Daft API Documentation#
Welcome to Daft Python API Documentation. For Daft User Guide, head to User Guide.
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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.
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Available DataFrame methods that are enqueued in the DataFrame's internal query plan and executed when Execution DataFrame methods are called.
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Expressions allow you to express some computation that needs to happen in a DataFrame.
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Daft provides a set of built-in operations that can be applied to DataFrame columns.
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User-Defined Functions (UDFs) are a mechanism to run Python code on the data that lives in a DataFrame.
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Window functions allow you to perform calculations across a set of rows that are related to the current row.
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Sessions enable you to attach catalogs, tables, and create temporary objects which are accessible through both the Python and SQL APIs.
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Daft integrates with various catalog implementations using its Catalog and Table interfaces to manage catalog objects like tables and namespaces.
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Daft can display your DataFrame's schema without materializing it by performing intelligent sampling of your data to determine appropriate schema.
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Daft provides simple DataTypes that are ubiquituous in many DataFrames such as numbers, strings, dates, tensors, and images.
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When performing aggregations such as sum, mean and count, Daft enables you to group data by certain keys and aggregate within those keys.
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Series expose methods which invoke high-performance kernels for manipulation of a column of data.
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Configure the execution backend, Daft in various ways during execution, and how Daft interacts with storage.