What Is pyarrow.Schema and How to Use It?

pyarrow.lib.Schema

(exposed as pyarrow.Schema) represents the schema of a table or a batch of data in PyArrow, the Python implementation of Apache Arrow. It stores metadata such as column names, data types, and nullability without containing actual row data.

Key Characteristics

Describes the structural metadata of a dataset: which columns exist, their names, types, and whether they can be null.

Implemented in the pyarrow module; the public class is pyarrow.Schema, while pyarrow.lib.Schema is the underlying C++ export.

Corresponds to Arrow C++'s arrow::Schema, an immutable in‑memory description of column names and types.

Main Uses of pyarrow.Schema

Define and validate table structures : When creating a RecordBatch or Table, a schema declares column names and types; it is also used to parse or validate files (Parquet, IPC, etc.).

Access column information :

Column names: schema.names or schema.field(i).name Column type: schema.field(i).type (e.g., pa.int64(), pa.string())

Number of columns: len(schema) Nullability: schema.field(i).nullable Serialization / deserialization : A schema can be written separately (e.g., in IPC streams) so that downstream systems know the table layout before receiving data batches.

Bridge to other libraries : Libraries compatible with Arrow—such as Pandas, Polars, and Lance—use pyarrow.Schema to represent columnar structures for table creation, scanning, and type inference.

Role in Arrow compute / query engines : Expressions, filters, and aggregations rely on the schema for name resolution, type checking, and type promotion (e.g., int32 + int64). Engines like DataFusion or Lance use the schema to plan scans and build indexes, recognizing complex types such as list<float> for vector indexing.

Simple Example

import pyarrow as pa

# Build a schema from a list of (name, type) tuples
schema = pa.schema([
    ("id", pa.int64()),
    ("name", pa.string()),
    ("vector", pa.list_(pa.float32(), 128)),  # 128‑dimensional vector
])

# Inspect common properties
print(schema.names)               # ['id', 'name', 'vector']
print(schema.field(0).type)       # int64
print(schema.field("name"))      # field accessed by name

# Create a Table using the schema
table = pa.table({
    "id": [1],
    "name": ["x"],
    "vector": [[0.0] * 128]
}, schema=schema)

print(table.schema)               # displays the pyarrow.Schema object

This example demonstrates constructing a schema, querying its attributes, and coupling it with a Table to produce a fully typed Arrow dataset.