>>> from ibis import schema, Schema
>>> sc = schema([("foo", "string"), ("bar", "int64"), ("baz", "boolean")])
>>> sc = schema(names=["foo", "bar", "baz"], types=["string", "int64", "boolean"])
>>> sc = schema(dict(foo="string"))
>>> sc = schema(Schema(dict(foo="string"))) # no-opSchemas
Table Schemas
schema
ibis.schema(pairs=None, names=None, types=None)
Validate and return a Schema object.
Parameters
| Name | Type | Description | Default |
|---|---|---|---|
pairs |
SchemaLike | None |
List or dictionary of name, type pairs. Mutually exclusive with names and types arguments. |
None |
names |
Iterable[str] | None | Field names. Mutually exclusive with pairs. |
None |
types |
Iterable[str | dt.DataType] | None |
Field types. Mutually exclusive with pairs. |
None |
Returns
| Type | Description |
|---|---|
| Schema | An ibis schema |
Examples
Schema
Schema(self, **kwargs)
An ordered mapping of str -> datatype, used to hold a Table’s schema.
Attributes
| Name | Description |
|---|---|
| fields | A mapping of str to |
Methods
| Name | Description |
|---|---|
| equals | Return whether other is equal to self. |
| from_numpy | Return the equivalent ibis schema. |
| from_pandas | Return the equivalent ibis schema. |
| from_polars | Return the equivalent ibis schema. |
| from_pyarrow | Return the equivalent ibis schema. |
| from_tuples | Construct a Schema from an iterable of pairs. |
| name_at_position | Return the name of a schema column at position i. |
| to_numpy | Return the equivalent numpy dtypes. |
| to_pandas | Return the equivalent pandas datatypes. |
| to_polars | Return the equivalent polars schema. |
| to_pyarrow | Return the equivalent pyarrow schema. |
equals
equals(other)
Return whether other is equal to self.
The order of fields in the schema is taken into account when computing equality.
Parameters
| Name | Type | Description | Default |
|---|---|---|---|
other |
Schema | Schema to compare self to. |
required |
Examples
>>> import ibis
>>> xy = ibis.schema({"x": int, "y": str})
>>> xy2 = ibis.schema({"x": int, "y": str})
>>> yx = ibis.schema({"y": str, "x": int})
>>> xy_float = ibis.schema({"x": float, "y": str})
>>> assert xy.equals(xy2)
>>> assert not xy.equals(yx)
>>> assert not xy.equals(xy_float)from_numpy
from_numpy(numpy_schema)
Return the equivalent ibis schema.
from_pandas
from_pandas(pandas_schema)
Return the equivalent ibis schema.
from_polars
from_polars(polars_schema)
Return the equivalent ibis schema.
from_pyarrow
from_pyarrow(pyarrow_schema)
Return the equivalent ibis schema.
from_tuples
from_tuples(values)
Construct a Schema from an iterable of pairs.
Parameters
| Name | Type | Description | Default |
|---|---|---|---|
values |
Iterable[tuple[str, str | dt.DataType]] |
An iterable of pairs of name and type. | required |
Returns
| Type | Description |
|---|---|
| Schema | A new schema |
Examples
>>> import ibis
>>> ibis.Schema.from_tuples([("a", "int"), ("b", "string")])ibis.Schema {
a int64
b string
}name_at_position
name_at_position(i)
Return the name of a schema column at position i.
Parameters
| Name | Type | Description | Default |
|---|---|---|---|
i |
int | The position of the column | required |
Returns
| Type | Description |
|---|---|
| str | The name of the column in the schema at position i. |
Examples
>>> import ibis
>>> sch = ibis.Schema({"a": "int", "b": "string"})
>>> sch.name_at_position(0)'a'>>> sch.name_at_position(1)'b'to_numpy
to_numpy()
Return the equivalent numpy dtypes.
to_pandas
to_pandas()
Return the equivalent pandas datatypes.
to_polars
to_polars()
Return the equivalent polars schema.
to_pyarrow
to_pyarrow()
Return the equivalent pyarrow schema.