Pandas Data Processing Skill

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This skill provides comprehensive pandas data processing capabilities through executable scripts and reference documentation. Use this skill whenever tasks involve data manipulation, cleaning, analysis, or transformation of tabular data.

When to Use This Skill

Activate this skill when the user requests:

• - Data cleaning operations (handling missing values, duplicates, outliers)
• Data analysis and statistical summaries
• Format conversions (CSV ↔ Excel ↔ JSON ↔ Parquet)
• Data transformation (filtering, sorting, aggregation, pivoting)
• Merging or combining multiple datasets
• Generating data quality reports
• Any pandas DataFrame operations

Core Capabilities

1. Data Cleaning (scripts/data_cleaner.py)

Handles common data cleaning tasks with a single command:

Usage:
CODEBLOCK0

Available Options:

• - --remove-duplicates: Remove duplicate rows
• INLINECODE2: Handle missing values

• - --fill-value [value]: Custom fill value for missing data
• INLINECODE10: Remove outliers using IQR or Z-score method
• INLINECODE11: Choose iqr or zscore (default: iqr)
• INLINECODE14: Standardize column names (lowercase, underscores)

Example:
CODEBLOCK1

2. Data Analysis (scripts/data_analyzer.py)

Generates comprehensive data analysis reports:

Usage:
CODEBLOCK2

Available Options:

• - --output, -o [file]: Save report to file
• INLINECODE17: Output format (json or text, default: json)

Report Includes:

• - Basic information (rows, columns, memory usage)
• Data type distribution
• Missing values analysis
• Numeric column statistics (mean, std, min, max, quartiles, skewness, kurtosis)
• Categorical column statistics (unique values, value counts)
• Correlation analysis
• Outlier detection

Example:
CODEBLOCK3

3. Data Transformation (scripts/data_transformer.py)

Performs various data transformation operations through subcommands:

Convert Format

python scripts/data_transformer.py convert input.csv output.xlsx

Merge Files

Filter Data

Sort Data

Select Columns

Reference Documentation

The references/ directory contains detailed documentation:

references/common_operations.md

Comprehensive reference covering:

• - Data reading/saving (CSV, Excel, JSON, SQL, Parquet)
• Data exploration (head, info, describe, dtypes)
• Data selection and filtering (loc, iloc, boolean indexing, query)
• Data cleaning (handling missing/duplicate values, type conversion)
• Data transformation (apply, map, sorting, column operations)
• Groupby and aggregation operations
• Pivot tables
• Merging and joining (concat, merge, join)
• Time series operations
• String operations
• Performance optimization tips

When to use: When Claude needs to understand pandas syntax or find the right method for a specific operation.

references/data_cleaning_best_practices.md

Best practices guide covering:

• - Data quality check checklist
• Missing value handling strategies with decision tree
• Outlier detection methods (IQR, Z-Score, percentile)
• Data type optimization for memory efficiency
• String cleaning techniques
• Date/time standardization
• Complete cleaning pipeline template
• Common problems and solutions
• Data validation methods

When to use: When designing a data cleaning workflow or deciding on the best approach for specific data quality issues.

Workflow Guidelines

Step 1: Initial Assessment

python scripts/data_analyzer.py input_file.csv -o analysis_report.json

Step 2: Plan Cleaning Strategy

• - Identify missing value strategy (reference: data_cleaning_best_practices.md)
• Determine if duplicates should be removed
• Decide on outlier handling approach
• Plan any necessary type conversions

Step 3: Execute Cleaning

Step 4: Transform as Needed

Step 5: Validate Results

Common Patterns

Pattern 1: Quick Data Quality Report

Pattern 2: Standard Cleaning Pipeline

Pattern 3: Excel to CSV with Filtering

Pattern 4: Merge Multiple CSVs

Dependencies

Ensure pandas is installed:
CODEBLOCK17

Optional for specific formats:
CODEBLOCK18

Tips for Effective Use

1. 1. Start with analysis: Always run the analyzer first to understand the data
2. Incremental cleaning: Apply cleaning operations step by step, verify each step
3. Preserve originals: Never overwrite original data files
4. Check references: Consult reference docs for complex operations or best practices
5. Validate results: Use the analyzer to verify cleaning effectiveness
6. Memory efficiency: For large files, consider using the data type optimization techniques in the reference docs
7. Combine operations: Chain multiple transformer commands for complex workflows

Limitations

• - Scripts work with single-machine memory constraints (for very large datasets, consider Dask)
• Time series resampling and rolling operations require custom pandas code
• Complex statistical modeling beyond basic descriptive statistics requires additional libraries
• For advanced visualizations, use matplotlib/seaborn directly

Troubleshooting

Import errors: Ensure pandas and dependencies are installed
Memory errors: Process data in chunks or optimize dtypes (see references)
Encoding issues: Add encoding='utf-8' parameter when loading CSVs
Date parsing issues: Use pd.to_datetime() with explicit format string

For detailed pandas operations and troubleshooting, always refer to references/common_operations.md and references/data_cleaning_best_practices.md.