#Python
130 articles tagged with #Python
Learn Python Through Cricket: Your Ultimate Beginner's Guide
Discover how cricket can help you understand Python programming in the most exciting way.
Learn Loops in Python by Building a Cricket Scoreboard
A comprehensive guide to learn loops in python by building a cricket scoreboard — written for learners at every level.
Learn Pandas by Analyzing Virat Kohli's Career Stats
A comprehensive guide to learn pandas by analyzing virat kohli's career stats — written for learners at every level.
Python for Beginners: A Complete 2026 Roadmap
A comprehensive guide to python for beginners: a complete 2026 roadmap — written for learners at every level.
How to Install Python and Set Up VS Code (Step by Step)
A comprehensive guide to how to install python and set up vs code (step by step) — written for learners at every level.
Top 20 Python Projects for Beginners to Build a Portfolio
A comprehensive guide to top 20 python projects for beginners to build a portfolio — written for learners at every level.
Object-Oriented Programming in Python Explained Simply
A comprehensive guide to object-oriented programming in python explained simply — written for learners at every level.
Python Error Handling: try, except, finally Made Simple
A comprehensive guide to python error handling: try, except, finally made simple — written for learners at every level.
Pandas for Beginners: A Complete Tutorial
A comprehensive guide to pandas for beginners: a complete tutorial — written for learners at every level.
10 Python Projects to Build From Beginner to Advanced
A comprehensive guide to 10 python projects to build from beginner to advanced — written for learners at every level.
From Cricket Fan to Python Developer: A Learner's Journey
A comprehensive guide to from cricket fan to python developer: a learner's journey — written for learners at every level.
How to Become an AI Engineer (Roadmap 2026)
A clear, step-by-step roadmap from Python foundations to deploying AI systems in production.
Python Functions Explained for Beginners
Functions are named, reusable blocks of code — learn to define them, pass arguments, and return values.
Python Interview Questions and Answers (2026 Edition)
Python interviews cluster around fundamentals, data structures, OOP, and gotchas — this guide prepares you for all of them.
Project: Build a REST API with Python and FastAPI
FastAPI is the fastest-growing Python web framework — and for good reason. In this hands-on project you'll build a fully functional REST API with auto-generated documentation, database persistence, and deployment on Render, all in a single afternoon.
Project: Build a Data Dashboard with Python and Streamlit
Streamlit turns a Python script into an interactive web app in minutes — no frontend knowledge required. In this project you'll build a live sales dashboard with filters, KPI metrics, and Plotly charts from a CSV dataset, then share it online for free.
Learn Python Through Cricket Statistics
Cricket generates rich data — runs, wickets, overs, strike rates, economy rates. This project uses real IPL-style match data to teach you pandas, matplotlib, and data analysis in a context that actually interests you. No dry tutorials — just cricket and code.
RAG Explained: Retrieval-Augmented Generation
RAG is how you give an LLM access to your own private data without training a new model. This guide explains the full pipeline — chunking, embeddings, vector search, and augmented generation — with a working Python example using open-source tools.
The 2026 AI Engineer Roadmap: Skills, Tools, and Career Path
AI Engineer is one of the fastest-growing roles in tech — and it's more accessible than traditional ML engineering. This guide maps the exact skills, tools, and learning sequence for becoming an AI engineer in 2026, from Python basics to deploying production RAG and agent systems.
Object-Oriented Programming in Python: A Practical Guide
OOP is how Python codebases stay organised as they grow. This guide explains classes, inheritance, encapsulation, and polymorphism with real examples — and tells you honestly when to use OOP and when plain functions are the better choice.
Async Python: asyncio Explained for Beginners
Async Python lets a single thread handle hundreds of concurrent I/O operations — making it essential for web APIs, database calls, and AI integrations. This guide explains coroutines, the event loop, await, gather, and real patterns you'll use in FastAPI, httpx, and LLM streaming.
Python Decorators: A Practical Guide for Beginners
Decorators are one of Python's most powerful features — they let you wrap functions with reusable logic without modifying the original. This guide explains how they work from first principles, builds several practical decorators (timing, caching, authentication), and covers class-based decorators and decorator factories.
From Cricket Fan to Python Developer: An Illustrative Learning Journey
This is a composite illustrative journey — based on the real paths taken by many self- taught developers — showing how a passionate cricket fan used IPL data to learn Python, pandas, and data visualisation, and landed a data analyst role in 8 months.
