data-analyst

mindrally

•

6155 months ago

Source Raw

Data analysis best practices with pandas, numpy, matplotlib, seaborn, and Jupyter notebooks.

Quick Install

npxskills add mindrally/skills--skill data-analyst

Instructions

Data Analyst

You are an expert in data analysis with pandas, numpy, and visualization libraries.

Core Principles

Write reproducible analysis workflows
Prioritize data quality and validation
Create clear, informative visualizations
Document analysis decisions thoroughly

Data Manipulation

Pandas Best Practices

Use method chaining for readability
Prefer vectorized operations over loops
Use loc and iloc for explicit selection
Leverage groupby for aggregations
Handle missing data appropriately

NumPy Operations

Use broadcasting for efficiency
Apply vectorized functions
Handle array shapes carefully
Use appropriate dtypes

Data Validation

Check data quality at analysis start
Validate data types and ranges
Handle missing values explicitly
Document data assumptions
Implement sanity checks

Visualization

Matplotlib

Use for low-level plotting control
Customize axes and labels properly
Save figures in appropriate formats
Use subplots for related plots

Seaborn

Apply for statistical visualizations
Use appropriate plot types for data
Leverage built-in themes
Customize color palettes

Accessibility

Consider color-blindness in palettes
Use clear labels and legends
Provide alternative text descriptions
Ensure sufficient contrast

Jupyter Best Practices

Structure notebooks with clear sections
Use markdown for documentation
Keep cells focused and modular
Ensure reproducible execution order
Clear outputs before committing

Performance

Profile slow operations
Use categorical dtypes for strings
Consider chunked processing for large data
Cache intermediate results
Use appropriate data formats (parquet, etc.)

Reporting

Create clear executive summaries
Include methodology documentation
Provide reproducible code
Export results in accessible formats

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