def test_from_dask_dataframe():
np.random.seed(0)
df = pd.DataFrame({'x': np.random.randint(0, 5, size=20),
'y': np.random.normal(size=20)})
ddf = dd.from_pandas(df, npartitions=2)
dgdf = dgd.from_dask_dataframe(ddf)
got = dgdf.compute().to_pandas()
expect = df
np.testing.assert_array_equal(got.index.values, expect.index.values)
np.testing.assert_array_equal(got.x.values, expect.x.values)
np.testing.assert_array_equal(got.y.values, expect.y.values)
def test_set_index(nelem):
np.random.seed(0)
# Use unique index range as the sort may not be stable-ordering
x = np.arange(nelem)
np.random.shuffle(x)
df = pd.DataFrame({'x': x,
'y': np.random.randint(0, nelem, size=nelem)})
ddf = dd.from_pandas(df, npartitions=2)
dgdf = dgd.from_dask_dataframe(ddf)
expect = ddf.set_index('x').compute()
got = dgdf.set_index('x').compute().to_pandas()
np.testing.assert_array_equal(got.index.values, expect.index.values)
np.testing.assert_array_equal(got.y.values, expect.y.values)
assert got.columns == expect.columns
def test_take(nelem, nparts):
np.random.seed(0)
# # Use unique index range as the sort may not be stable-ordering
x = np.random.randint(0, nelem, size=nelem)
y = np.random.random(nelem)
selected = np.random.randint(0, nelem - 1, size=nelem // 2)
df = pd.DataFrame({'x': x, 'y': y})
ddf = dd.from_pandas(df, npartitions=nparts)
dgdf = dgd.from_dask_dataframe(ddf)
out = dgdf.take(gd.Series(selected), npartitions=5)
got = out.compute().to_pandas()
expect = df.take(selected)
assert 1 < out.npartitions <= 5
np.testing.assert_array_equal(got.index, np.arange(len(got)))
np.testing.assert_array_equal(got.x, expect.x)
np.testing.assert_array_equal(got.y, expect.y)