def test_build_table_columnar(self):
from pymapd._pandas_loaders import build_input_columnar
data = pd.DataFrame({"a": [1, 2, 3], "b": [1.1, 2.2, 3.3]})
nulls = [False] * 3
result = build_input_columnar(data, preserve_index=False)
expected = [
TColumn(TColumnData(int_col=[1, 2, 3]), nulls=nulls),
TColumn(TColumnData(real_col=[1.1, 2.2, 3.3]), nulls=nulls)
]
assert_columnar_equal(result[0], expected)
def test_build_table_columnar_nulls(self):
import pandas as pd
import numpy as np
data = pd.DataFrame(
{
"boolean_": [True, False, None],
"bigint_":
np.array([0, 1, None], dtype=np.object),
"double_":
np.array([0, 1, None], dtype=np.float64),
"varchar_": ["a", "b", None],
"text_": ['a', 'b', None],
"time_": [datetime.time(0, 11, 59),
datetime.time(13), None],
"timestamp_":
[pd.Timestamp("2016"),
pd.Timestamp("2017"), None],
"date_":
[datetime.date(2016, 1, 1),
datetime.date(2017, 1, 1), None],
},
columns=[
'boolean_', 'bigint_', 'double_', 'varchar_', 'text_', 'time_',
'timestamp_', 'date_'
])
result = _pandas_loaders.build_input_columnar(data,
preserve_index=False)
nulls = [False, False, True]
int_na = -2147483648
bigint_na = -9223372036854775808
ns_na = -9223372037
expected = [
TColumn(TColumnData(int_col=[1, 0, int_na]), nulls=nulls),
TColumn(
TColumnData(int_col=np.array([0, 1, int_na], dtype=np.int64)),
nulls=nulls), # noqa
TColumn(TColumnData(
real_col=np.array([0, 1, np.nan], dtype=np.float64)),
nulls=nulls), # noqa
TColumn(TColumnData(str_col=['a', 'b', '']), nulls=nulls),
TColumn(TColumnData(str_col=['a', 'b', '']), nulls=nulls),
TColumn(TColumnData(int_col=[719, 46800, bigint_na]), nulls=nulls),
TColumn(TColumnData(int_col=[1451606400, 1483228800, ns_na]),
nulls=nulls), # noqa
TColumn(TColumnData(int_col=[1451606400, 1483228800, bigint_na]),
nulls=nulls) # noqa
]
assert_columnar_equal(result, expected)
def test_build_table_columnar_nulls(self):
import pandas as pd
import numpy as np
data = pd.DataFrame({
"boolean_": [True, False, None],
# Currently Pandas does not support storing None or NaN
# in integer columns, so int cols with null
# need to be objects. This means our type detection will be
# unreliable since if there is no number outside the int32
# bounds in a column with nulls then we will be assuming int
"int_": np.array([0, 1, None], dtype=np.object),
"bigint_": np.array([0, 9223372036854775807, None],
dtype=np.object),
"double_": np.array([0, 1, None], dtype=np.float64),
"varchar_": ["a", "b", None],
"text_": ['a', 'b', None],
"time_": [datetime.time(0, 11, 59), datetime.time(13), None],
"timestamp_": [pd.Timestamp("2016"), pd.Timestamp("2017"), None],
"date_": [datetime.date(1001, 1, 1), datetime.date(2017, 1, 1),
None],
}, columns=['boolean_', 'int_', 'bigint_',
'double_', 'varchar_', 'text_', 'time_', 'timestamp_',
'date_'])
result = _pandas_loaders.build_input_columnar(data,
preserve_index=False)
nulls = [False, False, True]
bool_na = -128
int_na = -2147483648
bigint_na = -9223372036854775808
ns_na = -9223372037
double_na = 0
expected = [
TColumn(TColumnData(int_col=[1, 0, bool_na]), nulls=nulls),
TColumn(TColumnData(int_col=np.array([0, 1, int_na], dtype=np.int32)), nulls=nulls), # noqa
TColumn(TColumnData(int_col=np.array([0, 9223372036854775807, bigint_na], dtype=np.int64)), nulls=nulls), # noqa
TColumn(TColumnData(real_col=np.array([0, 1, double_na], dtype=np.float64)), nulls=nulls), # noqa
TColumn(TColumnData(str_col=['a', 'b', '']), nulls=nulls),
TColumn(TColumnData(str_col=['a', 'b', '']), nulls=nulls),
TColumn(TColumnData(int_col=[719, 46800, bigint_na]), nulls=nulls),
TColumn(TColumnData(int_col=[1451606400, 1483228800, ns_na]), nulls=nulls), # noqa
TColumn(TColumnData(int_col=[-30578688000, 1483228800, bigint_na]), nulls=nulls) # noqa
]
assert_columnar_equal(result[0], expected)
def test_build_table_columnar_pandas(self):
import pandas as pd
import numpy as np
data = pd.DataFrame(
{
"boolean_": [True, False],
"smallint_": np.array([0, 1], dtype=np.int8),
"int_": np.array([0, 1], dtype=np.int32),
"bigint_": np.array([0, 1], dtype=np.int64),
"float_": np.array([0, 1], dtype=np.float32),
"double_": np.array([0, 1], dtype=np.float64),
"varchar_": ["a", "b"],
"text_": ['a', 'b'],
"time_": [datetime.time(0, 11, 59),
datetime.time(13)],
"timestamp_": [pd.Timestamp("2016"),
pd.Timestamp("2017")],
"date_":
[datetime.date(2016, 1, 1),
datetime.date(2017, 1, 1)],
},
columns=[
'boolean_', 'smallint_', 'int_', 'bigint_', 'float_',
'double_', 'varchar_', 'text_', 'time_', 'timestamp_', 'date_'
])
result = _pandas_loaders.build_input_columnar(data,
preserve_index=False)
nulls = [False, False]
expected = [
TColumn(TColumnData(int_col=[True, False]), nulls=nulls),
TColumn(TColumnData(int_col=np.array([0, 1], dtype=np.int8)),
nulls=nulls), # noqa
TColumn(TColumnData(int_col=np.array([0, 1], dtype=np.int32)),
nulls=nulls), # noqa
TColumn(TColumnData(int_col=np.array([0, 1], dtype=np.int64)),
nulls=nulls), # noqa
TColumn(TColumnData(real_col=np.array([0, 1], dtype=np.float32)),
nulls=nulls), # noqa
TColumn(TColumnData(real_col=np.array([0, 1], dtype=np.float64)),
nulls=nulls), # noqa
TColumn(TColumnData(str_col=['a', 'b']), nulls=nulls),
TColumn(TColumnData(str_col=['a', 'b']), nulls=nulls),
TColumn(TColumnData(int_col=[719, 46800]), nulls=nulls),
TColumn(TColumnData(int_col=[1451606400, 1483228800]),
nulls=nulls), # noqa
TColumn(TColumnData(int_col=[1451606400, 1483228800]), nulls=nulls)
]
assert_columnar_equal(result, expected)
