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from __future__ import annotations
import pytest
np = pytest.importorskip("numpy")
pd = pytest.importorskip("pandas")
from pandas.testing import assert_frame_equal, assert_index_equal, assert_series_equal
import dask
import dask.bag as db
import dask.dataframe as dd
from distributed.client import wait
from distributed.utils_test import gen_cluster
dfs = [
pd.DataFrame({"x": [1, 2, 3]}, index=[0, 10, 20]),
pd.DataFrame({"x": [4, 5, 6]}, index=[30, 40, 50]),
pd.DataFrame({"x": [7, 8, 9]}, index=[60, 70, 80]),
]
ignore_single_machine_warning = pytest.mark.filterwarnings(
"ignore:Running on a single-machine scheduler:UserWarning"
)
def assert_equal(a, b):
assert type(a) == type(b)
if isinstance(a, pd.DataFrame):
assert_frame_equal(a, b)
elif isinstance(a, pd.Series):
assert_series_equal(a, b)
elif isinstance(a, pd.Index):
assert_index_equal(a, b)
else:
assert a == b
@ignore_single_machine_warning
@gen_cluster(client=True)
async def test_dataframes(c, s, a, b):
df = pd.DataFrame(
{"x": np.random.random(1000), "y": np.random.random(1000)},
index=np.arange(1000),
)
ldf = dd.from_pandas(df, npartitions=10)
rdf = await c.persist(ldf)
assert rdf.divisions == ldf.divisions
remote = c.compute(rdf)
result = await remote
assert_frame_equal(result, ldf.compute(scheduler="sync"))
exprs = [
lambda df: df.x.mean(),
lambda df: df.y.std(),
lambda df: df.assign(z=df.x + df.y).drop_duplicates(),
lambda df: df.index,
lambda df: df.x,
lambda df: df.x.cumsum(),
lambda df: df.groupby(["x", "y"]).count(),
lambda df: df.loc[50:75],
]
for f in exprs:
local = f(ldf).compute(scheduler="sync")
remote = c.compute(f(rdf))
remote = await remote
assert_equal(local, remote)
@ignore_single_machine_warning
@gen_cluster(client=True)
async def test_dask_array_collections(c, s, a, b):
import dask.array as da
s.validate = False
x_dsk = {("x", i, j): np.random.random((3, 3)) for i in range(3) for j in range(2)}
y_dsk = {("y", i, j): np.random.random((3, 3)) for i in range(2) for j in range(3)}
x_futures = await c.scatter(x_dsk)
y_futures = await c.scatter(y_dsk)
dt = np.random.random(0).dtype
x_local = da.Array(x_dsk, "x", ((3, 3, 3), (3, 3)), dt)
y_local = da.Array(y_dsk, "y", ((3, 3), (3, 3, 3)), dt)
x_remote = da.Array(x_futures, "x", ((3, 3, 3), (3, 3)), dt)
y_remote = da.Array(y_futures, "y", ((3, 3), (3, 3, 3)), dt)
exprs = [
lambda x, y: x.T + y,
lambda x, y: x.mean() + y.mean(),
lambda x, y: x.dot(y).std(axis=0),
lambda x, y: x - x.mean(axis=1)[:, None],
]
for expr in exprs:
local = expr(x_local, y_local).compute(scheduler="sync")
remote = c.compute(expr(x_remote, y_remote))
remote = await remote
assert np.all(local == remote)
@gen_cluster(client=True)
async def test_bag_groupby_tasks_default(c, s, a, b):
b = db.range(100, npartitions=10)
b2 = b.groupby(lambda x: x % 13)
assert not any("partd" in k[0] for k in b2.dask)
@pytest.mark.parametrize("wait", [wait, lambda x: None])
def test_dataframe_set_index_sync(wait, client):
df = dask.datasets.timeseries(
start="2000",
end="2001",
dtypes={"value": float, "name": str, "id": int},
freq="2H",
partition_freq="1M",
seed=1,
)
df = df.persist()
wait(df)
df2 = df.set_index("name", shuffle="tasks")
df2 = df2.persist()
assert len(df2)
def make_time_dataframe():
return pd.DataFrame(
np.random.randn(30, 4),
columns=list("ABCD"),
index=pd.date_range("2000", periods=30, freq="B"),
)
def test_loc_sync(client):
df = make_time_dataframe()
ddf = dd.from_pandas(df, npartitions=10)
ddf.loc["2000-01-17":"2000-01-24"].compute()
def test_rolling_sync(client):
df = make_time_dataframe()
ddf = dd.from_pandas(df, npartitions=10)
ddf.A.rolling(2).mean().compute()
@gen_cluster(client=True)
async def test_loc(c, s, a, b):
df = make_time_dataframe()
ddf = dd.from_pandas(df, npartitions=10)
future = c.compute(ddf.loc["2000-01-17":"2000-01-24"])
await future
@ignore_single_machine_warning
def test_dataframe_groupby_tasks(client):
df = make_time_dataframe()
df["A"] = df.A // 0.1
df["B"] = df.B // 0.1
ddf = dd.from_pandas(df, npartitions=10)
for ind in [lambda x: "A", lambda x: x.A]:
a = df.groupby(ind(df)).apply(len)
b = ddf.groupby(ind(ddf)).apply(len, meta=(None, int))
assert_equal(a, b.compute(scheduler="sync").sort_index())
assert not any("partd" in k[0] for k in b.dask)
a = df.groupby(ind(df)).B.apply(len)
b = ddf.groupby(ind(ddf)).B.apply(len, meta=("B", int))
assert_equal(a, b.compute(scheduler="sync").sort_index())
assert not any("partd" in k[0] for k in b.dask)
with pytest.raises((NotImplementedError, ValueError)):
ddf.groupby(ddf[["A", "B"]]).apply(len, meta=int)
a = df.groupby(["A", "B"]).apply(len)
b = ddf.groupby(["A", "B"]).apply(len, meta=(None, int))
assert_equal(a, b.compute(scheduler="sync").sort_index())
@gen_cluster(client=True)
async def test_sparse_arrays(c, s, a, b):
sparse = pytest.importorskip("sparse")
da = pytest.importorskip("dask.array")
x = da.random.random((100, 10), chunks=(10, 10))
x[x < 0.95] = 0
s = x.map_blocks(sparse.COO)
future = c.compute(s.sum(axis=0)[:10])
await future
@gen_cluster(client=True, nthreads=[("127.0.0.1", 1)])
async def test_delayed_none(c, s, w):
x = dask.delayed(None)
y = dask.delayed(123)
[xx, yy] = c.compute([x, y])
assert await xx is None
assert await yy == 123
@pytest.mark.parametrize("typ", [tuple, list])
def test_tuple_futures_arg(client, typ):
x = client.submit(
make_time_dataframe,
)
df2 = client.submit(
pd.concat,
typ(
[
x,
]
),
)
dd.assert_eq(df2.result().iloc[:0], make_time_dataframe().iloc[:0])
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