scientific-computing

Python Programming Learning Circle

May 21, 2025 · Fundamentals

Introduction to NumPy: Core Features, Array Creation, Operations, Indexing, and I/O

This article provides a comprehensive overview of NumPy, covering its high‑performance ndarray object, core functionalities such as broadcasting and vectorized operations, array creation and manipulation methods, mathematical and statistical functions, linear‑algebra utilities, random number generation, and input/output capabilities with practical code examples.

NumPyPythonarray

0 likes · 6 min read

Introduction to NumPy: Core Features, Array Creation, Operations, Indexing, and I/O

php中文网 Courses

Apr 30, 2025 · Fundamentals

Comprehensive Introduction to NumPy: ndarray, Creation, Indexing, Operations, Linear Algebra, I/O, and Real‑World Data Analysis

This article provides a thorough overview of NumPy, covering its core ndarray structure, various array creation methods, indexing and slicing techniques, vectorized operations with broadcasting, statistical and linear‑algebra functions, file input/output, and a practical data‑analysis example, all illustrated with executable Python code.

Array OperationsNumPyPython

0 likes · 10 min read

Comprehensive Introduction to NumPy: ndarray, Creation, Indexing, Operations, Linear Algebra, I/O, and Real‑World Data Analysis

Baidu Tech Salon

Apr 2, 2025 · Artificial Intelligence

PaddlePaddle Framework 3.0 Released: Five Core Innovations for Large Models and Scientific Computing

PaddlePaddle 3.0, launched on April 1 2025, introduces five core innovations—including dynamic‑static unified automatic parallelism, a training‑inference integrated PIR, high‑order automatic differentiation for scientific computing, a one‑stage CINN compiler, and heterogeneous multi‑chip adaptation—that dramatically reduce distributed‑training code, boost performance up to four‑fold, and extend the framework to aerospace, automotive, meteorology and life‑science applications while remaining fully compatible with the 2.0 API.

Large ModelsNeural Network CompilerPaddlePaddle

0 likes · 21 min read

PaddlePaddle Framework 3.0 Released: Five Core Innovations for Large Models and Scientific Computing

Test Development Learning Exchange

Dec 15, 2024 · Fundamentals

45 Common NumPy Operations with Code Examples

This article presents a comprehensive guide to 45 essential NumPy operations, covering array creation, reshaping, arithmetic, statistical functions, linear algebra, and more, each illustrated with concise explanations and ready-to-run Python code examples to help readers efficiently leverage NumPy for scientific computing.

NumPyPythonTutorial

0 likes · 18 min read

45 Common NumPy Operations with Code Examples

Test Development Learning Exchange

Nov 8, 2024 · Fundamentals

Comprehensive Guide to Common NumPy Array Operations

This article presents a thorough tutorial on NumPy array creation, indexing, reshaping, concatenation, splitting, copying, slicing, statistical analysis, boolean indexing, sorting, unique values, broadcasting, merging, insertion, deletion, transposition, flattening, multi‑dimensional merging, random sampling, dot and outer products, cumulative operations, and differences, providing code examples for each to boost data‑processing efficiency in Python.

Array OperationsData ProcessingNumPy

0 likes · 12 min read

Comprehensive Guide to Common NumPy Array Operations

AntTech

Oct 28, 2024 · Artificial Intelligence

Highlights of AI Large‑Model Sessions at CNCC 2024

The CNCC 2024 conference featured a series of expert talks on AI large‑model research, covering paradigm shifts in scientific discovery, knowledge enhancement and governance, data‑infrastructure analytics, vertical‑domain inference, diffusion‑model advances, multimodal model progress, and medical AI applications, illustrating the breadth and impact of large‑model technologies across multiple domains.

AIData InfrastructureKnowledge Governance

0 likes · 9 min read

Highlights of AI Large‑Model Sessions at CNCC 2024

Java Captain

May 21, 2024 · Fundamentals

Fortran Returns to the TIOBE Top 10 in May 2024: Causes and Analysis

The May 2024 TIOBE index shows Fortran re‑entering the top‑10 programming languages, driven by its long‑standing evolution, high performance in scientific computing, free open‑source nature, and sustained market interest compared with newer languages like Kotlin and Rust.

