Building Tables and Schemas with PyArrow
To efficiently analyze and process structured data, you often need to organize multiple columnar arrays into a single, coherent table. In the previous chapter, you learned how to create and inspect PyArrow arrays — each representing a single column of data. However, real-world datasets are rarely just a single field; they typically consist of many columns, each potentially with its own data type and null values. Bringing these arrays together as columns within a table allows you to work with entire datasets in a way that's both memory-efficient and highly performant, leveraging Arrow's columnar model.
You can create a pyarrow.Table by combining several PyArrow arrays, each representing one column. When building a table, you specify a list of arrays and a corresponding list of column names. The table then acts as a logical grouping of these arrays, where each array becomes a column in the table. This approach allows you to keep your data organized and accessible by name, making it easy to select columns, filter rows, or perform vectorized operations across your dataset.
1234567891011import pyarrow as pa # Create three arrays representing different columns names = pa.array(["Alice", "Bob", "Charlie"]) ages = pa.array([25, 30, 22]) scores = pa.array([85.5, 92.0, 88.0]) # Build a table from the arrays, specifying column names table = pa.Table.from_arrays([names, ages, scores], names=["name", "age", "score"]) print(table)
A pyarrow.Schema describes the structure of a table in Arrow. It defines the column names, their data types, and any additional metadata. You can explicitly create a schema and use it when constructing a table, or you can inspect the schema of an existing table to understand its structure. Schemas help ensure data consistency and make it easier to share table layouts between systems or codebases.
After creating a table, you can inspect its schema to see the column names, data types, and any attached metadata. This is especially useful for validating that your data is structured as intended. Building on the previous example, you can access the schema property of a table to view its structure. The schema not only shows you the types of each column but also provides a foundation for enforcing data contracts and interoperability between systems.
123456# Inspect the schema of the table print(table.schema) # Accessing metadata (if any) and column information print("Table columns:", table.column_names) print("First column type:", table.schema.field("name").type)
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To efficiently analyze and process structured data, you often need to organize multiple columnar arrays into a single, coherent table. In the previous chapter, you learned how to create and inspect PyArrow arrays — each representing a single column of data. However, real-world datasets are rarely just a single field; they typically consist of many columns, each potentially with its own data type and null values. Bringing these arrays together as columns within a table allows you to work with entire datasets in a way that's both memory-efficient and highly performant, leveraging Arrow's columnar model.
You can create a pyarrow.Table by combining several PyArrow arrays, each representing one column. When building a table, you specify a list of arrays and a corresponding list of column names. The table then acts as a logical grouping of these arrays, where each array becomes a column in the table. This approach allows you to keep your data organized and accessible by name, making it easy to select columns, filter rows, or perform vectorized operations across your dataset.
1234567891011import pyarrow as pa # Create three arrays representing different columns names = pa.array(["Alice", "Bob", "Charlie"]) ages = pa.array([25, 30, 22]) scores = pa.array([85.5, 92.0, 88.0]) # Build a table from the arrays, specifying column names table = pa.Table.from_arrays([names, ages, scores], names=["name", "age", "score"]) print(table)
A pyarrow.Schema describes the structure of a table in Arrow. It defines the column names, their data types, and any additional metadata. You can explicitly create a schema and use it when constructing a table, or you can inspect the schema of an existing table to understand its structure. Schemas help ensure data consistency and make it easier to share table layouts between systems or codebases.
After creating a table, you can inspect its schema to see the column names, data types, and any attached metadata. This is especially useful for validating that your data is structured as intended. Building on the previous example, you can access the schema property of a table to view its structure. The schema not only shows you the types of each column but also provides a foundation for enforcing data contracts and interoperability between systems.
123456# Inspect the schema of the table print(table.schema) # Accessing metadata (if any) and column information print("Table columns:", table.column_names) print("First column type:", table.schema.field("name").type)
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