def test_getitem():
df = pd.DataFrame(
{
"A": [1, 2, 3, 4, 5, 6, 7, 8, 9],
"B": [9, 8, 7, 6, 5, 4, 3, 2, 1],
"C": [True, False, True] * 3,
},
columns=list("ABC"),
)
ddf = dd.from_pandas(df, 2)
assert_eq(ddf["A"], df["A"])
# check cache consistency
tm.assert_series_equal(ddf["A"]._meta, ddf._meta["A"])
assert_eq(ddf[["A", "B"]], df[["A", "B"]])
tm.assert_frame_equal(ddf[["A", "B"]]._meta, ddf._meta[["A", "B"]])
assert_eq(ddf[ddf.C], df[df.C])
tm.assert_series_equal(ddf.C._meta, ddf._meta.C)
assert_eq(ddf[ddf.C.repartition([0, 2, 5, 8])], df[df.C])
pytest.raises(KeyError, lambda: df["X"])
pytest.raises(KeyError, lambda: df[["A", "X"]])
pytest.raises(AttributeError, lambda: df.X)
# not str/unicode
df = pd.DataFrame(np.random.randn(10, 5))
ddf = dd.from_pandas(df, 2)
assert_eq(ddf[0], df[0])
assert_eq(ddf[[1, 2]], df[[1, 2]])
pytest.raises(KeyError, lambda: df[8])
pytest.raises(KeyError, lambda: df[[1, 8]])
def test_to_hdf():
pytest.importorskip("tables")
df = pd.DataFrame({
"x": ["a", "b", "c", "d"],
"y": [1, 2, 3, 4]
},
index=[1.0, 2.0, 3.0, 4.0])
a = dd.from_pandas(df, 2)
with tmpfile("h5") as fn:
a.to_hdf(fn, "/data")
out = pd.read_hdf(fn, "/data")
tm.assert_frame_equal(df, out[:])
with tmpfile("h5") as fn:
a.x.to_hdf(fn, "/data")
out = pd.read_hdf(fn, "/data")
tm.assert_series_equal(df.x, out[:])
a = dd.from_pandas(df, 1)
with tmpfile("h5") as fn:
a.to_hdf(fn, "/data")
out = pd.read_hdf(fn, "/data")
tm.assert_frame_equal(df, out[:])
# test compute = False
with tmpfile("h5") as fn:
r = a.to_hdf(fn, "/data", compute=False)
r.compute()
out = pd.read_hdf(fn, "/data")
tm.assert_frame_equal(df, out[:])
def test_object_missing_values():
# Check that the presence of missing values doesn't change how object dtype
# is hashed.
s = pd.Series(["a", "b", "c", None])
h1 = hash_pandas_object(s).iloc[:3]
h2 = hash_pandas_object(s.iloc[:3])
tm.assert_series_equal(h1, h2)
def test_pivot_table_dtype():
df = pd.DataFrame({
"A": list("AABB"),
"B": pd.Categorical(list("ABAB")),
"C": [1, 2, 3, 4]
})
ddf = dd.from_pandas(df, 2)
res = dd.pivot_table(ddf,
index="A",
columns="B",
values="C",
aggfunc="count")
exp_index = pd.CategoricalIndex(["A", "B"], name="B")
exp = pd.Series([np.float64] * 2, index=exp_index)
tm.assert_series_equal(res.dtypes, exp)
exp = pd.pivot_table(df,
index="A",
columns="B",
values="C",
aggfunc="count").astype(np.float64)
assert_eq(res, exp)
def assert_eq(
a,
b,
check_names=True,
check_dtype=True,
check_divisions=True,
check_index=True,
scheduler="sync",
**kwargs,
):
if check_divisions:
assert_divisions(a, scheduler=scheduler)
assert_divisions(b, scheduler=scheduler)
if hasattr(a, "divisions") and hasattr(b, "divisions"):
at = type(np.asarray(a.divisions).tolist()[0]) # numpy to python
bt = type(np.asarray(b.divisions).tolist()[0]) # scalar conversion
assert at == bt, (at, bt)
assert_sane_keynames(a)
assert_sane_keynames(b)
a = _check_dask(a,
check_names=check_names,
check_dtypes=check_dtype,
scheduler=scheduler)
b = _check_dask(b,
check_names=check_names,
check_dtypes=check_dtype,
scheduler=scheduler)
