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44 articles tagged #Python.
P.01Unit tests tell you if your code does what you wrote. They don't tell you if your AI feature does what users need. Here's how to build an evaluation pipeline that catches the failures that matter before users do.
P.02uv is a Python package manager written in Rust that handles dependencies, virtual environments, and Python version management. We've been using it across all our projects since early 2026. Here's what actually changed.
P.03Getting a language model to return valid, schema-conforming JSON is harder than it looks. Here's what works in production, from native structured output APIs to library-level validation.
P.04Async Django is production-ready in 2026, but landmines still exist. Four real projects, benchmarks, and a framework for deciding when async actually helps.
P.05EditorPickThe network latency between your Django app and your FastAPI ML service is probably longer than inference itself. Here is how to serve models from Django directly.
P.06EditorPickWe migrated three production projects from Celery to Django's new Tasks framework. Two went smoothly. One was a disaster. Here is everything we learned.
P.07EditorPickWe rebuilt a React SPA as a Django + HTMX app. 847KB of JavaScript became 48KB. Here is every pattern, rough edge, and performance win from the migration.
P.08EditorPickMost DRF vs FastAPI comparisons are written by people who only use one. We ship both in production. Here is what actually matters and when to choose each.
P.09How we built an AI-powered interface that lets non-technical users query any database using plain English, eliminating SQL expertise requirements and democratizing data access.
P.10Step-by-step guide to configuring a complete full-stack development environment with Python, Django backend, and React frontend.
P.11Learn Python variables and data types — integers, floats, strings, booleans, lists, tuples, and dictionaries with clear examples.
P.12Perform EDA on the Boston Housing dataset using scatter plots and regression lines to identify relationships between features and housing prices.
P.13Master hyperparameter tuning with GridSearchCV using KNN, Random Forest, and SVM models with custom scoring functions.
P.14Build a sentiment analysis pipeline with text preprocessing, TF-IDF vectorization, and Multinomial Naive Bayes classification on Twitter data.
P.15Explore time series forecasting methods including ARIMA, exponential smoothing, and seasonal decomposition for real-world prediction tasks.
P.16Understand decision tree algorithms for classification and regression, their pros and cons, and build an Iris classifier with Python code.
P.17Learn SVM theory including hyperplanes and the kernel trick, then build a classifier on the breast cancer dataset using scikit-learn.
P.18Build a logistic regression model to predict diabetes outcomes using the Pima Indians dataset, covering sigmoid functions, feature scaling, and evaluation.
P.19Implement K-Nearest Neighbors classification using scikit-learn with data visualization, model training, and performance evaluation on real datasets.
P.20Understand the KNN algorithm — how it works, distance metrics, choosing K, and its applications in both classification and regression tasks.
P.21Compare Naive Bayes, SVM, Decision Tree, and Random Forest for email spam detection with a complete Python pipeline from data loading to evaluation.
P.22Master MAE, MSE, R², RMSE, accuracy, precision, recall, F1-score, AUC-ROC, and confusion matrices with formulas and Python code examples.
P.23Build an insurance cost prediction model using multivariate linear regression with one-hot encoding, evaluation metrics, and residual analysis.
P.24EditorPickMaster 7 encoding techniques for categorical variables — one-hot, label, dummy, binning, count, frequency, and target encoding with Python examples.
P.25Build a linear regression model from scratch using scikit-learn, with data visualization, feature selection, and model evaluation metrics.
P.26Explore derivatives, integrals, multivariate calculus, optimization, and differential equations with Python implementations using SymPy and NumPy.
P.27Learn essential statistics concepts — mean, median, mode, variance, standard deviation, percentiles, quartiles, and z-scores with Python implementations.
P.28Understand vectors, matrices, transpose, inverse, determinant, trace, dot product, and eigenvalues with NumPy implementations for data science.
P.29EditorPickMaster SQL from basics to advanced — SELECT, JOIN, GROUP BY, ORDER BY, indexes, date functions, and more using SQLite with Python.
P.30Learn to create compelling data visualizations using Matplotlib and Seaborn — line plots, scatter plots, bar charts, histograms, heatmaps, and more.
P.31Master Pandas for data manipulation — reading data, selecting columns, grouping, merging DataFrames, handling missing values, and working with dates.
P.32Learn NumPy essentials — arrays, shapes, reshaping, slicing, stacking, broadcasting, universal functions, and image processing with practical examples.
P.33EditorPickA deep dive into Python lists, tuples, sets, dictionaries, and functions with comprehensive code examples and practical exercises.
P.34Learn Python fundamentals including identifiers, data types (int, float, str, list, tuple, set, dict), operators, and basic operations with hands-on examples.
P.35EditorPickA structured 100-day data science bootcamp roadmap covering Python, statistics, machine learning, deep learning, and real-world projects.
P.36EditorPickA comprehensive guide covering 10 regression types — linear, polynomial, logistic, ridge, lasso, elastic net, and more — with Python code examples and selection criteria.
P.37Master Python list comprehensions including syntax, filtering, nested comprehensions, and dictionary/set comprehensions with practical code examples.
P.38Learn OpenCV fundamentals including image I/O, pixel manipulation, color conversion, resizing, filtering, edge detection, and feature detection with SIFT and SURF.
P.39Understand the divide and conquer algorithmic paradigm through the maximum subarray sum problem, with Python implementation and step-by-step analysis.
P.40EditorPickA comprehensive guide to EDA covering visualization techniques, summary statistics, correlation analysis, data cleaning, PCA, anomaly detection, and feature engineering.
P.41EditorPickMaster bitwise operations, bitmasking, bit manipulation tricks, and bit-based algorithms for competitive programming and software engineering interviews.
P.42Build a CNN model using ResNet50 transfer learning to classify gender from eye images, covering data preprocessing, model architecture, and evaluation.
P.43Learn to predict stock prices using Long Short-Term Memory (LSTM) networks in Python with TensorFlow, from data preprocessing to building and evaluating the model.
P.44A hands-on guide to building a fruit image classifier using transfer learning with ResNet50 in TensorFlow and Keras, achieving high accuracy on the Fruits-360 dataset.