1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
|
"""Schema.
Adapted from Polars implementation at:
https://github.com/pola-rs/polars/blob/main/py-polars/polars/schema.py.
"""
from __future__ import annotations
from collections import OrderedDict
from functools import partial
from typing import TYPE_CHECKING, Iterable, Mapping, cast
from narwhals._utils import Implementation, Version, parse_version
if TYPE_CHECKING:
from typing import Any, ClassVar
import polars as pl
import pyarrow as pa
from narwhals.dtypes import DType
from narwhals.typing import DTypeBackend
BaseSchema = OrderedDict[str, DType]
else:
# Python 3.8 does not support generic OrderedDict at runtime
BaseSchema = OrderedDict
__all__ = ["Schema"]
class Schema(BaseSchema):
"""Ordered mapping of column names to their data type.
Arguments:
schema: The schema definition given by column names and their associated
*instantiated* Narwhals data type. Accepts a mapping or an iterable of tuples.
Examples:
Define a schema by passing *instantiated* data types.
>>> import narwhals as nw
>>> schema = nw.Schema({"foo": nw.Int8(), "bar": nw.String()})
>>> schema
Schema({'foo': Int8, 'bar': String})
Access the data type associated with a specific column name.
>>> schema["foo"]
Int8
Access various schema properties using the `names`, `dtypes`, and `len` methods.
>>> schema.names()
['foo', 'bar']
>>> schema.dtypes()
[Int8, String]
>>> schema.len()
2
"""
_version: ClassVar[Version] = Version.MAIN
def __init__(
self, schema: Mapping[str, DType] | Iterable[tuple[str, DType]] | None = None
) -> None:
schema = schema or {}
super().__init__(schema)
def names(self) -> list[str]:
"""Get the column names of the schema.
Returns:
Column names.
"""
return list(self.keys())
def dtypes(self) -> list[DType]:
"""Get the data types of the schema.
Returns:
Data types of schema.
"""
return list(self.values())
def len(self) -> int:
"""Get the number of columns in the schema.
Returns:
Number of columns.
"""
return len(self)
def to_arrow(self) -> pa.Schema:
"""Convert Schema to a pyarrow Schema.
Returns:
A pyarrow Schema.
Examples:
>>> import narwhals as nw
>>> schema = nw.Schema({"a": nw.Int64(), "b": nw.Datetime("ns")})
>>> schema.to_arrow()
a: int64
b: timestamp[ns]
"""
import pyarrow as pa # ignore-banned-import
from narwhals._arrow.utils import narwhals_to_native_dtype
return pa.schema(
(name, narwhals_to_native_dtype(dtype, self._version))
for name, dtype in self.items()
)
def to_pandas(
self, dtype_backend: DTypeBackend | Iterable[DTypeBackend] = None
) -> dict[str, Any]:
"""Convert Schema to an ordered mapping of column names to their pandas data type.
Arguments:
dtype_backend: Backend(s) used for the native types. When providing more than
one, the length of the iterable must be equal to the length of the schema.
Returns:
An ordered mapping of column names to their pandas data type.
Examples:
>>> import narwhals as nw
>>> schema = nw.Schema({"a": nw.Int64(), "b": nw.Datetime("ns")})
>>> schema.to_pandas()
{'a': 'int64', 'b': 'datetime64[ns]'}
>>> schema.to_pandas("pyarrow")
{'a': 'Int64[pyarrow]', 'b': 'timestamp[ns][pyarrow]'}
"""
import pandas as pd # ignore-banned-import
from narwhals._pandas_like.utils import narwhals_to_native_dtype
to_native_dtype = partial(
narwhals_to_native_dtype,
implementation=Implementation.PANDAS,
backend_version=parse_version(pd),
version=self._version,
)
if dtype_backend is None or isinstance(dtype_backend, str):
return {
name: to_native_dtype(dtype=dtype, dtype_backend=dtype_backend)
for name, dtype in self.items()
}
else:
backends = tuple(dtype_backend)
if len(backends) != len(self):
from itertools import chain, islice, repeat
n_user, n_actual = len(backends), len(self)
suggestion = tuple(
islice(
chain.from_iterable(islice(repeat(backends), n_actual)), n_actual
)
)
msg = (
f"Provided {n_user!r} `dtype_backend`(s), but schema contains {n_actual!r} field(s).\n"
"Hint: instead of\n"
f" schema.to_pandas({backends})\n"
"you may want to use\n"
f" schema.to_pandas({backends[0]})\n"
f"or\n"
f" schema.to_pandas({suggestion})"
)
raise ValueError(msg)
return {
name: to_native_dtype(dtype=dtype, dtype_backend=backend)
for name, dtype, backend in zip(self.keys(), self.values(), backends)
}
def to_polars(self) -> pl.Schema:
"""Convert Schema to a polars Schema.
Returns:
A polars Schema or plain dict (prior to polars 1.0).
Examples:
>>> import narwhals as nw
>>> schema = nw.Schema({"a": nw.Int64(), "b": nw.Datetime("ns")})
>>> schema.to_polars()
Schema({'a': Int64, 'b': Datetime(time_unit='ns', time_zone=None)})
"""
import polars as pl # ignore-banned-import
from narwhals._polars.utils import narwhals_to_native_dtype
pl_version = parse_version(pl)
schema = (
(
name,
narwhals_to_native_dtype(
dtype, self._version, backend_version=pl_version
),
)
for name, dtype in self.items()
)
return (
pl.Schema(schema)
if pl_version >= (1, 0, 0)
else cast("pl.Schema", dict(schema))
)
|