if hasattr(a, "to_pandas"):
a = a.to_pandas()
if hasattr(b, "to_pandas"):
b = b.to_pandas()
if isinstance(a, (pd.DataFrame, pd.Series)):
a = _maybe_sort(a, check_index)
b = _maybe_sort(b, check_index)
if not check_index:
a = a.reset_index(drop=True)
b = b.reset_index(drop=True)
if isinstance(a, pd.DataFrame):
tm.assert_frame_equal(a,
b,
check_names=check_names,
check_dtype=check_dtype,
**kwargs)
elif isinstance(a, pd.Series):
tm.assert_series_equal(a,
b,
check_names=check_names,
check_dtype=check_dtype,
**kwargs)
elif isinstance(a, pd.Index):
tm.assert_index_equal(a, b, exact=check_dtype, **kwargs)
else:
if a == b:
return True
else:
if np.isnan(a):
assert np.isnan(b)
else:
assert np.allclose(a, b)
return True
def test_from_pandas_series():
n = 20
s = pd.Series(np.random.randn(n), index=pd.date_range(start="20120101", periods=n))
ds = dd.from_pandas(s, 3)
assert len(ds.dask) == 3
assert len(ds.divisions) == len(ds.dask) + 1
assert isinstance(ds.divisions[0], type(s.index[0]))
tm.assert_series_equal(s, ds.compute())
ds = dd.from_pandas(s, chunksize=8)
assert len(ds.dask) == 3
assert len(ds.divisions) == len(ds.dask) + 1
assert isinstance(ds.divisions[0], type(s.index[0]))
tm.assert_series_equal(s, ds.compute())
def test_categorical_consistency():
# Check that categoricals hash consistent with their values, not codes
# This should work for categoricals of any dtype
for s1 in [
pd.Series(["a", "b", "c", "d"]),
pd.Series([1000, 2000, 3000, 4000]),
pd.Series(pd.date_range(0, periods=4)),
]:
s2 = s1.astype("category").cat.set_categories(s1)
s3 = s2.cat.set_categories(list(reversed(s1)))
for categorize in [True, False]:
# These should all hash identically
h1 = hash_pandas_object(s1, categorize=categorize)
h2 = hash_pandas_object(s2, categorize=categorize)
h3 = hash_pandas_object(s3, categorize=categorize)
tm.assert_series_equal(h1, h2)
tm.assert_series_equal(h1, h3)
def test_concat_unions_categoricals():
# Categorical DataFrame, regular index
tm.assert_frame_equal(_concat(frames), pd.concat(frames2))
# Categorical Series, regular index
tm.assert_series_equal(_concat([i.y for i in frames]),
pd.concat([i.y for i in frames2]))
# Categorical Index
tm.assert_index_equal(_concat([i.index for i in frames3]),
pd.concat([i for i in frames4]).index)
# Categorical DataFrame, Categorical Index
tm.assert_frame_equal(_concat(frames3), pd.concat(frames4))
# Non-categorical DataFrame, Categorical Index
tm.assert_frame_equal(
_concat([i[["x", "z"]] for i in frames3]),
pd.concat([i[["x", "z"]] for i in frames4]),
)
# Categorical Series, Categorical Index
tm.assert_series_equal(_concat([i.z for i in frames3]),
pd.concat([i.z for i in frames4]))
# Non-categorical Series, Categorical Index
tm.assert_series_equal(_concat([i.x for i in frames3]),
pd.concat([i.x for i in frames4]))
# MultiIndex with Categorical Index
tm.assert_index_equal(_concat([i.index for i in frames5]),
pd.concat([i for i in frames6]).index)
# DataFrame, MultiIndex with CategoricalIndex
tm.assert_frame_equal(_concat(frames5), pd.concat(frames6))