Python Error Handling: try, except, finally Explained
Errors are inevitable; crashes are not. This guide explains Python's exception system from first principles: how try/except/finally works, which exceptions to catch (and which to let propagate), how to raise your own exceptions, and how to write error handling that helps debugging rather than hiding bugs.
Python Virtual Environments: venv, conda, and poetry Explained
Installing packages globally is fine until it isn't — then you have version conflicts, broken projects, and chaos. This guide explains virtual environments from first principles and shows you how to use venv, pip, poetry, and conda to keep your projects isolated and reproducible.
Python List Comprehensions Made Easy
List comprehensions are one of Python's most beloved features — they let you create lists with concise, readable one-liners instead of multi-line for loops. This guide explains the syntax, filtering, nesting, dict and set comprehensions, and when to use (and avoid) them.
Testing Python Code with pytest: A Beginner's Guide
Untested code is legacy code from the moment it's written. This guide explains how to write effective Python tests with pytest — from your first test function through fixtures, parametrize, mocking, and measuring coverage.
From Teacher to Data Analyst: An Illustrative 8-Month Transition
This composite illustrative story follows how a secondary school maths teacher used her existing analytical skills to transition into a data analyst role — starting with Excel, moving to SQL and Python, and landing her first data role in 8 months.
Regular Expressions in Python: A Practical Guide
Regular expressions are one of the most powerful text-processing tools in programming — and one of the most avoided, because the syntax looks intimidating. This guide demystifies regex by building from first principles, with real patterns for emails, phone numbers, dates, and log parsing.
Python File I/O: Reading and Writing Files
Almost every real Python program reads or writes files — logs, configs, CSVs, JSON, reports. This guide covers text files, CSV, JSON, binary files, and the modern pathlib approach, with best practices for safe file handling.
NumPy for Data Science: Arrays and Vectorisation
NumPy is the foundation of Python's scientific computing stack. This guide covers ndarrays, vectorised operations, broadcasting, linear algebra, and why NumPy is 10-100x faster than equivalent Python loops — with practical examples for data science work.
Learn Data Science Through Bollywood Box Office Analytics
Bollywood produces hundreds of films a year and generates rich box office data. This project uses real film data to teach pandas groupby, matplotlib charting, correlation analysis, and time-series trends in a context that film fans genuinely find interesting.
Matplotlib and Seaborn: Data Visualisation in Python
The best data insight is worthless if no one understands the chart. This guide covers matplotlib's core API, Seaborn's statistical plots, best practices for clear design, and how to produce publication-quality figures — from first plot to polished dashboard chart.
Scikit-Learn for Beginners: Machine Learning in Python
Scikit-learn is the most widely used Python library for classical machine learning. This guide covers the fit-predict workflow, train/test splits, classification, regression, model evaluation, feature engineering, and pipelines — everything you need to build and evaluate your first ML models.
Python for Beginners: A Complete 2026 Roadmap
A clear, step-by-step Python roadmap for absolute beginners in 2026 covering setup, core syntax, projects, and the fastest path from zero to job-ready skills.
Python Decorators Explained With Examples
A Python decorator is a function that wraps another function to add behavior without changing its code. Learn how they work and when to reach for them.
Pandas for Data Analysis: A Complete Guide
Pandas is the Python library for working with tabular data. Learn DataFrames, selection, cleaning, grouping, and joins to analyze real datasets with confidence.
NumPy for Beginners: The Foundation of Data Science
NumPy powers Python's entire data science stack with fast numerical arrays. Learn arrays, vectorization, broadcasting, and indexing to compute at scale with clean code.
Getting Started With scikit-learn
scikit-learn is the standard Python library for classic machine learning. Learn its consistent API, core workflow, and how to train your first model correctly.
Data Visualization With Matplotlib: A Practical Guide
Matplotlib is Python's foundational plotting library. Learn its figure-and-axes model, core chart types, and styling to turn raw data into clear visuals.
Python List Comprehensions Explained With Examples
A Python list comprehension builds a list in one readable line: [expression for item in iterable if condition]. Learn the syntax, examples, and when to use it.
Python Decorators Made Simple for Beginners
A Python decorator is a function that wraps another function to add behavior without changing its code. Learn how the @ syntax works with clear beginner examples.
Understanding Python Generators and Yield
Python generators produce values lazily with yield instead of return, so you can process huge or infinite sequences without loading everything into memory at once.
Async and Await in Python Explained
Async and await let Python run many I/O-bound tasks concurrently on one thread by pausing coroutines while they wait, so your program stays busy instead of blocking.
