pyarrow.Schema — Apache Arrow v20.0.0 (original) (raw)

class pyarrow.Schema#

Bases: _Weakrefable

A named collection of types a.k.a schema. A schema defines the column names and types in a record batch or table data structure. They also contain metadata about the columns. For example, schemas converted from Pandas contain metadata about their original Pandas types so they can be converted back to the same types.

Warning

Do not call this class’s constructor directly. Instead usepyarrow.schema() factory function which makes a new Arrow Schema object.

Examples

Create a new Arrow Schema object:

import pyarrow as pa pa.schema([ ... ('some_int', pa.int32()), ... ('some_string', pa.string()) ... ]) some_int: int32 some_string: string

Create Arrow Schema with metadata:

pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())], ... metadata={"n_legs": "Number of legs per animal"}) n_legs: int64 animals: string -- schema metadata -- n_legs: 'Number of legs per animal'

__init__(*args, **kwargs)#

Methods

Attributes

add_metadata(self, metadata)#

DEPRECATED

Parameters:

metadatadict

Keys and values must be string-like / coercible to bytes

append(self, Field field)#

Append a field at the end of the schema.

In contrast to Python’s list.append() it does return a new object, leaving the original Schema unmodified.

Parameters:

fieldField

Returns:

schema: Schema

New object with appended field.

Examples

import pyarrow as pa schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())])

Append a field ‘extra’ at the end of the schema:

schema_new = schema.append(pa.field('extra', pa.bool_())) schema_new n_legs: int64 animals: string extra: bool

Original schema is unmodified:

schema n_legs: int64 animals: string

empty_table(self)#

Provide an empty table according to the schema.

Returns:

table: pyarrow.Table

Examples

import pyarrow as pa schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())])

Create an empty table with schema’s fields:

schema.empty_table() pyarrow.Table n_legs: int64 animals: string

n_legs: [[]] animals: [[]]

equals(self, Schema other, bool check_metadata=False)#

Test if this schema is equal to the other

Parameters:

otherpyarrow.Schema

check_metadatabool, default False

Key/value metadata must be equal too

Returns:

is_equalbool

Examples

import pyarrow as pa schema1 = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())], ... metadata={"n_legs": "Number of legs per animal"}) schema2 = pa.schema([ ... ('some_int', pa.int32()), ... ('some_string', pa.string()) ... ])

Test two equal schemas:

schema1.equals(schema1) True

Test two unequal schemas:

schema1.equals(schema2) False

field(self, i)#

Select a field by its column name or numeric index.

Parameters:

iint or str

Returns:

pyarrow.Field

Examples

import pyarrow as pa schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())])

Select the second field:

schema.field(1) pyarrow.Field<animals: string>

Select the field of the column named ‘n_legs’:

schema.field('n_legs') pyarrow.Field<n_legs: int64>

field_by_name(self, name)#

DEPRECATED

Parameters:

namestr

Returns:

field: pyarrow.Field

classmethod from_pandas(cls, df, preserve_index=None)#

Returns implied schema from dataframe

Parameters:

dfpandas.DataFrame

preserve_indexbool, default True

Whether to store the index as an additional column (or columns, for MultiIndex) in the resulting Table. The default of None will store the index as a column, except for RangeIndex which is stored as metadata only. Usepreserve_index=True to force it to be stored as a column.

Returns:

pyarrow.Schema

Examples

import pandas as pd import pyarrow as pa df = pd.DataFrame({ ... 'int': [1, 2], ... 'str': ['a', 'b'] ... })

Create an Arrow Schema from the schema of a pandas dataframe:

pa.Schema.from_pandas(df) int: int64 str: string -- schema metadata -- pandas: '{"index_columns": [{"kind": "range", "name": null, ...

get_all_field_indices(self, name)#

Return sorted list of indices for the fields with the given name.

Parameters:

namestr

The name of the field to look up.

Returns:

indicesList[int]

Examples

import pyarrow as pa schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string()), ... pa.field('animals', pa.bool_())])

Get the indexes of the fields named ‘animals’:

schema.get_all_field_indices("animals") [1, 2]

get_field_index(self, name)#

Return index of the unique field with the given name.

