Data Types and Formats
A guide to working with Pixeltable's type system and supported data formats. Pixeltable provides a unified interface for handling various data types common in AI/ML workflows.
Data Types
Pixeltable Type | Python Type | Description | Example |
---|---|---|---|
pxt.String | str | Text data | {'text': pxt.String} |
pxt.Int | int | Integer numbers | {'count': pxt.Int} |
pxt.Float | float | Floating-point numbers | {'score': pxt.Float} |
pxt.Bool | bool | Boolean values | {'valid': pxt.Bool} |
pxt.Timestamp | datetime.datetime | DateTime values | {'date': pxt.Timestamp} |
pxt.Json | list , dict | Structured data like lists or dictionaries | {'metadata': pxt.Json} |
pxt.Array | np.ndarray | N-dimensional arrays (requires shape and dtype) | {'embedding': pxt.Array[(512,), pxt.Float]} |
Media Types
Pixeltable Type | Python Type | Supported Formats | Common Use Cases |
---|---|---|---|
pxt.Image | PIL.Image.Image | Image File Formats | Computer vision, image processing |
pxt.Video | str (path) | MP4 | Video analysis, frame extraction |
pxt.Audio | str (path) | MP3 | Speech recognition, audio analysis |
pxt.Document | str (path) | PDF, MD, HTML | Text extraction, RAG applications |
For practical examples of working with these types, check out:
- Object Detection in Videos Tutorial
- Document Indexing & RAG Tutorial
- Working with External Files Guide
Creating Tables with Media Types
# Example from Object Detection Tutorial
videos = pxt.create_table('video_analysis', {
'video': pxt.Video,
'thumbnail': pxt.Image[(224, 224), 'RGB'], # With constraints
'transcript': pxt.Document,
'audio': pxt.Audio
})
Specialized Types
Array Type (Essential for ML Models)
# Example from RAG Tutorial
embeddings = pxt.create_table('embeddings', {
'text': pxt.String,
'vector': pxt.Array[(768,), pxt.Float] # For embeddings
})
# For variable-sized tensors
features = pxt.create_table('features', {
'tensor': pxt.Array[(None, 512), pxt.Float] # Dynamic first dimension
})
JSON Type (For Structured Data)
# Example from Object Detection Tutorial
detections = pxt.create_table('detections', {
'model_output': pxt.Json, # Stores bounding boxes, labels, scores
'metadata': pxt.Json
})
Working with Types
Image Type Constraints
# From Computer Vision Examples
images = pxt.create_table('image_processing', {
'original': pxt.Image, # Any valid image
'thumbnail': pxt.Image[(224, 224)], # ResNet input size
'rgb_image': pxt.Image['RGB'],
'grayscale': pxt.Image['L']
})
Media File Handling
# Various input formats supported
t.insert({'image': 'https://example.com/image.jpg'}) # URLs
t.insert({'document': '/path/to/doc.pdf'}) # Local files
t.insert({'video': 's3://my-bucket/video.mp4'}) # Cloud storage
Data Import/Export
Import Operations
import pixeltable as pxt
# CSV Import with type overrides
df = pxt.io.import_csv(
'customers',
'path/to/data.csv',
schema_overrides={
'balance': pxt.Float,
'status': pxt.String
}
)
# Excel Import
sales = pxt.io.import_excel(
'sales_data', # Table name
'sales_2024.xlsx', # File path or URL
schema_overrides={ # Optional type overrides
'revenue': pxt.Float,
'category': pxt.String
}
)
# Parquet Import
transactions = pxt.io.import_parquet(
'transactions', # Table name
'transactions.parquet', # File path or URL
schema_overrides={
'amount': pxt.Float,
'timestamp': pxt.Timestamp
}
)
# Hugging Face Dataset Import
from datasets import load_dataset
dataset = load_dataset('mnist', split='train[:1000]')
mnist = pxt.io.import_huggingface_dataset(
'mnist_samples',
dataset,
schema_overrides={
'image': pxt.Image,
'label': pxt.Int
}
)
For more details on integrating with Hugging Face, see our Hugging Face Integration Guide.
Export Formats
# To pandas DataFrame
df = t.collect().to_pandas()
# To PyTorch Dataset (from Computer Vision Tutorial)
dataset = t.to_pytorch_dataset(
features=['image'],
labels=['label']
)
# To COCO format (for object detection)
coco_data = t.to_coco_dataset()
Important Notes for Users
-
Media Files:
- Pixeltable handles downloading, caching, and format validation
- Local paths are managed automatically
- Supports URLs, local paths, and cloud storage (S3)
- See Working with External Files
-
Arrays:
- Must specify shape and dtype
- Use
None
for dynamic dimensions - Common in ML workflows for embeddings and features
- See examples in RAG Operations Tutorial
-
JSON Data:
- Flexible storage for structured data
- Commonly used for model outputs and metadata
- Supports nested structures
- See examples in Object Detection Tutorial
Additional Resources
Core Documentation
Integration Guides
Updated 22 days ago