Python Virtual Environments: A Complete Guide
A Python virtual environment is an isolated folder of packages for one project, so dependencies never clash between projects or with your system Python installation.
NumPy for Beginners: A Complete Tutorial
NumPy is Python's core library for fast numerical computing, built around the ndarray. Learn arrays, indexing, broadcasting, and vectorization in this beginner tutorial.
Data Cleaning in Python: A Practical Guide
Data cleaning fixes missing values, duplicates, wrong types, and outliers so analysis is trustworthy. This practical guide walks through the process with pandas.
How to Perform Exploratory Data Analysis in Python
Exploratory data analysis (EDA) summarizes and visualizes a dataset to understand its structure before modeling. Learn a repeatable EDA workflow with pandas.
How to Handle Missing Data in a Dataset
Handle missing data by first understanding why it is missing, then choosing to delete or impute. This guide covers the methods and the pitfalls with pandas.
Build a Chatbot With Python: Step-by-Step
Build a chatbot with Python by choosing rule-based or LLM-powered logic, handling user input in a loop, and connecting an API like OpenAI for real conversations.
Python Dictionaries Explained With Examples
A Python dictionary stores data as key-value pairs for instant lookups by key. Learn how to create, access, update, and loop through dictionaries with clear examples.
Python Sets and When to Use Them
A Python set is an unordered collection of unique items, perfect for removing duplicates and fast membership tests. Learn set operations and when to reach for one.
Python Tuples vs Lists: Key Differences
Tuples are immutable and lists are mutable — that single difference shapes when to use each. Learn the key distinctions, performance trade-offs, and practical examples.
Understanding Python String Formatting (f-strings)
F-strings are the fastest, most readable way to format strings in Python. Learn how to embed variables, format numbers, align text, and debug with f-string syntax.
Python File Handling: Read and Write Files
Learn to read and write files in Python using open() and the with statement. Covers text and binary modes, reading line by line, appending, and safe file handling.
Working With JSON in Python
Python's json module converts between JSON text and Python objects with four core functions. Learn to parse, create, read, and write JSON with practical examples.
Python Lambda Functions Explained
A Python lambda is a small anonymous function written in one line. Learn the syntax, where lambdas shine with sorted and map, and when a def function is better.
Map, Filter and Reduce in Python
Map, filter, and reduce transform, select, and combine items in a sequence. Learn how each works in Python, when to use them, and how comprehensions compare.
Python Classes and Objects for Beginners
A class is a blueprint and an object is an instance built from it. Learn Python classes, the __init__ method, self, attributes, and methods with beginner examples.
Python Inheritance and Polymorphism Explained
Inheritance lets a class reuse another's code; polymorphism lets different objects share one interface. Learn both pillars of Python OOP with clear examples.
Understanding Python Modules and Packages
A Python module is a single .py file and a package is a folder of modules. Learn how imports, __init__.py, and namespaces organize larger Python projects.
Python pip and Dependency Management Basics
pip installs and manages Python packages from PyPI. Learn to use virtual environments, requirements.txt, and version pinning to keep projects reproducible.
Python Type Hints Explained for Beginners
Python type hints annotate variables and functions with expected types. Learn the syntax, how tools like mypy check them, and why they make code clearer.
Working With Dates and Times in Python
Python's datetime module handles dates, times, and time zones. Learn to parse, format, and do arithmetic with dates while avoiding common timezone pitfalls.
Python Context Managers and the with Statement
Python context managers and the with statement guarantee cleanup like closing files, even if errors occur. Learn how they work and how to write your own.
Understanding args and kwargs in Python
In Python, *args collects extra positional arguments and **kwargs collects extra keyword arguments, letting functions accept any number of inputs flexibly.
Python Iterators and Iterables Explained
An iterable is anything you can loop over; an iterator is the object that produces its values one at a time. Learn the difference and how for loops use both.
How to Write Clean Python Functions
Clean Python functions are small, do one thing, have clear names, and few parameters. Learn practical rules for writing functions that are easy to read and test.
Pandas GroupBy Explained With Examples
Pandas GroupBy splits a DataFrame into groups, applies an aggregation, and combines the results. Learn the split-apply-combine pattern with clear examples.
Merging and Joining DataFrames in Pandas
Combine Pandas DataFrames with merge, join, and concat. Learn inner, left, right, and outer joins, how keys work, and how to avoid duplicated rows.
Pandas Apply, Map and Applymap Explained
apply, map, and applymap all transform Pandas data but at different scopes. Learn when to use each, and why vectorised operations usually beat them all.
How to Read CSV and Excel Files With Pandas
Load CSV and Excel files into Pandas with read_csv and read_excel. Learn to handle encodings, delimiters, dtypes, dates, and messy real-world files.
What Is a Correlation and How to Measure It
Correlation measures how two variables move together, from -1 to +1. Learn Pearson, Spearman, correlation vs causation, and how to measure it in Python.
Building Dashboards With Plotly and Dash
Dash lets you build interactive analytics dashboards in pure Python using Plotly charts and callbacks. Learn the layout, callbacks, and how to ship your first app.
Build an Expense Tracker With Python
Build an expense tracker in Python that records spending, stores it in a CSV or SQLite database, and reports totals by category. A practical project for learning file I/O and data.
Build a Web Scraper With Python and BeautifulSoup
Build a web scraper in Python with requests and BeautifulSoup: fetch a page, parse the HTML, select elements by tag or CSS, and extract structured data ethically and reliably.
Build a Discord Bot With Python
Build a Discord bot in Python with discord.py: create an application, get a token, handle slash commands and events, and deploy a bot that responds in your server in real time.
Build a REST API With Django REST Framework
Build a REST API with Django REST Framework: define models, serializers, and viewsets, then wire routers to expose CRUD endpoints with authentication, pagination, and browsable docs.
Free Data Analytics Courses: The Complete 2026 Roadmap
Follow a free data analytics roadmap for 2026: learn spreadsheets, SQL, Python, statistics, and dashboards in the right order and build a portfolio that gets you hired.
How to Learn Data Analytics for Free in 2026
Learn data analytics for free in 2026 with a practical self-study plan covering spreadsheets, SQL, Python, and dashboards, plus projects that make you job-ready.
Free Data Analyst Course: What to Study and in What Order
A free data analyst course laid out module by module: what to study and in what order, from spreadsheets and SQL to statistics, Python, and dashboards.
The Data Analyst Skill Stack: SQL, Spreadsheets, Python, BI
The data analyst skill stack explained: SQL, spreadsheets, Python, and BI tools, what each pillar does, and free ways to practise every one of them.
How to Clean Messy Data with Pandas
Learn how to clean messy data with Pandas step by step: fix missing values, correct dtypes, drop duplicates, tidy strings, and reshape frames for analysis.
Excel to Python: Level Up Your Data Analysis
Move from Excel to Python for data analysis and map your spreadsheet habits to pandas, so you gain power and repeatability without losing everyday productivity.
Pandas GroupBy: The Analyst's Most Useful Tool
Master pandas GroupBy, the analyst's most useful tool, with the split-apply-combine pattern, real business questions, and clear examples you can reuse immediately.
COVID Data Analysis: A Guided Pandas Project
Learn pandas by analyzing real COVID data: load daily case counts, compute rolling averages, spot trends, and build honest visualizations that avoid misleading readers.
Web Scraping to Dataset: Your First End-to-End Project
Build your first end-to-end web scraping project: scrape a site responsibly, structure the results into a clean dataset, and analyze it with pandas from start to finish.
Analyze Your Personal Finances With Python
Use Python to analyze your personal finances: import bank transactions, categorize spending automatically, and build a dashboard that shows exactly where your money goes.
Build a Weather Data Analysis Project
Build a weather data analysis project in Python: pull data from an API, wrangle the time series, and visualize temperature and climate trends with clear, honest charts.
Python for Data Analysis: A Free Starter Course
Learn Python for data analysis the practical way: master the small, high-value subset analysts use daily instead of drowning in the whole language.
NumPy Basics Every Data Analyst Should Know
Master the NumPy basics every data analyst needs: arrays, vectorization, broadcasting, and why they crush plain Python loops for speed and clarity.
Working With CSV and Excel Files in Python
A practical guide to working with CSV and Excel files in Python: read, write, clean, and automate tabular data with pandas, no more manual spreadsheet drudgery.
Virtual Environments and pip for Data Projects
Master virtual environments and pip for data projects: reproducible, isolated Python setups that end dependency conflicts and the it-works-on-my-machine problem.
How to Connect Python to a SQL Database
Learn how to connect Python to a SQL database, run queries safely, load results into pandas, and automate reports — a core skill for every data analyst.
Learn Pandas by Analyzing Your Fitness Data
Learn pandas by analyzing your fitness data — turn steps, heart rate, and workouts into a practice dataset that teaches real data-wrangling skills.
Hugging Face Transformers Explained
Hugging Face Transformers is a Python library that gives you pretrained NLP models in a few lines of code, no training from scratch required.
Go vs Python for Backend Development
Go wins for high-concurrency, low-latency infrastructure; Python wins for speed of development, AI integration, and ecosystem breadth.
What Is Python Used For? A Beginner's Guide
Python is a beginner-friendly, general-purpose programming language used for web development, data analysis, automation, and AI. This guide explains why it's popular, where it's used in the real world, and how to write your first script.
10 Python Projects for Beginners to Build Real Skills
The best Python projects for beginners are small, finished, and slightly harder than your last one, moving from a calculator to a simple web scraper within a few weeks. This guide lists projects in order and what each one teaches.
Data Types in Python: A Complete Beginner's Guide
Python data types define what kind of value a variable holds and what operations you can perform on it, covering numbers, text, booleans, and collections like lists and dictionaries. This guide explains each built-in type with practical examples.
What Is Pandas in Python? A Beginner's Guide to Data Analysis
Pandas is a Python library that gives developers fast, flexible data structures for cleaning, analyzing, and transforming tabular data. This guide covers its core objects, common operations, and where it fits in a data workflow.
Python or R for Data Analysis: Which Should You Learn?
Python wins for general-purpose flexibility and production deployment, while R wins for statistical depth and visualization polish. This guide compares both languages so you can pick the right one for your data analysis goals.
What Is Django? Python's Web Framework Explained
Django is a free, open-source Python web framework that lets developers build secure, database-driven websites quickly by handling routing, database access, and admin tooling out of the box. This guide covers what it does and why it's popular.
Julia Programming Language: What Makes It Different?
Julia is a programming language built to combine the speed of compiled languages like C with the readability of Python. This guide covers what Julia is used for, how it compares to Python, and where it fits in a data-heavy computing stack.
What Is NumPy and Why Does Python Need It?
NumPy is the foundational Python library for fast numerical computing, giving Python array operations that run at compiled-language speed. This guide explains what NumPy does, its core array object, and why so much of the Python data stack depends on it.
What Is PySpark? Python's Gateway to Big Data
PySpark is the Python API for Apache Spark, letting developers process massive datasets across many machines using familiar Python syntax. This guide covers what PySpark does, its core components, and when to reach for it.
AI Programming Languages: Which One Should You Learn?
Python leads AI development thanks to its libraries and readability, but R, Julia, C++, and Java each have a place. This guide breaks down which language fits which AI task, from prototyping to production deployment.
What Is a Decorator in Python? A Practical Guide
A decorator in Python is a function that wraps another function to add behavior without changing its source code. This guide explains how decorators work, why they exist, and walks through writing your own with clear, working examples.
Concurrency in Python: Threads, Processes, and asyncio
Choosing between threads, processes and asyncio in Python comes down to one question: is your work waiting on I/O or burning CPU. This guide makes that distinction precise, explains what the GIL actually blocks, and shows the failure modes — sequential awaits, blocking calls, unbounded fan-out — that make async code disappoint.
The Scientific Python Stack: NumPy, SciPy and Friends
The scientific Python stack is built on one data structure: NumPy's ndarray, a typed block of contiguous memory with shape and stride metadata. SciPy, pandas, scikit-learn and the deep learning frameworks all sit on that foundation, and understanding it explains their performance, their errors and their interoperability.
Choosing the Right Python Data Structure for the Job
Pick a Python container by the access pattern you need: dict and set for membership and lookup by key, list for ordered access by position, deque for work at both ends, heapq when you only need the smallest item. This article maps each structure to the operations it is actually optimised for.
Python Fundamentals: The Core Concepts That Carry Everything
A small set of Python mechanics explains most of the language's surprising behaviour: everything is an object with a reference, names are bindings rather than boxes, mutability decides what assignment does, and iteration is a protocol. Learn these four and mutable defaults, scope errors, identity checks and encoding bugs stop being mysteries.
Python Testing Explained: Fixtures, Mocks, and Coverage
A good Python test suite is layered: fast isolated tests for logic, slower integration tests against real dependencies, and a small number of end-to-end checks. This guide sets out that spectrum, the pytest features that make each layer manageable, and how to tell whether your suite is actually trustworthy.
Data Wrangling With Pandas: A Practical Field Guide
Wrangling in pandas follows a repeatable arc: load with explicit types, inspect, clean, reshape, join, aggregate, then export in a format that preserves what you fixed. This guide walks that arc, names the failure at each stage, and shows the habits that keep a pipeline reproducible.