Parameters:

namestr

The name of the field to look up.

Returns:

indexint

The index of the field with the given name; -1 if the name isn’t found or there are several fields with the given name.

Examples

import pyarrow as pa schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())])

Get the index of the field named ‘animals’:

schema.get_field_index("animals") 1

Index in case of several fields with the given name:

schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string()), ... pa.field('animals', pa.bool_())], ... metadata={"n_legs": "Number of legs per animal"}) schema.get_field_index("animals") -1

insert(self, int i, Field field)#

Add a field at position i to the schema.

Parameters:

iint

fieldField

Returns:

schema: Schema

Examples

import pyarrow as pa schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())])

Insert a new field on the second position:

schema.insert(1, pa.field('extra', pa.bool_())) n_legs: int64 extra: bool animals: string

metadata#

The schema’s metadata (if any is set).

Returns:

metadata: dict or None

Examples

import pyarrow as pa schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())], ... metadata={"n_legs": "Number of legs per animal"})

Get the metadata of the schema’s fields:

schema.metadata {b'n_legs': b'Number of legs per animal'}

names#

The schema’s field names.

Returns:

list of str

Examples

import pyarrow as pa schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())])

Get the names of the schema’s fields:

schema.names ['n_legs', 'animals']

pandas_metadata#

Return deserialized-from-JSON pandas metadata field (if it exists)

Examples

import pyarrow as pa import pandas as pd df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) schema = pa.Table.from_pandas(df).schema

Select pandas metadata field from Arrow Schema:

schema.pandas_metadata {'index_columns': [{'kind': 'range', 'name': None, 'start': 0, 'stop': 4, 'step': 1}], ...

remove(self, int i)#

Remove the field at index i from the schema.

Parameters:

iint

Returns:

schema: Schema

Examples

import pyarrow as pa schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())])

Remove the second field of the schema:

schema.remove(1) n_legs: int64

remove_metadata(self)#

Create new schema without metadata, if any

Returns:

schemapyarrow.Schema

Examples

import pyarrow as pa schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())], ... metadata={"n_legs": "Number of legs per animal"}) schema n_legs: int64 animals: string -- schema metadata -- n_legs: 'Number of legs per animal'

Create a new schema with removing the metadata from the original:

schema.remove_metadata() n_legs: int64 animals: string

serialize(self, memory_pool=None)#

Write Schema to Buffer as encapsulated IPC message

Parameters:

memory_poolMemoryPool, default None

Uses default memory pool if not specified

Returns:

serializedBuffer

Examples

import pyarrow as pa schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())])

Write schema to Buffer:

schema.serialize() <pyarrow.Buffer address=0x... size=... is_cpu=True is_mutable=True>

set(self, int i, Field field)#

Replace a field at position i in the schema.

Parameters:

iint

fieldField

Returns:

schema: Schema

Examples

import pyarrow as pa schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())])

Replace the second field of the schema with a new field ‘extra’:

schema.set(1, pa.field('replaced', pa.bool_())) n_legs: int64 replaced: bool

to_string(self, truncate_metadata=True, show_field_metadata=True, show_schema_metadata=True)#

Return human-readable representation of Schema

Parameters:

truncate_metadatabool, default True

Limit metadata key/value display to a single line of ~80 characters or less

show_field_metadatabool, default True

Display Field-level KeyValueMetadata

show_schema_metadatabool, default True

Display Schema-level KeyValueMetadata

Returns:

strthe formatted output

types#

The schema’s field types.

Returns:

list of DataType

Examples

import pyarrow as pa schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())])

Get the types of the schema’s fields:

schema.types [DataType(int64), DataType(string)]

with_metadata(self, metadata)#

Add metadata as dict of string keys and values to Schema

Parameters:

metadatadict

Keys and values must be string-like / coercible to bytes

Returns:

schemapyarrow.Schema

Examples

import pyarrow as pa schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())])

Add metadata to existing schema field:

schema.with_metadata({"n_legs": "Number of legs per animal"}) n_legs: int64 animals: string -- schema metadata -- n_legs: 'Number of legs per animal'