def test_timestamp_with_timezone():
df = pd.DataFrame(
{'A': pd.date_range('20130101', periods=3, tz='US/Eastern')}
)
schema = sch.infer(df)
expected = ibis.schema([('A', "timestamp('US/Eastern')")])
assert schema.equals(expected)
assert schema.types[0].equals(dt.Timestamp('US/Eastern'))
def table(self, name, path):
if name not in self.list_tables(path):
raise AttributeError(name)
# get the schema
with pd.HDFStore(str(path), mode='r') as store:
df = store.select(name, start=0, stop=0)
schema = sch.infer(df)
t = self.table_class(name, schema, self).to_expr()
self.dictionary[name] = path
return t
def test_insert(con, temp_table_db, exhaustive_df):
tmp_db, table_name = temp_table_db
schema = sch.infer(exhaustive_df)
con.create_table(table_name, database=tmp_db, schema=schema)
con.insert(table_name, exhaustive_df.iloc[:4], database=tmp_db)
con.insert(table_name, exhaustive_df.iloc[4:], database=tmp_db)
table = con.table(table_name, database=tmp_db)
result = (
table.execute().sort_values(by='tinyint_col').reset_index(drop=True)
)
assert_frame_equal(result, exhaustive_df)
def get_schema(self, table_name, database=None):
"""
Return a Schema object for the indicated table and database
Parameters
----------
table_name : string
May be fully qualified
database : string, default None
Returns
-------
schema : ibis Schema
"""
return sch.infer(self.dictionary[table_name])
def table(self, name, path):
if name not in self.list_tables(path):
raise AttributeError(name)
if path is None:
path = self.root
# get the schema
f = path / "{}.parquet".format(name)
parquet_file = pq.ParquetFile(str(f))
schema = sch.infer(parquet_file.schema)
table = self.table_class(name, schema, self).to_expr()
self.dictionary[name] = f
return table
def table(self, name, path=None, schema=None, **kwargs):
if name not in self.list_tables(path):
raise AttributeError(name)
if path is None:
path = self.root
# get the schema
f = path / "{}.{}".format(name, self.extension)
# read sample
schema = schema or sch.schema([])
sample = _read_csv(f, schema=schema, header=0, nrows=50, **kwargs)
# infer sample's schema and define table
schema = sch.infer(sample)
table = self.table_class(name, schema, self, **kwargs).to_expr()
self.dictionary[name] = f
return table
def get_schema(self, table_name, database=None):
"""
Return a Schema object for the indicated table and database
Parameters
----------
table_name : string
May be fully qualified
database : string
Spark does not have a database argument for its table() method,
so this must be None
Returns
-------
schema : ibis Schema
"""
if database is not None:
raise com.UnsupportedArgumentError(
'Spark does not support database param for table')
df = self._session.table(table_name)
return sch.infer(df)
def table(
self,
name: str,
schema: sch.Schema | None = None,
) -> ir.TableExpr:
"""Get an ibis expression representing a DataFusion table.
Parameters
----------
name
The name of the table to retreive
schema
An optional schema for the table
Returns
-------
TableExpr
A table expression
"""
catalog = self._context.catalog()
database = catalog.database('public')
table = database.table(name)
schema = sch.infer(table.schema)
return self.table_class(name, schema, self).to_expr()
def get_schema(self, name, database=None):
project, dataset = self._parse_project_and_dataset(database)
table_ref = self.client.dataset(dataset, project=project).table(name)
bq_table = self.client.get_table(table_ref)
return sch.infer(bq_table)
def get_schema(self):
# define a temporary table using delimited data
return sch.infer(self.df)
def test_dtype_categorical():
df = pd.DataFrame({'col': ['a', 'b', 'c', 'a']}, dtype='category')
inferred = sch.infer(df)
expected = ibis.schema([('col', dt.Category())])
assert inferred == expected
def test_dtype_uint64():
df = pd.DataFrame({'col': np.uint64([666, 2, 3])})
inferred = sch.infer(df)
expected = ibis.schema([('col', 'uint64')])
assert inferred == expected
def test_dtype_uint16():
df = pd.DataFrame({'col': np.uint16([5569, 1, 33])})
inferred = sch.infer(df)
expected = ibis.schema([('col', 'uint16')])
assert inferred == expected
def test_dtype_float32():
df = pd.DataFrame({'col': np.float32([45e-3, -0.4, 99.0])})
inferred = sch.infer(df)
expected = ibis.schema([('col', 'float')])
