An iterable is any object that can return an iterator when you call iter() on it. It must implement the iter() method. for example a list a dictionary is iterable.an iterator is an object that produces elements one at a time using the next() method. it must implement both iter and next methods. it = … Continue reading Iterator and Generator

Grafana is an open-source visualization and monitoring tool — it turns logs, metrics, and time-series data into dashboards and alerts. We can use it to see how the system is performing its work properly. Let's build a IndexCalc → QC → Grafana monitoring pipeline step-by-step to illustrate potential of using it in our workflow. First … Continue reading Thoughts on Using Grafana

The Command pattern intuitively works like placing a takeout order 🥡 at a restaurant. It's all about separating the person who asks for the food from the chef who makes it, using a ticket (the Command object) as the medium. So there is the client, the command/order ticket, invoker/order taker and receiver/chef. the Command pattern … Continue reading Patterns in Finance and Indexing 02

First, the adapter pattern should be implemented to standardize the data feed intake. It is crucial to establish a uniform method, such as data_provider.get_price("AAPL"), to accommodate the various API formats provided by different vendors. class BloombergAPI: def getField(self, ticker, field): return f"Bloomberg {ticker}:{field}=185.5" class FactSetAPI: def get_price(self, ticker): return f"FactSet {ticker}=185.5" # Unified interface class … Continue reading Patterns in Finance and Indexing 01

From procedural → class-based → design-pattern-level architecture, developing a library system serves as a profound method to implement software engineering principles. Jumping to stage 2 that is class-based as the following, note the three classes are too tightly coupled, not an ideal practice:class Book: def __init__(self, title, author): self.title = title self.author = author self.is_borrowed … Continue reading Building a Library System to Apply Software Engineer Thinking

The Interpreter Pattern: Defining a Language The Interpreter pattern is used to define a grammar for simple languages and implement an interpreter to process sentences in that language. It's ideal for domain-specific languages (DSLs), mathematical expressions, or simple configuration rules. How it Works: The grammar is defined using a class hierarchy where each rule or … Continue reading Python Literacy Continue 3

Metaclasses: The Class of a Class ✨ A metaclass is the entity that creates a class—it is literally the "class of a class." Metaclasses allow you to define rules or inject behavior into classes as they are being built. Rule Enforcement: Metaclasses are used to ensure rules on a class are followed (e.g., ensuring all … Continue reading Python Literacy Continue 2

This blog post summary covers several key object-oriented programming (OOP) and design principles, with a focus on Python implementation and idiom. Class Construction and Separation of Concerns Internal vs. External Data: When constructing a class, data passed as arguments from the user (like initial values) are external. Internal operational data, such as a running count … Continue reading Python Literacy Continue 1

In the age of AI, code literacy has become what mathematical literacy once was: the essential tool for expressing, testing, and sharing thought. Understanding how to structure code — not just make it run — is how modern scientists, engineers, and creators communicate ideas that scale beyond themselves. Python, being expressive and readable, is the … Continue reading Notes Summery on Python Literacy

Summary: Top 18 Advanced Python Concepts #ConceptLocationWhy Important1Async Context Managerclient.py:55-78Resource lifecycle2Dataclass field()types.py:37Mutable defaults3post_inittypes.py:132Auto-validation4Enum(str, Enum)types.py:17JSON-serializable enums5Union (AB)client.py:366isinstance type guardsclient.py:144Type narrowing7Literal typestypes.py:84Exact value constraints8Callable/Awaitabletypes.py:90Async callback types9Private methods (_)client.py:198API encapsulation10Defensive copyclient.py:333Prevent mutation11Error-as-valueclient.py:141-192Railway-oriented design12@staticmethodengine.py:20Pure functions13dict.get()engine.py:84Safe access14perf_counter()client.py:139High-precision timing15Observer patternclient.py:198-239Hook system16Module structureinit.pySeparation of concerns17Relative importsclient.py:12Package portability18Builder patterntypes.py:104Flexible configuration One by one analysis: First Async Context Manager for example class CalculatorClient: def … Continue reading Advanced Python Features