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feat(plugin-chart-echarts): support non-timeseries x-axis (#17917)

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* feat(plugin-chart-echarts): support non-timeseries x-axis

* fix tests

* change formula return type from Date to number

* add x_axis test coverage

* rename func and improve coverage

* add x-axis control to bar chart

* remove redundant console.log

* fix description

* make x-axis control mandatory

* 🙃

* fix x-axis formatter

* fix showValues

* fix implicit rDTTM_ALIAS references in postProcessing

* replace TIME_COLUMN with DTTM_ALIAS

* fix remaining implicit indexes

* fix: Disable filtering on wide result sets (#18021)

* fix: handle null values in time-series table (#18039)

* cleanup column_type_mappings (#17569)

Signed-off-by: Đặng Minh Dũng <dungdm93@live.com>

* important change to MakeFile (#18037)

* add missing is_timeseries to pivot op

Co-authored-by: Erik Ritter <erik.ritter@airbnb.com>
Co-authored-by: Grace Guo <grace.guo@airbnb.com>
Co-authored-by: Đặng Minh Dũng <dungdm93@live.com>
Co-authored-by: AAfghahi <48933336+AAfghahi@users.noreply.github.com>
This commit is contained in:
Ville Brofeldt
2022-01-21 21:23:23 +02:00
committed by GitHub
parent b083b3421f
commit e9651ea52f
42 changed files with 489 additions and 201 deletions
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12
superset/utils/pandas_postprocessing.py
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@@ -14,6 +14,7 @@
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
# pylint: disable=too-many-lines
import logging
from decimal import Decimal
from functools import partial
@@ -785,6 +786,7 @@ def prophet( # pylint: disable=too-many-arguments
yearly_seasonality: Optional[Union[bool, int]] = None,
weekly_seasonality: Optional[Union[bool, int]] = None,
daily_seasonality: Optional[Union[bool, int]] = None,
index: Optional[str] = None,
) -> DataFrame:
"""
Add forecasts to each series in a timeseries dataframe, along with confidence
@@ -808,8 +810,10 @@ def prophet( # pylint: disable=too-many-arguments
:param daily_seasonality: Should daily seasonality be applied.
An integer value will specify Fourier order of seasonality, `None` will
automatically detect seasonality.
:param index: the name of the column containing the x-axis data
:return: DataFrame with contributions, with temporal column at beginning if present
"""
index = index or DTTM_ALIAS
# validate inputs
if not time_grain:
raise QueryObjectValidationError(_("Time grain missing"))
@@ -826,15 +830,15 @@ def prophet( # pylint: disable=too-many-arguments
raise QueryObjectValidationError(
_("Confidence interval must be between 0 and 1 (exclusive)")
)
if DTTM_ALIAS not in df.columns:
if index not in df.columns:
raise QueryObjectValidationError(_("DataFrame must include temporal column"))
if len(df.columns) < 2:
raise QueryObjectValidationError(_("DataFrame include at least one series"))
target_df = DataFrame()
for column in [column for column in df.columns if column != DTTM_ALIAS]:
for column in [column for column in df.columns if column != index]:
fit_df = _prophet_fit_and_predict(
df=df[[DTTM_ALIAS, column]].rename(columns={DTTM_ALIAS: "ds", column: "y"}),
df=df[[index, column]].rename(columns={index: "ds", column: "y"}),
confidence_interval=confidence_interval,
yearly_seasonality=_prophet_parse_seasonality(yearly_seasonality),
weekly_seasonality=_prophet_parse_seasonality(weekly_seasonality),
@@ -855,7 +859,7 @@ def prophet( # pylint: disable=too-many-arguments
for new_column in new_columns:
target_df = target_df.assign(**{new_column: fit_df[new_column]})
target_df.reset_index(level=0, inplace=True)
return target_df.rename(columns={"ds": DTTM_ALIAS})
return target_df.rename(columns={"ds": index})
def boxplot(