def test_groupby_aggregate_updating(stream):
df = cudf.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_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'
from hvplot import renderer
from streamz import Stream
from streamz.dataframe import DataFrame
# Set up streaming DataFrame
stream = Stream()
index = pd.DatetimeIndex([])
example = pd.DataFrame({'x': [], 'y': [], 'z': []},
columns=['x', 'y', 'z'], index=index)
df = DataFrame(stream, example=example)
cumulative = df.cumsum()[['x', 'z']]
# Declare plots
line = cumulative.hvplot.line(width=400)
scatter = cumulative.hvplot.scatter(width=400)
bars = df.groupby('y').sum().hvplot.bar(width=400)
box = df.hvplot.box(width=400)
kde = df.x.hvplot.kde(width=400)
# Compose plots
layout = (line * scatter + bars + box + kde).cols(2)
# Set up callback with streaming data
def emit():
now = pd.datetime.now()
delta = np.timedelta64(500, 'ms')
index = pd.date_range(np.datetime64(now)-delta, now, freq='100ms')
df = pd.DataFrame({'x': np.random.randn(len(index)),
'y': np.random.randint(0, 10, len(index)),
'z': np.random.randn(len(index))},
columns=['x', 'y', 'z'], index=index)
