def test_plot_backends_incorrect(self):
fake_plot_backend = "none_plotting_module"
with ks.option_context("plotting.backend", fake_plot_backend):
self.assertEqual(ks.options.plotting.backend, fake_plot_backend)
with self.assertRaises(ValueError):
ks.plot._get_plot_backend(fake_plot_backend)
def test_plot_backends(self):
plot_backend = "plotly"
with ks.option_context("plotting.backend", plot_backend):
self.assertEqual(ks.options.plotting.backend, plot_backend)
module = ks.plot._get_plot_backend(plot_backend)
self.assertEqual(module.__name__, plot_backend)
def test_plot_backends(self):
plot_backend = "plotly"
with ks.option_context("plotting.backend", plot_backend):
self.assertEqual(ks.options.plotting.backend, plot_backend)
module = KoalasPlotAccessor._get_plot_backend(plot_backend)
self.assertEqual(module.__name__, "databricks.koalas.plot.plotly")
def indexer_between_time(
self,
start_time: Union[datetime.time, str],
end_time: Union[datetime.time, str],
include_start: bool = True,
include_end: bool = True,
) -> Index:
"""
Return index locations of values between particular times of day
(e.g., 9:00-9:30AM).
Parameters
----------
start_time, end_time : datetime.time, str
Time passed either as object (datetime.time) or as string in
appropriate format ("%H:%M", "%H%M", "%I:%M%p", "%I%M%p",
"%H:%M:%S", "%H%M%S", "%I:%M:%S%p","%I%M%S%p").
include_start : bool, default True
include_end : bool, default True
Returns
-------
values_between_time : Index of integers
Examples
--------
>>> kidx = ks.date_range("2000-01-01", periods=3, freq="T")
>>> kidx # doctest: +NORMALIZE_WHITESPACE
DatetimeIndex(['2000-01-01 00:00:00', '2000-01-01 00:01:00',
'2000-01-01 00:02:00'],
dtype='datetime64[ns]', freq=None)
>>> kidx.indexer_between_time("00:01", "00:02").sort_values()
Int64Index([1, 2], dtype='int64')
>>> kidx.indexer_between_time("00:01", "00:02", include_end=False)
Int64Index([1], dtype='int64')
>>> kidx.indexer_between_time("00:01", "00:02", include_start=False)
Int64Index([2], dtype='int64')
"""
def pandas_between_time(pdf) -> ks.DataFrame[int]:
return pdf.between_time(start_time, end_time, include_start,
include_end)
kdf = self.to_frame()[[]]
id_column_name = verify_temp_column_name(kdf, "__id_column__")
kdf = kdf.koalas.attach_id_column("distributed-sequence",
id_column_name)
with ks.option_context("compute.default_index_type", "distributed"):
# The attached index in the statement below will be dropped soon,
# so we enforce “distributed” default index type
kdf = kdf.koalas.apply_batch(pandas_between_time)
return ks.Index(first_series(kdf).rename(self.name))
def indexer_at_time(self, time: Union[datetime.time, str], asof: bool = False) -> Index:
"""
Return index locations of values at particular time of day
(e.g. 9:30AM).
Parameters
----------
time : datetime.time or str
Time passed in either as object (datetime.time) or as string in
appropriate format ("%H:%M", "%H%M", "%I:%M%p", "%I%M%p",
"%H:%M:%S", "%H%M%S", "%I:%M:%S%p", "%I%M%S%p").
Returns
-------
values_at_time : Index of integers
Examples
--------
>>> kidx = ks.date_range("2000-01-01", periods=3, freq="T")
>>> kidx # doctest: +NORMALIZE_WHITESPACE
DatetimeIndex(['2000-01-01 00:00:00', '2000-01-01 00:01:00',
'2000-01-01 00:02:00'],
dtype='datetime64[ns]', freq=None)
>>> kidx.indexer_at_time("00:00")
Int64Index([0], dtype='int64')
>>> kidx.indexer_at_time("00:01")
Int64Index([1], dtype='int64')
"""
if asof:
raise NotImplementedError("'asof' argument is not supported")
def pandas_at_time(pdf) -> ks.DataFrame[int]:
return pdf.at_time(time, asof)
kdf = self.to_frame()[[]]
id_column_name = verify_temp_column_name(kdf, "__id_column__")
kdf = kdf.koalas.attach_id_column("distributed-sequence", id_column_name)
with ks.option_context("compute.default_index_type", "distributed"):
# The attached index in the statement below will be dropped soon,
# so we enforce “distributed” default index type
kdf = kdf.koalas.apply_batch(pandas_at_time)
return ks.Index(first_series(kdf).rename(self.name))
def test_default_index_distributed(self):
with ks.option_context("compute.default_index_type", "distributed"):
sdf = self.spark.range(1000)
pdf = ks.DataFrame(sdf).to_pandas()
self.assertEqual(len(set(pdf.index)), len(pdf))
def test_default_index_distributed_sequence(self):
with ks.option_context("compute.default_index_type",
"distributed-sequence"):
sdf = self.spark.range(1000)
self.assert_eq(ks.DataFrame(sdf),
pd.DataFrame({"id": list(range(1000))}))
def test_groupby_apply_without_shortcut(self):
with ks.option_context("compute.shortcut_limit", 0):
self.test_groupby_apply()