n-201720027/nulls-data-science-and-machine-learning-assistant-75

You are an experienced data scientist who specializes in Python-based
data science and machine learning. You use the following tools:
- Python 3 as the primary programming language
- PyTorch for deep learning and neural networks
- NumPy for numerical computing and array operations
- Pandas for data manipulation and analysis
- Jupyter for interactive development and visualization
- Conda for environment and package management
- Matplotlib for data visualization and plotting

pytorch-rules

- You are a PyTorch ML engineer
- Use type hints consistently
- Optimize for readability over premature optimization
- Write modular code, using separate files for models, data loading, training, and evaluation
- Follow PEP8 style guide for Python code

data-science-rules

You are an experienced data scientist who specializes in Python-based
data science and machine learning. You use the following tools:
- Python 3 as the primary programming language
- PyTorch for deep learning and neural networks
- NumPy for numerical computing and array operations
- Pandas for data manipulation and analysis
- Jupyter for interactive development and visualization
- Conda for environment and package management
- Matplotlib for data visualization and plotting

Docs

Learn more

Condahttps://docs.conda.io/en/latest/

Jupyterhttps://docs.jupyter.org/en/latest/

Matplotlibhttps://matplotlib.org/stable/

NumPyhttps://numpy.org/doc/stable/

Pandashttps://pandas.pydata.org/docs/

Pythonhttps://docs.python.org/3/

PyTorchhttps://pytorch.org/docs/stable/index.html

torch.nn Docshttps://pytorch.org/docs/stable/nn.html

Pandashttps://pandas.pydata.org/docs/

NumPyhttps://numpy.org/doc/stable/

Prompts

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Exploratory Data Analysis

Initial data exploration and key insights

Create an exploratory data analysis workflow that includes:

Data Overview:
- Basic statistics (mean, median, std, quartiles)
- Missing values and data types
- Unique value distributions

Visualizations:
- Numerical: histograms, box plots
- Categorical: bar charts, frequency plots
- Relationships: correlation matrices
- Temporal patterns (if applicable)

Quality Assessment:
- Outlier detection
- Data inconsistencies
- Value range validation

Insights & Documentation:
- Key findings summary
- Data quality issues
- Variable relationships
- Next steps recommendations
- Reproducible Jupyter notebook

The user has provided the following information:

Data Pipeline Development

Create robust and scalable data processing pipelines

Generate a data processing pipeline with these requirements:

Input:
- Data loading from multiple sources (CSV, SQL, APIs)
- Input validation and schema checks
- Error logging for data quality issues

Processing:
- Standardized cleaning (missing values, outliers, types)
- Memory-efficient operations for large datasets
- Numerical transformations using NumPy
- Feature engineering and aggregations

Quality & Monitoring:
- Data quality checks at key stages
- Validation visualizations with Matplotlib
- Performance monitoring

Structure:
- Modular, documented code with error handling
- Configuration management
- Reproducible in Jupyter notebooks
- Example usage and tests

The user has provided the following information:

New Module

Create a new PyTorch module

Please create a new PyTorch module following these guidelines:
- Include docstrings for the model class and methods
- Add type hints for all parameters
- Add basic validation in __init__

Page

Creates a new Next.js page based on the description provided.

Create a new Next.js page based on the following description.

Data Pipeline Development

Create robust and scalable data processing pipelines

Generate a data processing pipeline with these requirements:

Input:
- Data loading from multiple sources (CSV, SQL, APIs)
- Input validation and schema checks
- Error logging for data quality issues

Processing:
- Standardized cleaning (missing values, outliers, types)
- Memory-efficient operations for large datasets
- Numerical transformations using NumPy
- Feature engineering and aggregations

Quality & Monitoring:
- Data quality checks at key stages
- Validation visualizations with Matplotlib
- Performance monitoring

Structure:
- Modular, documented code with error handling
- Configuration management
- Reproducible in Jupyter notebooks
- Example usage and tests

The user has provided the following information:

Exploratory Data Analysis

Initial data exploration and key insights

Create an exploratory data analysis workflow that includes:

Data Overview:
- Basic statistics (mean, median, std, quartiles)
- Missing values and data types
- Unique value distributions

Visualizations:
- Numerical: histograms, box plots
- Categorical: bar charts, frequency plots
- Relationships: correlation matrices
- Temporal patterns (if applicable)

Quality Assessment:
- Outlier detection
- Data inconsistencies
- Value range validation

Insights & Documentation:
- Key findings summary
- Data quality issues
- Variable relationships
- Next steps recommendations
- Reproducible Jupyter notebook

The user has provided the following information:

Context

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@code

Reference specific functions or classes from throughout your project

@docs

Reference the contents from any documentation site

@diff

Reference all of the changes you've made to your current branch

@terminal

Reference the last command you ran in your IDE's terminal and its output

@problems

Get Problems from the current file

@folder

Uses the same retrieval mechanism as @Codebase, but only on a single folder

@codebase

Reference the most relevant snippets from your codebase

@currentFile

Reference the currently open file

@open

Reference the contents of all of your open files

@clipboard

Reference recent clipboard items

Data

Learn more

Azure Blob Storage

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MCP Servers

Learn more

Memory

npx -y @modelcontextprotocol/server-memory

Exa

npx -y exa-mcp-server

Playwright

npx -y @executeautomation/playwright-mcp-server