a5c-ai pandas-dataframe-analyzer
Overview
Automated DataFrame analysis skill for statistical summaries, missing value detection, data type inference, and memory optimization recommendations using pandas and profiling libraries.
Capabilities
- Statistical profiling of DataFrames
- Missing value pattern detection
- Data type optimization suggestions
- Memory footprint analysis
- Duplicate detection and handling
- Distribution analysis and visualization
- Correlation matrix computation
- Cardinality analysis for categorical features
Target Processes
- Exploratory Data Analysis (EDA) Pipeline
- Data Collection and Validation Pipeline
- Feature Engineering Design and Implementation
Tools and Libraries
- pandas
- pandas-profiling / ydata-profiling
- numpy
- scipy (for statistical tests)
Input Schema
{
"type": "object",
"required": ["dataPath"],
"properties": {
"dataPath": {
"type": "string",
"description": "Path to the data file (CSV, Parquet, JSON)"
},
"sampleSize": {
"type": "integer",
"description": "Number of rows to sample for analysis",
"default": 10000
},
"profileType": {
"type": "string",
"enum": ["minimal", "standard", "full"],
"default": "standard"
},
"outputFormat": {
"type": "string",
"enum": ["json", "html", "markdown"],
"default": "json"
}
}
}
Output Schema
{
"type": "object",
"required": ["summary", "columns", "recommendations"],
"properties": {
"summary": {
"type": "object",
"properties": {
"rowCount": { "type": "integer" },
"columnCount": { "type": "integer" },
"memoryUsageMB": { "type": "number" },
"duplicateRows": { "type": "integer" },
"missingCells": { "type": "integer" },
"missingCellsPercent": { "type": "number" }
}
},
"columns": {
"type": "array",
"items": {
"type": "object",
"properties": {
"name": { "type": "string" },
"dtype": { "type": "string" },
"nullCount": { "type": "integer" },
"uniqueCount": { "type": "integer" },
"stats": { "type": "object" }
}
}
},
"recommendations": {
"type": "array",
"items": {
"type": "object",
"properties": {
"type": { "type": "string" },
"column": { "type": "string" },
"suggestion": { "type": "string" },
"impact": { "type": "string" }
}
}
}
}
}
Usage Example
{
kind: 'skill',
title: 'Analyze training dataset',
skill: {
name: 'pandas-dataframe-analyzer',
context: {
dataPath: 'data/train.csv',
profileType: 'full',
outputFormat: 'json'
}
}
}