Django 6.0's Built-in Tasks Framework -- We Ripped Out Celery and Here Is What Happened
We migrated three production projects from Celery to Django's new Tasks framework. Two went smoothly. One was a disaster. Here is everything we learned.
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#Python
41 posts tagged with "Python"
Async Django in 2026 -- An Honest Assessment of What Actually Works
Async Django is production-ready in 2026, but landmines still exist. Four real projects, benchmarks, and a framework for deciding when async actually helps.
Django as Your AI Backend -- Serving ML Models Without the Microservices Tax
The 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.
Django + HTMX -- Building Interactive Apps Without the JavaScript Bloat
We 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.
DRF vs FastAPI -- An Honest Comparison From Someone Who Ships Both
Most 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.
Building a Natural Language Database Query Tool — The QueryLytic Case Study
How 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.
Setting Up Your Full-Stack Development Environment with Python, Django, and React
Step-by-step guide to configuring a complete full-stack development environment with Python, Django backend, and React frontend.
Variables and Data Types in Python: A Beginner's Guide
Learn Python variables and data types — integers, floats, strings, booleans, lists, tuples, and dictionaries with clear examples.
Exploratory Data Analysis Using Scatter Plots and Line of Best Fit
Perform EDA on the Boston Housing dataset using scatter plots and regression lines to identify relationships between features and housing prices.
GridSearchCV in Scikit-learn: A Comprehensive Guide to Hyperparameter Tuning
Master hyperparameter tuning with GridSearchCV using KNN, Random Forest, and SVM models with custom scoring functions.
Sentiment Analysis of Twitter Data Using Naive Bayes Algorithm
Build a sentiment analysis pipeline with text preprocessing, TF-IDF vectorization, and Multinomial Naive Bayes classification on Twitter data.
Time Series Prediction: Methods, Models, and Applications
Explore time series forecasting methods including ARIMA, exponential smoothing, and seasonal decomposition for real-world prediction tasks.
Decision Trees: Advantages, Disadvantages, and Applications
Understand decision tree algorithms for classification and regression, their pros and cons, and build an Iris classifier with Python code.
Support Vector Machines (SVM): Theory and Hands-On Classification
Learn SVM theory including hyperplanes and the kernel trick, then build a classifier on the breast cancer dataset using scikit-learn.
Predicting Diabetes Outcomes with Logistic Regression: A Hands-On Guide
Build a logistic regression model to predict diabetes outcomes using the Pima Indians dataset, covering sigmoid functions, feature scaling, and evaluation.
KNN Classification: A Hands-On Guide with Python and Scikit-learn
Implement K-Nearest Neighbors classification using scikit-learn with data visualization, model training, and performance evaluation on real datasets.
The K-Nearest Neighbors Algorithm for Regression and Classification
Understand the KNN algorithm — how it works, distance metrics, choosing K, and its applications in both classification and regression tasks.
Uncovering the Best Email Spam Classifier: A Comparative Study of ML Algorithms
Compare Naive Bayes, SVM, Decision Tree, and Random Forest for email spam detection with a complete Python pipeline from data loading to evaluation.
Evaluation Metrics for Classification and Regression: A Comprehensive Guide
Master MAE, MSE, R², RMSE, accuracy, precision, recall, F1-score, AUC-ROC, and confusion matrices with formulas and Python code examples.
Predicting Medical Costs Using Multivariate Linear Regression in Python
Build an insurance cost prediction model using multivariate linear regression with one-hot encoding, evaluation metrics, and residual analysis.
Transforming Categorical Data: A Practical Guide for Machine Learning
Master 7 encoding techniques for categorical variables — one-hot, label, dummy, binning, count, frequency, and target encoding with Python examples.
Linear Regression in Python: From Data to Model
Build a linear regression model from scratch using scikit-learn, with data visualization, feature selection, and model evaluation metrics.
Calculus for Data Science: An Introduction with Python
Explore derivatives, integrals, multivariate calculus, optimization, and differential equations with Python implementations using SymPy and NumPy.
Statistics for Data Science with Practice in Python
Learn essential statistics concepts — mean, median, mode, variance, standard deviation, percentiles, quartiles, and z-scores with Python implementations.
Linear Algebra for Data Science: Vectors, Matrices, and Operations with NumPy
Understand vectors, matrices, transpose, inverse, determinant, trace, dot product, and eigenvalues with NumPy implementations for data science.
A Comprehensive Guide to Relational Database Management Systems and SQL
Master SQL from basics to advanced — SELECT, JOIN, GROUP BY, ORDER BY, indexes, date functions, and more using SQLite with Python.
Data Visualization in Data Science: An Overview of Matplotlib and Seaborn
Learn to create compelling data visualizations using Matplotlib and Seaborn — line plots, scatter plots, bar charts, histograms, heatmaps, and more.
Mastering Pandas: A Comprehensive Guide with Exercises
Master Pandas for data manipulation — reading data, selecting columns, grouping, merging DataFrames, handling missing values, and working with dates.
NumPy 101: A Beginner's Guide to Data Science with Python
Learn NumPy essentials — arrays, shapes, reshaping, slicing, stacking, broadcasting, universal functions, and image processing with practical examples.
Take Your Python Skills to the Next Level with Built-in Data Structures
A deep dive into Python lists, tuples, sets, dictionaries, and functions with comprehensive code examples and practical exercises.
Master the Basics of Python: A Step-by-Step Guide
Learn Python fundamentals including identifiers, data types (int, float, str, list, tuple, set, dict), operators, and basic operations with hands-on examples.
Complete Data Science Roadmap: From Beginner to Expert in 100 Days
A structured 100-day data science bootcamp roadmap covering Python, statistics, machine learning, deep learning, and real-world projects.
Regression Modeling 101: Understanding Different Types and Choosing the Right One
A comprehensive guide covering 10 regression types — linear, polynomial, logistic, ridge, lasso, elastic net, and more — with Python code examples and selection criteria.
The Power of List Comprehensions in Python
Master Python list comprehensions including syntax, filtering, nested comprehensions, and dictionary/set comprehensions with practical code examples.
Mastering OpenCV: A Comprehensive Introduction to Computer Vision with Python
Learn OpenCV fundamentals including image I/O, pixel manipulation, color conversion, resizing, filtering, edge detection, and feature detection with SIFT and SURF.
Divide and Conquer: A Powerful Strategy for Solving Complex Problems
Understand the divide and conquer algorithmic paradigm through the maximum subarray sum problem, with Python implementation and step-by-step analysis.
Data Detective: Tips and Tricks for Effective Exploratory Data Analysis
A comprehensive guide to EDA covering visualization techniques, summary statistics, correlation analysis, data cleaning, PCA, anomaly detection, and feature engineering.
The Ultimate Guide to Bit Manipulation in Python
Master bitwise operations, bitmasking, bit manipulation tricks, and bit-based algorithms for competitive programming and software engineering interviews.
Gender Determination with Morphometry of Eyes Using Deep Learning
Build a CNN model using ResNet50 transfer learning to classify gender from eye images, covering data preprocessing, model architecture, and evaluation.
Time Series Forecasting of Stock Data Using LSTM Deep Learning
Learn to predict stock prices using Long Short-Term Memory (LSTM) networks in Python with TensorFlow, from data preprocessing to building and evaluating the model.
Transfer Learning for Fruits Classification with ResNet50 and TensorFlow
A 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.