Skip to main content

​
Working with Data in Pixeltable

Pixeltable provides a unified interface for working with diverse data types - from structured tables to unstructured media files. This guide covers everything you need to know about bringing your data into Pixeltable.

​
Direct Import with Table Creation and Insertion

Pixeltable supports importing data directly during table creation and insertion operations. This streamlines the process of loading data into Pixeltable tables from external sources. Pixeltable supports importing from a variety of data sources:
  • CSV files (.csv)
  • Excel files (.xls, .xlsx)
  • Parquet files (.parquet, .pq, .parq)
  • JSON files (.json)
  • Pandas DataFrames
  • Pixeltable DataFrames
  • Hugging Face datasets
  • Row data structures or Iterators

​
Creating Tables from External Sources

You can create a table directly from an external data source using the source parameter in the create_table function. Pixeltable will automatically infer the schema from the source data. 
import pixeltable as pxt
import pandas as pd

# Create from CSV file
table = pxt.create_table('from_csv', source='https://raw.githubusercontent.com/pixeltable/pixeltable/main/docs/resources/world-population-data.csv')

# Create from pandas DataFrame
df = pd.DataFrame({
    'col1': [1, 2, 3],
    'col2': ['a', 'b', 'c']
})
table = pxt.create_table('from_df', source=df)
You can also provide schema overrides for more control: 
import pixeltable as pxt

# With schema overrides
table = pxt.create_table(
    'from_csv_with_overrides', 
    source='https://raw.githubusercontent.com/pixeltable/pixeltable/main/docs/resources/world-population-data.csv',
    schema_overrides={'pop_2023': pxt.Required[pxt.Int]}
)

​
Inserting Data from External Sources

You can also insert data from external sources into existing tables using the insert method: 
import pixeltable as pxt
import pandas as pd

# Insert from CSV file
table.insert('https://raw.githubusercontent.com/pixeltable/pixeltable/main/docs/resources/world-population-data.csv')

# Insert from pandas DataFrame
df = pd.DataFrame({
    'col1': [4, 5, 6],
    'col2': ['d', 'e', 'f']
})
table.insert(df)

​
Supported Data Types & Formats

import pixeltable as pxt
from datetime import datetime

# Create table with basic data types
basic_table = pxt.create_table('myproject.basic_types', {
    'id': pxt.Int,                  # Integer values
    'score': pxt.Float,             # Floating point numbers  
    'active': pxt.Bool,             # Boolean values
    'name': pxt.String,             # Text data
    'created_at': pxt.Timestamp     # DateTime values
})

# Insert single row
basic_table.insert([
    {
        'id': 1,
        'score': 95.5,
        'active': True,
        'name': 'Example',
        'created_at': datetime.now()
    }
])

# Batch insert
basic_table.insert([
    {
        'id': 2,
        'score': 88.3,
        'active': True,
        'name': 'Test A',
        'created_at': datetime(2024, 1, 1)
    },
    {
        'id': 3, 
        'score': 76.9,
        'active': False,
        'name': 'Test B',
        'created_at': datetime(2024, 1, 15)
    }
])

# Import from CSV
pxt.io.import_csv('myproject.from_csv', 'data.csv', 
    schema_overrides={
        'id': pxt.Int,
        'score': pxt.Float,
        'active': pxt.Bool,
        'created_at': pxt.Timestamp
    }
)

# Import from Excel
pxt.io.import_excel('myproject.from_excel', 'data.xlsx',
    schema_overrides={
        'id': pxt.Int,
        'score': pxt.Float,
        'active': pxt.Bool,
        'created_at': pxt.Timestamp
    }
)

# Create table with array columns
array_table = pxt.create_table('myproject.arrays', {
    'fixed_vector': pxt.Array[(768,), pxt.Float],      # Fixed-size vector
    'variable_tensor': pxt.Array[(None, 512), pxt.Float], # Variable first dimension
    'any_int_array': pxt.Array[pxt.Int],               # Any-shaped integer array
    'any_float_array': pxt.Array[pxt.Float],           # Any-shaped float array
    'completely_flexible': pxt.Array                    # Any array (shape and dtype)
})

