docs/api/index.md
Welcome to Daft Python API Documentation. For Daft User Guide, head to User Guide.
<div class="grid cards" markdown>Functions and interfaces for building AI applications with daft. This includes providers and various model inference protocols such as text embedding.
Variety of approaches to creating a DataFrame from reading various data sources like in-memory data, files, data catalogs, and integrations and writing to various data sources.
Available DataFrame methods that are enqueued in the DataFrame's internal query plan and executed when Execution DataFrame methods are called.
Simple, performant, and responsible ways to access useful datasets like Common Crawl.
Expressions allow you to express some computation that needs to happen in a DataFrame.
Daft provides a set of built-in operations that can be applied to DataFrame columns.
User-Defined Functions (UDFs) are a mechanism to run Python code on the data that lives in a DataFrame.
Window functions allow you to perform calculations across a set of rows that are related to the current row.
Sessions enable you to attach catalogs, tables, and create temporary objects which are accessible through both the Python and SQL APIs.
Daft integrates with various catalog implementations using its Catalog and Table interfaces to manage catalog objects like tables and namespaces.
Daft can display your DataFrame's schema without materializing it by performing intelligent sampling of your data to determine appropriate schema.
Daft provides simple DataTypes that are ubiquituous in many DataFrames such as numbers, strings, dates, tensors, and images.
When performing aggregations such as sum, mean and count, Daft enables you to group data by certain keys and aggregate within those keys.
Series expose methods which invoke high-performance kernels for manipulation of a column of data.
Configure the execution backend, Daft in various ways during execution, and how Daft interacts with storage.