This article presents eight essential probability distributions for everyday data‑science tasks, explains when to use each, provides concise Python code for fitting and sampling, and shares practical tips and a real‑world case study to help you choose the right model quickly.
This tutorial demonstrates how to import, clean, and explore 2013 weather observations from Toulouse Airport using Python libraries such as pandas and SciPy, perform consistency checks, visualize temperature trends, assess variable correlations, and fit probability distributions—including normal, log‑normal, and Weibull—to the data.
This article walks through a common Python matplotlib visualization issue, showing how to read parameter data, generate multiple gamma distribution curves, add labels and legends, and produce polished plots, with complete code examples and explanations to help readers replicate the solution.
This article presents thirty essential Python techniques for rapid statistical analysis, covering calculations such as mean, median, mode, variance, moving average, correlation, and more, each accompanied by complete code snippets and sample outputs to help readers apply these methods directly.
This article introduces ten widely used Python libraries for image processing—such as scikit‑image, NumPy, SciPy, Pillow, OpenCV, SimpleCV, Mahotas, SimpleITK, pgmagick, and Pycairo—explaining their main features and providing concise code examples for tasks like filtering, segmentation, and visualization.
This article introduces several common probability distributions—including uniform, normal, lognormal, Poisson, exponential, binomial, Student's t, and chi‑squared—explains their properties, and provides Python code using NumPy, SciPy, and Matplotlib to visualize each distribution.
Linear programming optimizes a linear objective under linear constraints, and this article explains its theory, common solution methods such as Simplex, Interior‑Point, and Branch‑and‑Bound, illustrates a production‑planning case, and provides a complete Python implementation using SciPy’s linprog function.
Learn how to perform one-dimensional interpolation in Python using SciPy's interp1d function, explore various interpolation methods such as linear, nearest, quadratic, and cubic, and visualize the results with Matplotlib to compare their fitting behaviors on sample data.
This article introduces four advanced Python data‑visualization methods—heat map, 2D density plot, spider (radar) plot, and hierarchical tree diagram—explaining their concepts, practical use cases, and providing complete seaborn, matplotlib, and SciPy code examples for each.
This article introduces the most popular Python libraries for image processing—including scikit‑image, NumPy, SciPy, Pillow, OpenCV, SimpleCV, Mahotas, SimpleITK, pgmagick, and Pycairo—explains their core features, and provides concise code snippets demonstrating tasks such as filtering, template matching, masking, blurring, and visualisation.
This article lists the key SciPy sparse‑graph functions—such as connected components, Laplacian, various shortest‑path algorithms, traversals, minimum spanning tree, flow and matching utilities—and provides Python code examples demonstrating their use.
This guide introduces SciPy's optimize module, covering scalar and multivariate minimization, global optimization algorithms, root finding, linear programming, and assignment problems, complete with clear Python code examples and explanations of each method's usage and output.
This article demonstrates how to locate the global maximum of the function f(x) = (1‑x³)·sin(3x) by visualizing it with Matplotlib, applying SciPy’s optimization tools such as fminbound, and comparing deterministic methods with random sampling, highlighting the pitfalls of local optima.
This article introduces the concept of interpolation for discrete data, demonstrates how to use SciPy's interp1d function with various methods (linear, nearest, nearest‑up, zero, quadratic, cubic), visualizes the resulting curves alongside the original points, and provides complete Python code for reproducing the plots.
This article explains the concept of data (curve) fitting, demonstrates how to plot raw data points with Matplotlib, and shows step‑by‑step how to use SciPy's curve_fit to derive an exponential model that matches the given dataset.
This article introduces the most widely used Python image‑processing libraries—including scikit‑image, NumPy, SciPy, Pillow, OpenCV‑Python, SimpleCV, Mahotas, SimpleITK, pgmagick, and Pycairo—explaining their key features and providing concise code snippets that demonstrate filtering, segmentation, enhancement, and computer‑vision tasks.
This article provides a concise overview of key Python data‑analysis libraries—including NumPy, pandas, matplotlib, IPython/Jupyter, SciPy, scikit‑learn, and statsmodels—explaining their core features, typical use cases, and how they interoperate to form a powerful scientific computing ecosystem.
This article reviews eight Python-based simple linear regression methods, explains their underlying algorithms, compares their computational complexity and execution speed on datasets up to ten million points, and offers guidance on selecting the most efficient approach for data‑science tasks.