assert inferred == expected
def get_schema(self, name, database=None):
table_id = self._fully_qualified_name(name, database)
table_ref = bq.TableReference.from_string(table_id)
bq_table = self.client.get_table(table_ref)
return sch.infer(bq_table)
def test_schema_infer(col_data, schema_type):
df = pd.DataFrame({'col': col_data})
inferred = sch.infer(df)
expected = ibis.schema([('col', schema_type)])
assert inferred == expected
def test_infer_exhaustive_dataframe():
df = pd.DataFrame(
{
'bigint_col': np.array(
[0, 10, 20, 30, 40, 50, 60, 70, 80, 90], dtype='i8'
),
'bool_col': np.array(
[
True,
False,
True,
False,
True,
None,
True,
False,
True,
False,
],
dtype=np.bool_,
),
'bool_obj_col': np.array(
[
True,
False,
np.nan,
False,
True,
np.nan,
True,
np.nan,
True,
False,
],
dtype=np.object_,
),
'date_string_col': [
'11/01/10',
None,
'11/01/10',
'11/01/10',
'11/01/10',
'11/01/10',
'11/01/10',
'11/01/10',
'11/01/10',
'11/01/10',
],
'double_col': np.array(
[
0.0,
10.1,
np.nan,
30.299999999999997,
40.399999999999999,
50.5,
60.599999999999994,
70.700000000000003,
80.799999999999997,
90.899999999999991,
],
dtype=np.float64,
),
'float_col': np.array(
[
np.nan,
1.1000000238418579,
2.2000000476837158,
3.2999999523162842,
4.4000000953674316,
5.5,
6.5999999046325684,
7.6999998092651367,
8.8000001907348633,
9.8999996185302734,
],
dtype='f4',
),
'int_col': np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype='i4'),
'month': [11, 11, 11, 11, 2, 11, 11, 11, 11, 11],
'smallint_col': np.array(
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype='i2'
),
'string_col': [
'0',
'1',
None,
'double , whammy',
'4',
'5',
'6',
'7',
'8',
'9',
],
'timestamp_col': [
pd.Timestamp('2010-11-01 00:00:00'),
None,
pd.Timestamp('2010-11-01 00:02:00.100000'),
pd.Timestamp('2010-11-01 00:03:00.300000'),
pd.Timestamp('2010-11-01 00:04:00.600000'),
pd.Timestamp('2010-11-01 00:05:00.100000'),
pd.Timestamp('2010-11-01 00:06:00.150000'),
pd.Timestamp('2010-11-01 00:07:00.210000'),
pd.Timestamp('2010-11-01 00:08:00.280000'),
pd.Timestamp('2010-11-01 00:09:00.360000'),
],
'tinyint_col': np.array(
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype='i1'
),
'year': [
2010,
2010,
2010,
2010,
2010,
2009,
2009,
2009,
2009,
2009,
],
}
)
expected = [
('bigint_col', dt.int64),
('bool_col', dt.boolean),
('bool_obj_col', dt.boolean),
('date_string_col', dt.string),
('double_col', dt.double),
('float_col', dt.float),
('int_col', dt.int32),
('month', dt.int64),
('smallint_col', dt.int16),
('string_col', dt.string),
('timestamp_col', dt.timestamp),
('tinyint_col', dt.int8),
('year', dt.int64),
]
assert sch.infer(df) == ibis.schema(expected)
def test_infer_simple_dataframe(column, expected_dtype):
df = pd.DataFrame({'col': column})
assert sch.infer(df) == ibis.schema([('col', expected_dtype)])
def get_schema(self, table_name, database=None):
return sch.infer(self.dictionary[table_name])
def __init__(self, table, source, schema=None):
schema = sch.infer(table, schema=schema)
super().__init__(table.name, schema, source)
self.sqla_table = table
def test_dtype_int64():
df = pd.DataFrame({'col': np.int64([102, 67_228_734, -0])})
inferred = sch.infer(df)
expected = ibis.schema([('col', 'int64')])
assert inferred == expected
def test_infer_simple_dataframe(column, expected_dtype):
df = pd.DataFrame({'col': column})
assert sch.infer(df) == ibis.schema([('col', expected_dtype)])
def test_dtype_float64():
df = pd.DataFrame({'col': np.float64([-3e43, 43.0, 10_000_000.0])})
inferred = sch.infer(df)
expected = ibis.schema([('col', 'double')])
assert inferred == expected
def get_schema(self):
# define a temporary table using delimited data
return sch.infer(self.df)
def test_dtype_uint32():
df = pd.DataFrame({'col': np.uint32([100, 0, 6])})
inferred = sch.infer(df)
expected = ibis.schema([('col', 'uint32')])
assert inferred == expected
def test_dtype_uint8(self):
df = pd.DataFrame({'col': np.uint8([3, 0, 16])})
inferred = sch.infer(df)
expected = ibis.schema([('col', 'uint8')])
assert inferred == expected
def test_dtype_string():
df = pd.DataFrame({'col': ['foo', 'bar', 'hello']})
inferred = sch.infer(df)
expected = ibis.schema([('col', 'string')])
assert inferred == expected
def table(self, name, schema=None):
