Browse

data visualization .cursorrules strategies

Filters

Tech

Clear

loguru

SMILES

RDKit

Weights & Biases

OpenBabel

pytest-mock

Black

shutil

transformers

watchdog

torch

spaCy

Pylint

PyPDF2

python-docx

beautifulsoup4

setuptools

git

Jupyter

numpy

PyQt6

EEG signal processing

REDCap

Lucide React

shadcn/ui

select_related

prefetch_related

Bitbucket

GitLab

gRPC

Protocol Buffers

GCP

REST

NewSQL

Testing Library

Viem v2

Wagmi v2

AppError

appErrors

SSR (Server-Side Rendering).

supabase

Next.JS

Starknet React

Plasticode

Telegram Bot API

Iconify

NextUI

WASM

KV database

ai-sdk-rsc

Vercel middleware

Orval

Tanstack query

Quokka

Vercel AI

Pint

PHP 8.3

spatie/laravel-package-tools

Web APIs

Chrome Extension Development

Tailwind.

TanStack Query

TanStack Router

next-safe-action.

ES module

ES

nodejs

tailwind-merge

@mui/icons-material

@types/bcryptjs

aceternityui

tailwindcss

shadcn-ui

@mui/styled-engine-sc

@mui/material

@emotion/react

init

@ckeditor/ckeditor5-react

@types/react-dom

clsx

next-auth

react

@prisma/client

typescript

@emotion/styled

ckeditor5

NestJS

MikroORM

ORM

lazy loading

code splitting

bun

PSR-12

PHP 8.1

Eloquent ORM

Luvi

Composer.

Blade

Categories

Clear

Dynamic Routing

Reusability

Continuous Integration.

Animation and Transition

Programming Instructions

UI/UX

Component Design

PHP 7.4 Features

PSR-12 Coding Standards

Web Application

Backend Integration

Cellular Automata

Environmental Simulation

Data Synchronization

Shader Programming

Interactive Systems

Grid Systems

Generative User Interfaces

Middleware Integration

AI SDK Integration

Deployment Considerations

React Server Components

Business Process Modeling

BPMN Diagrams

Project Management Tools

Business Analysis

AI Chatbot

Code Implementation Guidelines

Component-Based Development

Accessibility in Web Design

Modern Web Technologies

Documentation structure

Development guidelines

Coding standards

Testing strategy

Package integration

Code structure

Laravel package development

Browser APIs.

Code Architecture

Web Security

Functional Programming.

Error Boundaries

TypeScript Conventions.

Server Actions

DRY (Don't Repeat Yourself)

Best practices

Code improvements

Agile methodologies

Code comments.

UI Frameworks

Portfolio

Software Dependencies

Efficiency Principles

Mobile-Friendly Design

File Management

Design and User Experience

Cross-Platform Commands

Modular Architecture

NestJS Guidelines

Modern Web Development

Business Logic

User Management

Software Development Lifecycle

Software Development Practices

Development Guidelines

Accessbility

Forms and Actions

Svelte Documentation

Extreme Programming

Software Maintenance.

Software Development Principles

Software Engineering Principles

Code Review Guidelines

State Management.

Web Optimization

Data Model Creation

WordPress plugin

readability

coding standards

code optimization

type hinting

Component-Based Architecture

Model Quantization

Web Development Tools.

Code Style Guides

Project Scalability

Component Organization.

Error Handling and Feedback

Firebase Best Practices

AI-powered Features

Native-like Features

Consistent Design System

Touch-Friendly UI

Responsive Typography

Animation and Transitions

Software Design Principles

Python Development

Python Coding Standards

Code Best Practices

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: