def test_groupby_aggregate_updating(stream):
df = pd.DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]})
sdf = DataFrame(example=df, stream=stream)
assert sdf.groupby("x").y.mean()._stream_type == "updating"
assert sdf.x.sum()._stream_type == "updating"
assert (sdf.x.sum() + 1)._stream_type == "updating"
def test_to_frame(stream):
df = pd.DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]})
sdf = DataFrame(example=df, stream=stream)
assert sdf.to_frame() is sdf
a = sdf.x.to_frame()
assert isinstance(a, DataFrame)
assert list(a.columns) == ["x"]
def test_dataframe_simple(func):
df = pd.DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]})
expected = func(df)
a = DataFrame(example=df)
L = func(a).stream.sink_to_list()
a.emit(df)
assert_eq(L[0], expected)
def test_getitem(stream):
df = pd.DataFrame({"x": list(range(10)), "y": [1] * 10})
a = DataFrame(example=df.iloc[:0], stream=stream)
L = a[a.x > 4].stream.gather().sink_to_list()
a.emit(df.iloc[:5])
a.emit(df.iloc[5:])
assert len(L) == 2
assert_eq(pd.concat(L, axis=0), df[df.x > 4])
def test_pair_arithmetic(stream):
df = pd.DataFrame({"x": list(range(10)), "y": [1] * 10})
a = DataFrame(example=df.iloc[:0], stream=stream)
L = ((a.x + a.y) * 2).stream.gather().sink_to_list()
a.emit(df.iloc[:5])
a.emit(df.iloc[5:])
assert len(L) == 2
assert_eq(pd.concat(L, axis=0), (df.x + df.y) * 2)
def test_index(stream):
df = pd.DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]})
a = DataFrame(example=df, stream=stream)
b = a.index + 5
L = b.stream.gather().sink_to_list()
a.emit(df)
a.emit(df)
assert_eq(L[0], df.index + 5)
assert_eq(L[1], df.index + 5)
def test_binary_stream_operators(stream):
df = pd.DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]})
expected = df.x + df.y
a = DataFrame(example=df, stream=stream)
b = (a.x + a.y).stream.gather().sink_to_list()
a.emit(df)
assert_eq(b[0], expected)
def test_tail(stream):
df = pd.DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]})
sdf = DataFrame(example=df, stream=stream)
L = sdf.tail(2).stream.gather().sink_to_list()
sdf.emit(df)
sdf.emit(df)
assert_eq(L[0], df.tail(2))
assert_eq(L[1], df.tail(2))
def test_unary_operators(op, getter):
df = pd.DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]})
try:
expected = op(getter(df))
except Exception:
return
a = DataFrame(example=df)
b = op(getter(a)).stream.sink_to_list()
a.emit(df)
assert_eq(b[0], expected)
def test_window_sum(stream):
df = pd.DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]})
sdf = DataFrame(example=df, stream=stream)
L = sdf.window(n=4).x.sum().stream.gather().sink_to_list()
sdf.emit(df)
assert L == [6]
sdf.emit(df)
assert L == [6, 9]
sdf.emit(df)
assert L == [6, 9, 9]
def test_reductions(stream, func):
df = pd.DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]})
for example in [df, df.iloc[:0]]:
sdf = DataFrame(example=example, stream=stream)
df_out = func(sdf).stream.gather().sink_to_list()
x = sdf.x
x_out = func(x).stream.gather().sink_to_list()
sdf.emit(df)
sdf.emit(df)
assert_eq(df_out[-1], func(pd.concat([df, df])))
assert_eq(x_out[-1], func(pd.concat([df, df]).x))
def test_binary_operators(op, getter, stream):
df = pd.DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]})
try:
left = op(getter(df), 2)
right = op(2, getter(df))
except Exception:
return
a = DataFrame(example=df, stream=stream)
l = op(getter(a), 2).stream.gather().sink_to_list()
r = op(2, getter(a)).stream.gather().sink_to_list()
a.emit(df)
assert_eq(l[0], left)
assert_eq(r[0], right)
def test_dtype(stream):
df = pd.DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]})
sdf = DataFrame(example=df, stream=stream)
assert str(sdf.dtypes) == str(df.dtypes)
assert sdf.x.dtype == df.x.dtype
assert sdf.index.dtype == df.index.dtype
def test_window_sum_dataframe(stream):
df = pd.DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]})
sdf = DataFrame(example=df, stream=stream)
L = sdf.window(n=4).sum().stream.gather().sink_to_list()
sdf.emit(df)
assert_eq(L[0], pd.Series([6, 15], index=["x", "y"]))
sdf.emit(df)
assert_eq(L[0], pd.Series([6, 15], index=["x", "y"]))
assert_eq(L[1], pd.Series([9, 21], index=["x", "y"]))
sdf.emit(df)
assert_eq(L[0], pd.Series([6, 15], index=["x", "y"]))
assert_eq(L[1], pd.Series([9, 21], index=["x", "y"]))
assert_eq(L[2], pd.Series([9, 21], index=["x", "y"]))
def test_groupby_windowing_value(func, value, getter, grouper, indexer):
index = pd.DatetimeIndex(start="2000-01-01", end="2000-01-03", freq="1h")
df = pd.DataFrame(
{
"x": np.arange(len(index), dtype=float),
"y": np.arange(len(index), dtype=float) % 2,
},
index=index,
)
sdf = DataFrame(example=df)
def f(x):
return func(indexer(x.groupby(grouper(x))))
L = f(sdf.window(value)).stream.gather().sink_to_list()
value = pd.Timedelta(value)
diff = 13
for i in range(0, len(index), diff):
sdf.emit(df.iloc[i : i + diff])
sdf.emit(df.iloc[:0])
assert len(L) == 5
first = df.iloc[:diff]
lost = first[first.index.min() + value :]
first = first.iloc[len(lost) :]
assert_eq(L[0], f(first))
last = df.loc[index.max() - value + pd.Timedelta("1s") :]
assert_eq(L[-1], f(last))
def test_window_full():
df = pd.DataFrame({"x": np.arange(10, dtype=float), "y": [1.0, 2.0] * 5})
sdf = DataFrame(example=df)
L = sdf.window(n=4).apply(lambda x: x).stream.sink_to_list()
sdf.emit(df.iloc[:3])
sdf.emit(df.iloc[3:8])
sdf.emit(df.iloc[8:])
assert_eq(L[0], df.iloc[:3])
assert_eq(L[1], df.iloc[4:8])
assert_eq(L[2], df.iloc[-4:])
def test_rolling_count_aggregations(
op, window, m, pre_get, post_get, kwargs, stream
):
index = pd.DatetimeIndex(start="2000-01-01", end="2000-01-03", freq="1h")
df = pd.DataFrame({"x": np.arange(len(index))}, index=index)
expected = getattr(post_get(pre_get(df).rolling(window)), op)(**kwargs)
sdf = DataFrame(example=df.iloc[:0], stream=stream)
roll = getattr(post_get(pre_get(sdf).rolling(window)), op)(**kwargs)
L = roll.stream.gather().sink_to_list()
assert len(L) == 0
for i in range(0, len(df), m):
sdf.emit(df.iloc[i : i + m])
assert len(L) > 1
assert_eq(pd.concat(L), expected)
def test_display(stream):
pytest.importorskip("ipywidgets")
pytest.importorskip("IPython")
df = pd.DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]})
sdf = DataFrame(example=df, stream=stream)
s = sdf.x.sum()
s._ipython_display_()
def test_attributes():
df = pd.DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]})
sdf = DataFrame(example=df)
assert "x" in dir(sdf)
assert "z" not in dir(sdf)
sdf.x
with pytest.raises(AttributeError):
sdf.z
def test_repr_html(stream):
df = pd.DataFrame(
{"x": (np.arange(10) // 2).astype(float), "y": [1.0] * 10}
)
a = DataFrame(example=df, stream=stream)
for x in [a, a.y, a.y.mean()]:
html = x._repr_html_()
assert type(x).__name__ in html
assert "1" in html
def test_instantiate_with_dict(stream):
df = pd.DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]})
sdf = DataFrame(example=df, stream=stream)
sdf2 = DataFrame({"a": sdf.x, "b": sdf.x * 2, "c": sdf.y % 2})
L = sdf2.stream.gather().sink_to_list()
assert len(sdf2.columns) == 3
sdf.emit(df)
sdf.emit(df)
assert len(L) == 2
for x in L:
assert_eq(
x[["a", "b", "c"]],
pd.DataFrame(
{"a": df.x, "b": df.x * 2, "c": df.y % 2},
columns=["a", "b", "c"],
),
)
def test_setitem(stream):
df = pd.DataFrame({"x": list(range(10)), "y": [1] * 10})
sdf = DataFrame(example=df.iloc[:0], stream=stream)
stream = sdf.stream
sdf["z"] = sdf["x"] * 2
sdf["a"] = 10
sdf[["c", "d"]] = sdf[["x", "y"]]
L = sdf.mean().stream.gather().sink_to_list()
stream.emit(df.iloc[:3])
stream.emit(df.iloc[3:7])
stream.emit(df.iloc[7:])
df["z"] = df["x"] * 2
df["a"] = 10
df[["c", "d"]] = df[["x", "y"]]
assert_eq(L[-1], df.mean())
def test_exceptions(stream):
df = pd.DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]})
sdf = DataFrame(example=df, stream=stream)
with pytest.raises(TypeError):
sdf.emit(1)
with pytest.raises(IndexError):
sdf.emit(pd.DataFrame())
def test_setitem_overwrites(stream):
df = pd.DataFrame({"x": list(range(10))})
sdf = DataFrame(example=df.iloc[:0], stream=stream)
stream = sdf.stream
sdf["x"] = sdf["x"] * 2
L = sdf.stream.gather().sink_to_list()
stream.emit(df.iloc[:3])
stream.emit(df.iloc[3:7])
stream.emit(df.iloc[7:])
assert_eq(L[-1], df.iloc[7:] * 2)
def test_gc():
sdf = sd.Random(freq="5ms", interval="100ms")
a = DataFrame(
{
"volatility": sdf.x.rolling("100ms").var(),
"sub": sdf.x - sdf.x.rolling("100ms").mean(),
}
)
n = len(sdf.stream.downstreams)
yield gen.sleep(0.1)
a = DataFrame(
{
"volatility": sdf.x.rolling("100ms").var(),
"sub": sdf.x - sdf.x.rolling("100ms").mean(),
}
)
yield gen.sleep(0.1)
a = DataFrame(
{
"volatility": sdf.x.rolling("100ms").var(),
"sub": sdf.x - sdf.x.rolling("100ms").mean(),
}
)
yield gen.sleep(0.1)
a = DataFrame(
{
"volatility": sdf.x.rolling("100ms").var(),
"sub": sdf.x - sdf.x.rolling("100ms").mean(),
}
)
assert len(sdf.stream.downstreams) == n
del a
import gc
gc.collect()
assert len(sdf.stream.downstreams) == 0
def test_repr(stream):
df = pd.DataFrame(
{"x": (np.arange(10) // 2).astype(float), "y": [1.0] * 10}
)
a = DataFrame(example=df, stream=stream)
text = repr(a)
assert type(a).__name__ in text
assert "x" in text
assert "y" in text
text = repr(a.x)
assert type(a.x).__name__ in text
assert "x" in text
text = repr(a.x.sum())
assert type(a.x.sum()).__name__ in text
def test_groupby_aggregate(agg, grouper, indexer, stream):
df = pd.DataFrame(
{"x": (np.arange(10) // 2).astype(float), "y": [1.0, 2.0] * 5}
)
a = DataFrame(example=df.iloc[:0], stream=stream)
def f(x):
return agg(indexer(x.groupby(grouper(x))))
L = f(a).stream.gather().sink_to_list()
a.emit(df.iloc[:3])
a.emit(df.iloc[3:7])
a.emit(df.iloc[7:])
first = df.iloc[:3]
assert assert_eq(L[0], f(first))
assert assert_eq(L[-1], f(df))
def test_windowing_n(func, n, getter):
df = pd.DataFrame({"x": list(range(10)), "y": [1, 2] * 5})
sdf = DataFrame(example=df)
L = func(getter(sdf).window(n=n) + 10).stream.gather().sink_to_list()
for i in range(0, 10, 3):
sdf.emit(df.iloc[i : i + 3])
sdf.emit(df.iloc[:0])
assert len(L) == 5
assert_eq(L[0], func(getter(df).iloc[max(0, 3 - n) : 3] + 10))
assert_eq(L[-1], func(getter(df).iloc[len(df) - n :] + 10))
def test_cumulative_aggregations(op, getter, stream):
df = pd.DataFrame({"x": list(range(10)), "y": [1] * 10})
expected = getattr(getter(df), op)()
sdf = DataFrame(example=df, stream=stream)
L = getattr(getter(sdf), op)().stream.gather().sink_to_list()
for i in range(0, 10, 3):
sdf.emit(df.iloc[i : i + 3])
sdf.emit(df.iloc[:0])
assert len(L) > 1
assert_eq(pd.concat(L), expected)
def test_identity(stream):
df = pd.DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]})
sdf = DataFrame(example=df, stream=stream)
L = sdf.stream.gather().sink_to_list()
sdf.emit(df)
assert L[0] is df
assert list(sdf.example.columns) == ["x", "y"]
x = sdf.x
assert isinstance(x, Series)
L2 = x.stream.gather().sink_to_list()
assert not L2
sdf.emit(df)
assert isinstance(L2[0], pd.Series)
assert assert_eq(L2[0], df.x)