# Insert array data
array_table.insert([{
    'fixed_vector': np.random.randn(768),
    'variable_tensor': np.random.randn(5, 512),
    'any_int_array': np.array([1, 2, 3, 4]),          # 1D array
    'any_float_array': np.random.randn(3, 3),         # 2D array
    'completely_flexible': np.array([[1, 2], [3, 4]])  # Any numpy array
}])

# Create table with JSON columns
json_table = pxt.create_table('myproject.json_data', {
    'metadata': pxt.Json,           # Arbitrary JSON data
    'config': pxt.Json,             # Configuration objects
    'features': pxt.Json            # Nested structures
})

# Insert structured data
json_table.insert([
    {
        'metadata': {
            'source': 'api',
            'version': '1.0',
            'tags': ['test', 'example']
        },
    'config': {
        'mode': 'testing',
        'parameters': {
            'batch_size': 32,
            'learning_rate': 0.001
        }
        },
        'features': {
            'numeric': [1, 2, 3],
            'categorical': {
                'color': 'red',
                'size': 'large'
            }
        }
    }
])

# Import JSON file
pxt.io.import_json('myproject.from_json', 'data.json',
    schema_overrides={
        'metadata': pxt.Json,
        'config': pxt.Json
    }
)

# Create table with image columns
image_table = pxt.create_table('myproject.images', {
    'original': pxt.Image,                  # Any valid image
    'thumbnail': pxt.Image[(224, 224)],     # Fixed size constraint
    'rgb_only': pxt.Image['RGB'],           # Mode constraint
    'thumbnail_rgb': pxt.Image[(64, 64), 'RGB']  # Both constraints
})

# Insert local files
image_table.insert([
    {'original': '/path/to/image1.jpg'},
    {'original': '/path/to/image2.png'},
    {'original': '/path/to/image3.heic'}
])

# Insert URLs
image_table.insert([
    {'original': 'https://example.com/image1.jpg'},
    {'original': 'https://example.com/image2.png'}
])

# Insert from cloud storage
image_table.insert([
    {'original': 's3://my-bucket/image1.jpg'}
])

# Create table with video columns
video_table = pxt.create_table('myproject.videos', {
    'video': pxt.Video,            # Video file reference
})

# Insert local video files
video_table.insert([
    {'video': '/path/to/video1.mp4'},
    {'video': '/path/to/video2.webm'}
])

# Insert video URLs
video_table.insert([
    {'video': 'https://example.com/video1.mp4'},
    {'video': 'https://example.com/video2.webm'}
])

# Insert from cloud storage
video_table.insert([
    {'video': 's3://my-bucket/video1.mp4'}
])

# Create table with audio columns
audio_table = pxt.create_table('myproject.audio', {
    'audio': pxt.Audio,           # Audio file reference
})

# Insert local audio files
audio_table.insert([
    {'audio': '/path/to/audio1.mp3'},
    {'audio': '/path/to/audio2.wav'}
])

# Insert audio URLs
audio_table.insert([
    {'audio': 'https://example.com/audio1.mp3'},
    {'audio': 'https://example.com/audio2.wav'}
])

# Insert from cloud storage
audio_table.insert([
    {'audio': 's3://my-bucket/audio1.mp3'}
])

# Create table with document columns
doc_table = pxt.create_table('myproject.documents', {
    'document': pxt.Document,     # Document file reference
})

# Insert local documents
doc_table.insert([
    {'document': '/path/to/doc1.pdf'},
    {'document': '/path/to/doc2.docx'},
    {'document': '/path/to/text1.md'}
])

# Insert document URLs
doc_table.insert([
    {'document': 'https://example.com/doc1.pdf'},
    {'document': 'https://example.com/doc2.docx'}
])