FortranTIOBE indexlanguage popularity

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Fortran Returns to the TIOBE Top 10 in May 2024: Causes and Analysis

Test Development Learning Exchange

Apr 22, 2024 · Big Data

Mayavi, Matplotlib, and Plotly for 3D Data Visualization in Python

This content introduces Mayavi, Matplotlib, and Plotly as Python libraries for 3D data visualization, providing code examples for creating surface plots, scatter plots, and interactive charts, with a focus on handling large-scale scientific datasets.

3D graphicsData VisualizationMatplotlib

0 likes · 2 min read

Mayavi, Matplotlib, and Plotly for 3D Data Visualization in Python

Model Perspective

Nov 13, 2022 · Fundamentals

Essential Python Programming Resources: Basics, Libraries, and Modeling Tools

This article compiles a curated list of Python programming fundamentals and popular third‑party libraries for data processing, visualization, scientific computing, optimization, and more, linking to detailed tutorials for each topic, including Numpy, Matplotlib, Pandas, Scipy, PuLP, NetworkX, Geatpy, Numba, and Taichi.

LibrariesPythonTutorial

0 likes · 4 min read

Essential Python Programming Resources: Basics, Libraries, and Modeling Tools

Python Programming Learning Circle

Mar 4, 2022 · Big Data

Introduction to NumPy and Pandas: Fundamentals, Operations, and Data Handling in Python

This article provides a comprehensive overview of NumPy and pandas, covering ndarray basics, multi‑dimensional array creation, core array attributes, broadcasting, random number generation, reshaping, as well as pandas Series and DataFrame structures, data import/export, grouping, merging, and advanced data manipulation techniques for scientific and data‑analysis tasks.

Array OperationsDataFramesNumPy

0 likes · 17 min read

Introduction to NumPy and Pandas: Fundamentals, Operations, and Data Handling in Python

Python Programming Learning Circle

Dec 3, 2021 · Fundamentals

Using Mayavi for 3D Visualization in Python: Installation, Core Concepts, and Example Code

This tutorial walks through installing the Mayavi library, explains its core mlab module and API, and provides a series of Python code examples—including mesh, points3d, plot3d, imshow, surf, contour3d, and quiver3d—to demonstrate how to create various 3‑D visualizations.

3D visualizationData VisualizationMayavi

0 likes · 8 min read

Using Mayavi for 3D Visualization in Python: Installation, Core Concepts, and Example Code

Architects' Tech Alliance

Sep 23, 2021 · Fundamentals

Understanding High‑Performance Computing (HPC): Principles, Architecture, and Applications

The article explains high‑performance computing (HPC) concepts, including serial and parallel processing, supercomputer performance measured in FLOPS, real‑world scientific applications such as drug discovery and weather forecasting, and the hardware architectures that enable these massive computational capabilities.

Computer ArchitectureFLOPSGPU

0 likes · 7 min read

Understanding High‑Performance Computing (HPC): Principles, Architecture, and Applications

Python Programming Learning Circle

Sep 3, 2021 · Fundamentals

A Practical Guide to Matplotlib: High‑Quality 2D/3D Plots and Advanced Styling in Python

This article introduces Matplotlib as a versatile, open‑source Python plotting library, showcasing its 2D and 3D capabilities, various chart types, color‑customization options, LaTeX support, and integration with tools like Seaborn, while also providing installation tips and concise code examples.

Data VisualizationMatplotlibPlotting

0 likes · 5 min read

A Practical Guide to Matplotlib: High‑Quality 2D/3D Plots and Advanced Styling in Python

Python Programming Learning Circle

Mar 8, 2021 · Operations

Using IPython and Jupyter for Multi‑language and Parallel Computing

This article explains how IPython and Jupyter notebooks support multi‑language execution, integrate Fortran via F2PY, and enable parallel and distributed computing with ipyparallel, illustrating practical magic commands, cluster setup, and performance considerations for scientific Python workflows.

IPythonJupyterMagic Commands

0 likes · 12 min read

Using IPython and Jupyter for Multi‑language and Parallel Computing

Architects' Tech Alliance

Dec 27, 2019 · Fundamentals

Survey of GPU-Accelerated HPC Applications Across Scientific Domains

This article surveys the rapid growth of GPU-accelerated high‑performance computing (HPC) applications driven by NVIDIA's ecosystem, detailing the most common scientific fields, the proportion of GPU‑supported tools, and the emerging role of AI as a primary growth engine.

AICUDAGPU

0 likes · 8 min read

Survey of GPU-Accelerated HPC Applications Across Scientific Domains