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Fix test_create_table_numpy to run without pandas in the numpy: 1 CI leg #875

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Fix test_create_table_numpy to run without pandas in the numpy: 1 CI leg #875

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75 changes: 26 additions & 49 deletions tests/test_create.py
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Original file line number Diff line number Diff line change
Expand Up @@ -25,9 +25,9 @@
from sqlite_utils.utils import hash_record, sqlite3

try:
import pandas as pd # type: ignore
import numpy as np # type: ignore
except ImportError:
pd = None # type: ignore
np = None # type: ignore


def test_create_table(fresh_db):
Expand Down Expand Up @@ -1181,61 +1181,38 @@ def test_works_with_pathlib_path(tmpdir):
assert db.table("demo").count == 1


@pytest.mark.skipif(pd is None, reason="pandas and numpy are not installed")
@pytest.mark.skipif(np is None, reason="numpy is not installed")
def test_create_table_numpy(fresh_db):
assert pd is not None
df = pd.DataFrame({"col 1": range(3), "col 2": range(3)})
fresh_db.table("pandas").insert_all(df.to_dict(orient="records"))
records = [
{"col 1": np.int64(0).item(), "col 2": np.int64(0).item()},
{"col 1": np.int64(1).item(), "col 2": np.int64(1).item()},
{"col 1": np.int64(2).item(), "col 2": np.int64(2).item()}
]
fresh_db.table("numpy_test").insert_all(records)
assert [
{"col 1": 0, "col 2": 0},
{"col 1": 1, "col 2": 1},
{"col 1": 2, "col 2": 2},
] == list(fresh_db.table("pandas").rows)
] == list(fresh_db.table("numpy_test").rows)

# Now try all the different types
df = pd.DataFrame(
{
"np.int8": [-8],
"np.int16": [-16],
"np.int32": [-32],
"np.int64": [-64],
"np.uint8": [8],
"np.uint16": [16],
"np.uint32": [32],
"np.uint64": [64],
"np.float16": [16.5],
"np.float32": [32.5],
"np.float64": [64.5],
}
)
df = df.astype(
diff_types = [
{
"np.int8": "int8",
"np.int16": "int16",
"np.int32": "int32",
"np.int64": "int64",
"np.uint8": "uint8",
"np.uint16": "uint16",
"np.uint32": "uint32",
"np.uint64": "uint64",
"np.float16": "float16",
"np.float32": "float32",
"np.float64": "float64",
"np.int8": np.int8(-8).item(),
"np.int16": np.int16(-16).item(),
"np.int32": np.int32(-32).item(),
"np.int64": np.int64(-64).item(),
"np.uint8": np.uint8(8).item(),
"np.uint16": np.uint16(16).item(),
"np.uint32": np.uint32(32).item(),
"np.uint64": np.uint64(64).item(),
"np.float16": np.float16(16.5).item(),
"np.float32": np.float32(32.5).item(),
"np.float64": np.float64(64.5).item(),
}
)
assert [
"int8",
"int16",
"int32",
"int64",
"uint8",
"uint16",
"uint32",
"uint64",
"float16",
"float32",
"float64",
] == [str(t) for t in df.dtypes]
fresh_db.table("types").insert_all(df.to_dict(orient="records"))
]

fresh_db.table("types").insert_all(diff_types)
assert [
{
"np.float16": 16.5,
Expand Down
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