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The Lightdash Python SDK lets you query your semantic layer directly from Python. Use it in Jupyter notebooks, Python scripts, or anywhere you use Python to ensure everyone pulls from a single source of truth.

See it in action

Try the getting started Jupyter notebook for a hands-on walkthrough.

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Installation

pip install lightdash

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Quick start

from lightdash import Client

client = Client(
    instance_url="https://app.lightdash.cloud",
    access_token="your-token",
    project_uuid="your-uuid",
)
model = client.get_model("orders")

# Build and execute a query
result = (
    model.query()
    .metrics(model.metrics.revenue, model.metrics.profit)
    .dimensions(model.dimensions.country)
    .filter(model.dimensions.status == "active")
    .sort(model.metrics.revenue.desc())
    .limit(100)
    .execute()
)

# Get results as a DataFrame
df = result.to_df()

# Or as a list of dictionaries
records = result.to_records()

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Filters

Use Python comparison operators on dimensions to create filters: 
# Equality and inequality
model.dimensions.country == "USA"
model.dimensions.country != "USA"

# Numeric comparisons
model.dimensions.amount > 1000
model.dimensions.amount >= 1000
model.dimensions.amount < 500
model.dimensions.amount <= 500

# String operations
model.dimensions.name.starts_with("John")
model.dimensions.name.ends_with("son")
model.dimensions.name.contains("Smith")

# List membership
model.dimensions.country.in_(["USA", "UK", "Canada"])

# Null checks
model.dimensions.email.is_null()
model.dimensions.email.is_not_null()

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Combining filters

Use & (AND) and | (OR) to combine filters: 
# AND
f = (model.dimensions.country == "USA") & (model.dimensions.amount > 1000)

# OR
f = (model.dimensions.status == "active") | (model.dimensions.status == "pending")

# Complex combinations
f = (
    (model.dimensions.country == "USA") &
    ((model.dimensions.amount > 1000) | (model.dimensions.priority == "high"))
)

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Raw SQL

Execute SQL queries directly against your data warehouse: 
result = client.sql("SELECT * FROM orders WHERE status = 'active' LIMIT 100")
df = result.to_df()
For detailed documentation, see the SDK reference on GitHub.