def test_exceptions():
sdf = StreamingDataFrame(columns=['x', 'y'])
with pytest.raises(TypeError):
sdf.emit(1)
with pytest.raises(IndexError):
sdf.emit(pd.DataFrame())
def test_pair_arithmetic():
df = pd.DataFrame({'x': list(range(10)), 'y': [1] * 10})
a = StreamingDataFrame(example=df.iloc[0])
L = ((a.x + a.y) * 2).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_arithmetic():
a = StreamingDataFrame(columns=['x', 'y'])
b = a + 1
L1 = b.stream.sink_to_list()
c = b.x * 10
L2 = c.stream.sink_to_list()
df = pd.DataFrame({'x': [1, 2, 3], 'y': [4, 5, 6]})
a.emit(df)
assert assert_eq(L1[0], df + 1)
assert assert_eq(L2[0], (df + 1).x * 10)
def test_sum():
sdf = StreamingDataFrame(columns=['x', 'y'])
df_out = sdf.sum().stream.sink_to_list()
x = sdf.x
x_out = (x.sum() + 1).stream.sink_to_list()
df = pd.DataFrame({'x': [1, 2, 3], 'y': [4, 5, 6]})
sdf.emit(df)
sdf.emit(df)
assert assert_eq(df_out[0], df.sum())
assert assert_eq(df_out[1], df.sum() + df.sum())
assert x_out[0] == df.x.sum() + 1
assert x_out[1] == df.x.sum() + df.x.sum() + 1
def test_mean():
sdf = StreamingDataFrame(columns=['x', 'y'])
mean = sdf.mean()
assert isinstance(mean, StreamingSeries)
df_out = mean.stream.sink_to_list()
x = sdf.x
x_out = x.mean().stream.sink_to_list()
df = pd.DataFrame({'x': [1, 2, 3], 'y': [4, 5, 6]})
sdf.emit(df)
sdf.emit(df)
assert assert_eq(df_out[0], df.mean())
assert assert_eq(df_out[1], df.mean())
assert x_out[0] == df.x.mean()
assert x_out[1] == df.x.mean()
def test_identity():
sdf = StreamingDataFrame(columns=['x', 'y'])
L = sdf.stream.sink_to_list()
df = pd.DataFrame({'x': [1, 2, 3], 'y': [4, 5, 6]})
sdf.emit(df)
assert L[0] is df
assert list(sdf.example.columns) == ['x', 'y']
x = sdf.x
assert isinstance(x, StreamingSeries)
L2 = x.stream.sink_to_list()
assert not L2
sdf.emit(df)
assert isinstance(L2[0], pd.Series)
assert assert_eq(L2[0], df.x)
def test_rolling():
df = pd.DataFrame({'x': [1, 2, 3], 'y': [4, 5, 6]})
sdf = StreamingDataFrame(example=df)
out = sdf.rolling(3, min_periods=2)
L = out.stream.sink_to_list()
for i in range(10):
df = pd.DataFrame({'x': [i], 'y': [i + 1]})
sdf.emit(df)
assert len(L) == 9
assert all(len(df) >= 2 for df in L)
for i, df in enumerate(L[1:]):
expected = pd.DataFrame({
'x': [i, i + 1, i + 2],
'y': [i + 1, i + 2, i + 3]
})
tm.assert_frame_equal(df.reset_index(drop=True), expected)
def test_rolling_time():
now = pd.Timestamp.now()
df = pd.DataFrame({'x': [1], 'y': [4]}, index=[now])
sdf = StreamingDataFrame(example=df)
L = sdf.rolling('10ms').stream.sink_to_list()
for i in range(100):
df = pd.DataFrame({
'x': [i, i + 0.5],
'y': [i, i + 0.5]
},
index=[pd.Timestamp.now(),
pd.Timestamp.now()])
sdf.emit(df)
time.sleep(0.001)
assert L
for df in L[3:]:
assert df.index.max() - df.index.min() < pd.Timedelta('10ms')
assert df.index.max() - df.index.min() > pd.Timedelta('1ms')
def test_groupby_aggregate(agg, grouper, indexer):
df = pd.DataFrame({
'x': (np.arange(10) // 2).astype(float),
'y': [1.0] * 10
})
a = StreamingDataFrame(example=df.iloc[:0])
L = getattr(indexer(a.groupby(grouper(a))), agg)().stream.sink_to_list()
a.emit(df.iloc[:3])
a.emit(df.iloc[3:7])
a.emit(df.iloc[7:])
assert assert_eq(L[-1], getattr(indexer(df.groupby(grouper(df))), agg)())