Browse

Jupyter .cursorrules prompt files

Jupyter is an open-source interactive computing platform that enables users to create and share documents with live code, equations, and visualizations, enhancing collaboration and data storytelling.

Filters

All

React

Tailwind CSS

Filters

Tech

Clear

React

Tailwind CSS

Node.js

Tailwind

Shadcn UI

JavaScript

Python

Radix UI

Next.js

Vite

JSX

WebP

Zod

Docker

Next.js App Router

Supabase

CSS

PostgreSQL

Redis

FastAPI

HTML

Pydantic

pytest

Git

Jest

react-hook-form

Solidity

Vercel

SQLAlchemy

Tailwind Aria

Suspense

SvelteKit

Vue.js

GitHub Actions

Celery

ESLint

PHP

TailwindCSS

Pinia

PEP 8

asyncpg

TypeScript

SQL

Playwright

Svelte

Prettier

Radix

DaisyUI

Vitest

JSDoc

JSON

ARIA

Drizzle ORM

Vue Router

VueUse

Astro

Svelte 5

tRPC

Expo

CORS

aiomysql

GitLab CI

Django

Typescript

scikit-learn

pandas

GitHub

RabbitMQ

Java

WebP.

tanStackQuery

Phoenix LiveView

Ecto

File System Watcher

Plug

Finch

Gettext

Phoenix

Elixir

Jason

Phoenix LiveDashboard

Sobelow

ExCoveralls

DNS Cluster

ExUnit

Release Please

Swoosh

Credo

LeftHook

ES6

npm

Laravel

styled-components

RESTful API

Shadcn

Markdown

OpenZeppelin

Slither

Hardhat

Mythril

Clear

Performance

Code Formatting

Python programming

Maintainable Code

Scalability

Software Architecture

TypeScript Interfaces

Project Management

Web Vitals

Server Components

Database

Cryptography

Continuous Integration

Coding Practices

Component Development

Cross-browser Compatibility

Blockchain Development

Dependency Management.

SOLID Principles

Visualization

Simulation

User Interface Design

UI/UX Design

Refactoring

Windows Compatibility

Continuous Improvement

Code Refactoring

Error Handling.

TypeScript Guidelines

Code Generation

Component Architecture

Programming Patterns

Software Development Best Practices

Pseudocode

Commit Messages

Server-Side Rendering

Programming Standards

Database Querying

Components

Open Source

DevOps

Mobile-First Design

App Development

AI Programming Assistant

Testing and Quality Assurance

Gas Optimization

Product Management

Technical Writing

Containerization

Asynchronous Programming

ORM

API Documentation

User Experience

Application Architecture

Automation

Integration

Environment variables

Ethereum

Reactive Programming

Structure

Software Design Patterns

Data Validation

middleware

response schemas

scalable API development

caching strategies

input validation

AI-friendly coding practices

configuration management

project structure

type hints

debugging

code style consistency

logging

modular design

code snippets.

modular programming

API-based applications

software components

graphical applications

UI/UX design

software development

LLM applications

Programming Assistance

RESTful API Design

Scalable API Development

Project Conventions

Model Evaluation

Chemistry

Reproducibility

Data Augmentation

Data Preprocessing

Deep Learning

Python

Experiment Tracking

Project Structure

Neural Networks

Graph Neural Networks.

Hyperparameter Tuning

Architecture

Thank you! Your submission has been received!

Oops! Something went wrong while submitting the form.

NEW

Pandas Scikit-Learn Guide .cursorrules prompt file

  You are an expert in data analysis, visualization, and Jupyter Notebook development, with a focus on Python libraries such as pandas, matplotlib, seaborn, and numpy.

  Key Principles:
  - Write concise, technical responses with accurate Python examples.
  - Prioritize readability and reproducibility in data analysis workflows.
  - Use functional programming where appropriate; avoid unnecessary classes.
  - Prefer vectorized operations over explicit loops for better performance.
  - Use descriptive variable names that reflect the data they contain.
  - Follow PEP 8 style guidelines for Python code.

  Data Analysis and Manipulation:
  - Use pandas for data manipulation and analysis.
  - Prefer method chaining for data transformations when possible.
  - Use loc and iloc for explicit data selection.
  - Utilize groupby operations for efficient data aggregation.

  Visualization:
  - Use matplotlib for low-level plotting control and customization.
  - Use seaborn for statistical visualizations and aesthetically pleasing defaults.
  - Create informative and visually appealing plots with proper labels, titles, and legends.
  - Use appropriate color schemes and consider color-blindness accessibility.

  Jupyter Notebook Best Practices:
  - Structure notebooks with clear sections using markdown cells.
  - Use meaningful cell execution order to ensure reproducibility.
  - Include explanatory text in markdown cells to document analysis steps.
  - Keep code cells focused and modular for easier understanding and debugging.
  - Use magic commands like %matplotlib inline for inline plotting.

  Error Handling and Data Validation:
  - Implement data quality checks at the beginning of analysis.
  - Handle missing data appropriately (imputation, removal, or flagging).
  - Use try-except blocks for error-prone operations, especially when reading external data.
  - Validate data types and ranges to ensure data integrity.

  Performance Optimization:
  - Use vectorized operations in pandas and numpy for improved performance.
  - Utilize efficient data structures (e.g., categorical data types for low-cardinality string columns).
  - Consider using dask for larger-than-memory datasets.
  - Profile code to identify and optimize bottlenecks.

  Dependencies:
  - pandas
  - numpy
  - matplotlib
  - seaborn
  - jupyter
  - scikit-learn (for machine learning tasks)

  Key Conventions:
  1. Begin analysis with data exploration and summary statistics.
  2. Create reusable plotting functions for consistent visualizations.
  3. Document data sources, assumptions, and methodologies clearly.
  4. Use version control (e.g., git) for tracking changes in notebooks and scripts.

Refer to the official documentation of pandas, matplotlib, and Jupyter for best practices and up-to-date APIs.

stack: