pandas-dataframe-analyzer

name	pandas-dataframe-analyzer
description	Automated DataFrame analysis skill for statistical summaries, missing value detection, data type inference, and memory optimization recommendations.
allowed-tools	["Read","Write","Bash","Glob","Grep"]
graph	{"domains":["domain:data-science"],"specializations":["specialization:data-science-ml"],"skillAreas":["skill-area:data-analysis","skill-area:data-preprocessing"],"roles":["role:data-scientist","role:data-analyst"],"workflows":["workflow:experiment-design","workflow:data-quality-monitoring"]}

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"
       
    
     
       
         
       
    
  

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" 
            
            
      
    
     
       
       
         
         
              
              
              
              
              
        
      
    
     
       
       
         
         
              
              
              
              
        
      
    
  

Usage Example

{
  kind: 'skill',
  title: 'Analyze training dataset',
  skill: {
    name: 'pandas-dataframe-analyzer',
    context: {
      dataPath: 'data/train.csv',
      profileType: 'full',
      outputFormat: 'json'
    }
  }
}