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#!/usr/bin/env python3
"""
Comprehensive Dataset Analysis Tool
Analyzes all n8n datasets including JSONL and Parquet formats.
Provides detailed statistics, validation, and duplicate detection.
"""
import json
import os
from pathlib import Path
from typing import Dict, List, Any
from collections import defaultdict
try:
import pandas as pd
PANDAS_AVAILABLE = True
except ImportError:
PANDAS_AVAILABLE = False
print("⚠️ pandas not available - Parquet analysis will be skipped")
print(" Install with: pip install pandas pyarrow")
def analyze_jsonl(filepath: Path) -> Dict[str, Any]:
"""Analyze JSONL format dataset."""
print(f"\n📊 Analyzing: {filepath.name}")
print(f" Size: {filepath.stat().st_size / (1024*1024):.2f} MB")
examples = []
errors = []
with open(filepath, 'r', encoding='utf-8') as f:
for line_num, line in enumerate(f, 1):
line = line.strip()
if not line:
continue
try:
examples.append(json.loads(line))
except json.JSONDecodeError as e:
errors.append(f"Line {line_num}: {e}")
if len(errors) < 5: # Only show first 5 errors
print(f" ⚠️ Error on line {line_num}: {e}")
# Analyze structure
fields = set()
if examples:
for ex in examples[:100]: # Sample first 100
fields.update(ex.keys())
print(f" ✅ Valid: {len(examples):,} examples")
print(f" 📝 Fields: {', '.join(sorted(fields))}")
return {
'filename': filepath.name,
'format': 'JSONL',
'size_mb': filepath.stat().st_size / (1024*1024),
'example_count': len(examples),
'fields': sorted(fields),
'errors': errors,
'sample': examples[0] if examples else None
}
def analyze_json_array(filepath: Path) -> Dict[str, Any]:
"""Analyze JSON array format dataset."""
print(f"\n📊 Analyzing: {filepath.name}")
print(f" Size: {filepath.stat().st_size / (1024*1024):.2f} MB")
try:
with open(filepath, 'r', encoding='utf-8') as f:
data = json.load(f)
if not isinstance(data, list):
print(f" ❌ Not a JSON array!")
return None
fields = set()
if data:
for ex in data[:100]:
if isinstance(ex, dict):
fields.update(ex.keys())
print(f" ✅ Valid: {len(data):,} examples")
print(f" 📝 Fields: {', '.join(sorted(fields))}")
return {
'filename': filepath.name,
'format': 'JSON Array',
'size_mb': filepath.stat().st_size / (1024*1024),
'example_count': len(data),
'fields': sorted(fields),
'errors': [],
'sample': data[0] if data else None
}
except Exception as e:
print(f" ❌ Error: {e}")
return None
def analyze_parquet(filepath: Path) -> Dict[str, Any]:
"""Analyze Parquet format dataset."""
if not PANDAS_AVAILABLE:
print(f"\n⚠️ Skipping {filepath.name} - pandas not installed")
return None
print(f"\n📊 Analyzing: {filepath.name}")
print(f" Size: {filepath.stat().st_size / (1024*1024):.2f} MB")
try:
df = pd.read_parquet(filepath)
print(f" ✅ Valid: {len(df):,} examples")
print(f" 📝 Columns: {', '.join(df.columns.tolist())}")
return {
'filename': filepath.name,
'format': 'Parquet',
'size_mb': filepath.stat().st_size / (1024*1024),
'example_count': len(df),
'fields': df.columns.tolist(),
'errors': [],
'sample': df.iloc[0].to_dict() if len(df) > 0 else None
}
except Exception as e:
print(f" ❌ Error: {e}")
return None
def main():
"""Main analysis function."""
print("=" * 70)
print("N8N DATASET COLLECTION ANALYSIS")
print("=" * 70)
datasets_dir = Path(__file__).parent
results = []
# Find all dataset files
jsonl_files = sorted(datasets_dir.glob('*.jsonl'))
json_files = sorted([f for f in datasets_dir.glob('dataset_*.json')])
parquet_files = sorted(datasets_dir.glob('*.parquet'))
print(f"\n📁 Found:")
print(f" - {len(jsonl_files)} JSONL files")
print(f" - {len(json_files)} JSON files")
print(f" - {len(parquet_files)} Parquet files")
# Analyze JSONL files
for filepath in jsonl_files:
result = analyze_jsonl(filepath)
if result:
results.append(result)
# Analyze JSON array files
for filepath in json_files:
result = analyze_json_array(filepath)
if result:
results.append(result)
# Analyze Parquet files
for filepath in parquet_files:
result = analyze_parquet(filepath)
if result:
results.append(result)
# Summary
print("\n" + "=" * 70)
print("COLLECTION SUMMARY")
print("=" * 70)
total_examples = sum(r['example_count'] for r in results)
total_size = sum(r['size_mb'] for r in results)
print(f"\n📦 Total Datasets: {len(results)}")
print(f"📝 Total Examples: {total_examples:,}")
print(f"💾 Total Size: {total_size:.2f} MB ({total_size/1024:.2f} GB)")
# Detailed table
print("\n" + "-" * 70)
print(f"{'Dataset':<45} {'Format':<12} {'Examples':>12}")
print("-" * 70)
for r in sorted(results, key=lambda x: x['example_count'], reverse=True):
print(f"{r['filename']:<45} {r['format']:<12} {r['example_count']:>12,}")
# Field analysis
print("\n" + "=" * 70)
print("FIELD ANALYSIS")
print("=" * 70)
field_counts = defaultdict(int)
for r in results:
for field in r['fields']:
field_counts[field] += 1
print(f"\nCommon fields across datasets:")
for field, count in sorted(field_counts.items(), key=lambda x: x[1], reverse=True):
print(f" {field:<30} (in {count}/{len(results)} datasets)")
# Sample structure
print("\n" + "=" * 70)
print("SAMPLE STRUCTURE")
print("=" * 70)
for r in results[:2]: # Show first 2 samples
if r['sample']:
print(f"\n{r['filename']}:")
print(f" Fields: {list(r['sample'].keys())}")
for key in list(r['sample'].keys())[:3]: # Show first 3 fields
value = str(r['sample'][key])[:100]
print(f" {key}: {value}...")
print("\n" + "=" * 70)
if __name__ == '__main__':
main()
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