How to cut a pandas DataFrame's memory use before you reach for Spark
Most oversized DataFrames are oversized for three fixable reasons: strings stored as Python objects, 64-bit numerics that never needed the range, and columns you loaded but never used. Declaring dtypes at read time, converting low-cardinality text to category and selecting columns usually recovers enough room to stay on one machine.
How to fix SettingWithCopyWarning in pandas for good
SettingWithCopyWarning means pandas cannot tell whether the object you are assigning into is a view of another frame or a fresh copy, so your write may silently go nowhere. The durable fix is structural: select and assign in one .loc step, or take an explicit .copy() when you mean to branch.
How to vectorise a Python loop with NumPy, step by step
Vectorising is a translation procedure, not a bag of tricks. Classify the loop first — elementwise, reduction, sliding window or conditional — then map it to its array form: arithmetic, a reduction with an axis, a windowed view or cumulative operation, and boolean masks or where.
melt vs pivot in pandas: choosing wide or long for the job ahead
Choose the shape by what consumes the table, not by what looks tidier. Long form suits grouping, plotting and storage; wide form suits human reading and matrix-style model inputs. melt and stack go long, pivot and pivot_table go wide, and they differ mainly in what they do with duplicate pairs.
How to catch a broken pandas merge with validate and indicator
A pandas merge will not warn you when it multiplies rows or matches nothing at all. Passing validate= to declare the expected cardinality turns a silent many-to-many explosion into an exception, and indicator=True lets you count unmatched rows on each side before you trust the result.
How Python dictionaries work: hashing, collisions and ordering
A dict is a hash table with a compact index layer, and every surprising behaviour follows from that structure — unhashable keys, equal-but-distinct keys colliding, preserved insertion order and resize pauses. The payoff is knowing what makes a good key and when a dict is the wrong container.
Mocking in Python: when to patch and when to inject
Patching binds a test to the import path of the code under test, so refactors break tests that never touched behaviour. This sets out where patch belongs, why patching the wrong location silently does nothing, what autospec catches, and when passing the dependency in is the better seam.
Python iterators and generators: how yield actually works
Understand iteration from the protocol up. A generator function returns a paused computation rather than a value, which explains why generators can be consumed only once, why exceptions surface where they do, and where laziness saves memory or quietly costs you.
Python list vs array.array vs NumPy array
A list stores pointers to objects, array.array stores raw values of one type, and a NumPy array adds vectorised operations over that same contiguous buffer. The choice comes down to whether your data is homogeneous and whether you operate on it element-wise — and mixing Python loops with NumPy throws away the reason to use it.
Why Python mutable default arguments cause bugs
Understand the shared-default bug properly: default values are evaluated once when the function is defined, so a mutable default becomes state attached to the function object. Learn the None sentinel fix and where the same once-at-definition rule surprises you elsewhere.
Python scope explained: LEGB, closures, global and nonlocal
Make scoping errors predictable. Python decides a name is local at compile time if the function assigns to it anywhere, which explains UnboundLocalError, and closures capture the variable rather than its value, which explains the loop-in-a-lambda surprise.
Sharing state between Python processes with multiprocessing
Every way of sharing data between Python processes trades copy cost against coordination cost. Learn what start methods let workers inherit, why pickling usually dominates, when shared memory earns its locking burden, and how restructuring often removes the need to share anything at all.
How to test Python code that calls external APIs
Tests that hit a real third-party service are neither fast nor deterministic, and hand-written stubs quietly drift from reality. The workable answer is layered: stub the transport for logic, keep recorded responses for shape, and run one small contract check against the live service on its own schedule.
The Python collections module: Counter, defaultdict, deque and namedtuple
Each collections type replaces one specific hand-written pattern: a tally becomes Counter, a group-by becomes defaultdict, work at both ends becomes deque, and a fixed record becomes namedtuple. Learn the pattern behind each, and why defaultdict's silent key creation is the one genuine trap.
Understanding Python decorators from scratch
Write decorators you actually understand. A decorator is a function that takes a function and returns a replacement, and every confusing variant — arguments, stacking, class decorators — is that one idea with an extra layer wrapped around it.
When to use a Python set instead of a list
The decision is membership testing: a set answers whether an item is present in near-constant time while a list scans every element, and that difference only matters when the check happens inside a loop. Here is the break-even, and the ordering and hashability you trade away.