Data Analyst

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