df = self.dictionary[name]
schema = sch.infer(df, schema=schema)
return self.table_class(name, schema, self).to_expr()
def test_dtype_bool():
df = pd.DataFrame({'col': [True, False, False]})
inferred = sch.infer(df)
expected = ibis.schema([('col', 'boolean')])
assert inferred == expected
def __init__(self, table, source, schema=None):
schema = sch.infer(table, schema=schema)
super().__init__(table.name, schema, source)
self.sqla_table = table
def table(self, name: str, schema: sch.Schema = None):
df = self.dictionary[name]
schema = sch.infer(df, schema=schema or self.schemas.get(name, None))
return self.table_class(name, schema, self).to_expr()
def table(self, name, schema=None):
df = self.dictionary[name]
schema = sch.infer(df, schema=schema)
return PandasTable(name, schema, self).to_expr()
def test_schema_infer(col_data, schema_type):
df = dd.from_pandas(pd.DataFrame({'col': col_data}), npartitions=1)
inferred = sch.infer(df)
expected = ibis.schema([('col', schema_type)])
assert inferred == expected
def get_schema(self, name, database=None):
(table_id, dataset_id) = _ensure_split(name, database)
bq_table = self._proxy.get_table(table_id, dataset_id)
return sch.infer(bq_table)
def table(self, name, schema=None):
df = self.dictionary[name]
schema = sch.infer(df, schema=schema)
return PandasTable(name, schema, self).to_expr()
def test_dtype_int8():
df = pd.DataFrame({'col': np.int8([-3, 9, 17])})
inferred = sch.infer(df)
expected = ibis.schema([('col', 'int8')])
assert inferred == expected
def table(self, name: str, schema: sch.Schema = None) -> DaskTable:
df = self.dictionary[name]
schema = sch.infer(df, schema=schema)
return DaskTable(name, schema, self).to_expr()
def test_dtype_int16():
df = pd.DataFrame({'col': np.int16([-5, 0, 12])})
inferred = sch.infer(df)
expected = ibis.schema([('col', 'int16')])
assert inferred == expected
def test_infer_exhaustive_dataframe():
df = dd.from_pandas(
pd.DataFrame({
'bigint_col':
np.array([0, 10, 20, 30, 40, 50, 60, 70, 80, 90], dtype='i8'),
'bool_col':
np.array(
[
True,
False,
True,
False,
True,
None,
True,
False,
True,
False,
],
dtype=np.bool_,
),
'bool_obj_col':
np.array(
[
True,
False,
np.nan,
False,
True,
np.nan,
True,
np.nan,
True,
False,
],
dtype=np.object_,
),
'date_string_col': [
'11/01/10',
None,
'11/01/10',
'11/01/10',
'11/01/10',
'11/01/10',
'11/01/10',
'11/01/10',
'11/01/10',
'11/01/10',
],
'double_col':
np.array(
[
0.0,
10.1,
np.nan,
30.299999999999997,
40.399999999999999,
50.5,
60.599999999999994,
70.700000000000003,
80.799999999999997,
90.899999999999991,
],
dtype=np.float64,
),
'float_col':
np.array(
[
np.nan,
1.1000000238418579,
2.2000000476837158,
3.2999999523162842,
4.4000000953674316,
5.5,
6.5999999046325684,
7.6999998092651367,
8.8000001907348633,
9.8999996185302734,
],
dtype='f4',
),
'int_col':
np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype='i4'),
'month': [11, 11, 11, 11, 2, 11, 11, 11, 11, 11],
'smallint_col':
np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype='i2'),
'string_col': [
'0',
'1',
None,
'double , whammy',
'4',
'5',
'6',
'7',
'8',
'9',
],
'timestamp_col': [
pd.Timestamp('2010-11-01 00:00:00'),
None,
pd.Timestamp('2010-11-01 00:02:00.100000'),
pd.Timestamp('2010-11-01 00:03:00.300000'),
pd.Timestamp('2010-11-01 00:04:00.600000'),
pd.Timestamp('2010-11-01 00:05:00.100000'),
pd.Timestamp('2010-11-01 00:06:00.150000'),
pd.Timestamp('2010-11-01 00:07:00.210000'),
pd.Timestamp('2010-11-01 00:08:00.280000'),
pd.Timestamp('2010-11-01 00:09:00.360000'),
],
'tinyint_col':
np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype='i1'),
'year': [
2010,
2010,
2010,
2010,
2010,
2009,
2009,
2009,
2009,
2009,
],
}),
npartitions=1,
)
expected = [
('bigint_col', dt.int64),
('bool_col', dt.boolean),
('bool_obj_col', dt.boolean),
('date_string_col', dt.string),
('double_col', dt.double),
('float_col', dt.float),
('int_col', dt.int32),
('month', dt.int64),
('smallint_col', dt.int16),
('string_col', dt.string),
('timestamp_col', dt.timestamp),
('tinyint_col', dt.int8),
('year', dt.int64),
]
assert sch.infer(df) == ibis.schema(expected)
def test_dtype_int32():
df = pd.DataFrame({'col': np.int32([-12, 3, 25000])})
inferred = sch.infer(df)
expected = ibis.schema([('col', 'int32')])
assert inferred == expected
