with this private variable safely. This structure helps to validate inputs and maintain integrity, as any changes to the balance must go through these methods, which can enforce rules like not allowing negative deposits or withdrawals exceeding the current balance. This becomes critical when managing sensitive data, such as financial information in the example. By masking the underlying implementation details, encapsulation allows you to change the internal workings of a class without affecting code that uses the class. This flexibility adds to the robustness and maintainability of your code. Quick challenge: How would you modify the `BankAccount` class to include a method that prevents the balance from going below zero? #WhatImReadingToday #Python #PythonProgramming #OOP #Encapsulation #Programming

Stop writing Python like Java/C++! Building scalable applications in Python means embracing its unique strengths, not fighting them. A truly "clean" API in Python isn't just about naming conventions; it's about thinking in terms of Python's object model, its dynamic nature, and its emphasis on readability. Let's look at how we handle optional parameters. Okay: class Service: def process(self, data, config=None): if config is None: config = {} # Boilerplate to handle None # ... process with data and config Best (Pythonic): class Service: def process(self, data, config=None): config = config or {} # Concise and idiomatic # ... process with data and config The "Best" version uses Python's truthiness. None evaluates to False, so config or {} will assign an empty dictionary if config is None, otherwise it uses the provided config. It's shorter, clearer, and less prone to errors. Takeaway: Design APIs that leverage Python's expressiveness for clarity and conciseness. #Python #CodingTips

🚀 Python Learning Journey – Day 3 📅 13/03/2026 Continuing my Python learning journey at Global Quest Technologies, today’s session focused on exploring Python implementations and understanding how it compares with Java. 🔍 Topics Covered: 🔹 Flavours of Python 🔹 Differences between Python and Java 💡 Key Takeaways: ✔️ Learned about different Python implementations: • CPython – Default and most widely used • Jython – Runs on the Java Virtual Machine (JVM) • IronPython – Works with the .NET framework • PyPy – Focused on performance using JIT compilation. ✔️ Understood key differences between Python and Java : 🔹 Python is interpreted and dynamically typed, while Java is compiled and statically typed 🔹 Python emphasizes simplicity and readability, whereas Java focuses on structure and performance 🔹 Python uses indentation, while Java uses braces {} 🔹 Java generally offers faster execution, while Python provides ease of development and debugging. This session helped me gain clarity on how Python works across different environments and how it stands in comparison with Java in real-world applications. Grateful for the continuous guidance from Global Quest Technologies and G.R NARENDRA REDDY Sir. Looking forward to applying these concepts and exploring more advanced topics ahead! #Python #LearningJourney #CorePython #Programming #Java #TechSkills #Upskilling #FullStackDevelopment 🚀

Automate Excel Tasks with Python for Efficiency Boost your Excel productivity with Python. Automate repetitive tasks like data cleaning, merging files, and generating reports. Save time and minimize errors....