# Insert from cloud storage
doc_table.insert([
    {'document': 's3://my-bucket/doc1.pdf'}
])

​
Import Functions (Alternative Approach)

# Basic CSV import with schema inference
table1 = pxt.io.import_csv(
    'myproject.data',
    'data.csv'
)

# CSV import with schema overrides
table2 = pxt.io.import_csv(
    'myproject.data_typed',
    'data.csv',
    schema_overrides={
        'id': pxt.Int,
        'name': pxt.String,
        'score': pxt.Float,
        'active': pxt.Bool,
        'created_at': pxt.Timestamp
    }
)

# CSV import with pandas options
table3 = pxt.io.import_csv(
    'myproject.data_options',
    'data.csv',
    sep=';',                # Custom separator
    encoding='utf-8',       # Specify encoding
    na_values=['NA', '-'],  # Custom NA values
    parse_dates=['date']    # Parse date columns
)

# Basic Excel import
table1 = pxt.io.import_excel(
    'myproject.excel_data',
    'data.xlsx'
)

# Excel import with schema overrides
table2 = pxt.io.import_excel(
    'myproject.excel_typed',
    'data.xlsx',
    schema_overrides={
        'id': pxt.Int,
        'amount': pxt.Float,
        'date': pxt.Timestamp
    }
)

# Excel import with options
table3 = pxt.io.import_excel(
    'myproject.excel_options',
    'data.xlsx',
    sheet_name='Sheet2',    # Specific sheet
    header=1,               # Header row
    na_values=['NA', '-'],  # Custom NA values
    parse_dates=['date']    # Parse date columns
)

# Basic Parquet import
table1 = pxt.io.import_parquet(
    table='myproject.parquet_data',
    parquet_path='data.parquet'
)

# Parquet import with schema overrides
table2 = pxt.io.import_parquet(
    table='myproject.parquet_typed',
    parquet_path='data.parquet',
    schema_overrides={
        'features': pxt.Array[(100,), pxt.Float],
        'labels': pxt.Array[(10,), pxt.Int]
    }
)

# Import from directory of Parquet files
table3 = pxt.io.import_parquet(
    table='myproject.parquet_dir',
    parquet_path='data/*.parquet'  # Glob pattern
)

# Basic JSON import
table1 = pxt.io.import_json(
    'myproject.json_data',
    'data.json'
)

# JSON import with schema overrides
table2 = pxt.io.import_json(
    'myproject.json_typed',
    'data.json',
    schema_overrides={
        'metadata': pxt.Json,
        'features': pxt.Array[(None,), pxt.Float]
    }
)

# Import from URL
table3 = pxt.io.import_json(
    'myproject.json_url',
    'https://api.example.com/data.json'
)

import datasets

# Load Hugging Face dataset
dataset = load_dataset('mnist', split='train[:1000]')

# Import with default schema inference
table1 = pxt.io.import_huggingface_dataset(
    'myproject.hf_data',
    dataset
)

# Import with schema overrides
table2 = pxt.io.import_huggingface_dataset(
    'myproject.hf_typed',
    dataset,
    schema_overrides={
        'image': pxt.Image,
        'label': pxt.Int
    }
)

# Import with split information
table3 = pxt.io.import_huggingface_dataset(
    'myproject.hf_split',
    dataset,
    column_name_for_split='split_info'
)

​
Key Points

  • All media types (Image, Video, Audio, Document) support local files, URLs, and cloud storage paths
  • Array types require explicit shape and dtype specifications
  • JSON type can store any valid JSON data structure
  • Basic types (Int, Float, Bool, String, Timestamp) match their Python equivalents
  • Import functions support schema overrides to ensure correct type assignment
  • Use batch inserts for better performance when adding multiple rows
  • Cloud storage paths (s3://) require appropriate credentials to be configured
  • Tables can be created directly from CSV, Excel, Parquet files, and pandas DataFrames using the source parameter
  • Existing tables can import data directly from external sources using the insert method
  • Schema inference is automatic when importing from external sources, with optional schema overrides
⌘I