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Load structured data from JSON files into Pixeltable tables for processing and analysis.

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Problem

You have data in JSON format—from APIs, exports, or application logs. You need to load this data for processing with AI models or combining with other data sources.

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Solution

What’s in this recipe:
  • Import JSON files directly into tables
  • Import from URLs (APIs, remote files)
  • Handle nested JSON structures
You use pxt.create_table() with a source parameter to create a table from a JSON file or URL. The JSON must be an array of objects, where each object becomes a row.

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Setup

%pip install -qU pixeltable

import pixeltable as pxt
import json
import tempfile
from pathlib import Path

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Create sample JSON file

First, create a sample JSON file to demonstrate the import process: 
# Create sample JSON data (array of objects)
sample_data = [
    {'id': 1, 'title': 'Introduction to ML', 'author': 'Alice', 'tags': ['ml', 'intro'], 'rating': 4.5},
    {'id': 2, 'title': 'Deep Learning Basics', 'author': 'Bob', 'tags': ['dl', 'neural'], 'rating': 4.8},
    {'id': 3, 'title': 'NLP Fundamentals', 'author': 'Carol', 'tags': ['nlp', 'text'], 'rating': 4.2},
    {'id': 4, 'title': 'Computer Vision', 'author': 'Dave', 'tags': ['cv', 'images'], 'rating': 4.6},
    {'id': 5, 'title': 'Reinforcement Learning', 'author': 'Eve', 'tags': ['rl', 'agents'], 'rating': 4.3}
]

# Save to temporary JSON file
temp_dir = tempfile.mkdtemp()
json_path = Path(temp_dir) / 'articles.json'

with open(json_path, 'w') as f:
    json.dump(sample_data, f, indent=2)

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Import JSON file

Use create_table with source to create a table directly from a JSON file: 
# Create a fresh directory
pxt.drop_dir('json_demo', force=True)
pxt.create_dir('json_demo')

Connected to Pixeltable database at: postgresql+psycopg://postgres:@/pixeltable?host=/Users/pjlb/.pixeltable/pgdata
Created directory ‘json_demo’.
<pixeltable.catalog.dir.Dir at 0x1556b2800>
# Import JSON file into a new table
articles = pxt.create_table(
    'json_demo.articles',
    source=str(json_path),
    source_format='json'  # Explicitly specify format when using local file paths
)

Created table ‘articles’.Inserting rows into `articles`: 0 rows [00:00, ? rows/s]
Inserting rows into `articles`: 5 rows [00:00, 538.52 rows/s]
Inserted 5 rows with 0 errors.
# View imported data
articles.collect()

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Import from URL

You can import JSON directly from a URL—useful for APIs and remote data: 
# Import from a public JSON URL
# Using JSONPlaceholder API as an example
posts = pxt.create_table(
    'json_demo.posts',
    source='https://jsonplaceholder.typicode.com/posts',
    source_format='json'  # Required for URL sources
)

Created table ‘posts’.Inserting rows into `posts`: 0 rows [00:00, ? rows/s]
Inserting rows into `posts`: 100 rows [00:00, 15623.57 rows/s]
Inserted 100 rows with 0 errors.
# View first few rows
posts.head(5)

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Import from Python dictionaries

Use create_table with a list of dictionaries as source—useful when you have data in memory: 
# Import from a list of dictionaries
events = [
    {'event': 'page_view', 'user_id': 101, 'timestamp': '2024-01-15T10:30:00'},
    {'event': 'click', 'user_id': 101, 'timestamp': '2024-01-15T10:31:00'},
    {'event': 'purchase', 'user_id': 102, 'timestamp': '2024-01-15T10:32:00'},
]

event_table = pxt.create_table('json_demo.events', source=events)

Created table ‘events’.Inserting rows into `events`: 0 rows [00:00, ? rows/s]
Inserting rows into `events`: 3 rows [00:00, 988.06 rows/s]
Inserted 3 rows with 0 errors.
# View imported events
event_table.collect()

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Add computed columns

Once imported, you can enrich the data with computed columns: 
# Add a computed column combining title and author
articles.add_computed_column(
    summary=articles.title + ' by ' + articles.author
)

Added 5 column values with 0 errors.
5 rows updated, 10 values computed.
# View with computed column
articles.select(articles.title, articles.author, articles.summary).collect()

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Explanation

JSON format requirements: The JSON file must contain an array of objects at the top level: 
[
  {"col1": "value1", "col2": 123},
  {"col1": "value2", "col2": 456}
]
Source types supported:
Nested JSON handling: Nested objects and arrays are stored as JSON columns. You can access nested fields using Pixeltable’s JSON path syntax in computed columns.

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See also