feat: linear imputation in Resample

superset-frontend/packages/superset-ui-chart-controls/src/sections/advancedAnalytics.tsx

@@ -170,6 +170,7 @@ export const advancedAnalyticsControls: ControlPanelSectionConfig = {
  choices: [
  ['asfreq', 'Null imputation'],
  ['zerofill', 'Zero imputation'],
+ ['linear', 'Linear imputation'],
  ['ffill', 'Forward values'],
  ['bfill', 'Backward values'],
  ['median', 'Median values'],

superset/utils/pandas_postprocessing/resample.py

@@ -20,6 +20,7 @@
 from flask_babel import gettext as _
 
 from superset.exceptions import InvalidPostProcessingError
+from superset.utils.pandas_postprocessing.utils import RESAMPLE_METHOD
 
 
 def resample(
@@ -40,9 +41,15 @@ def resample(
  """
  if not isinstance(df.index, pd.DatetimeIndex):
  raise InvalidPostProcessingError(_("Resample operation requires DatetimeIndex"))
+ if method not in RESAMPLE_METHOD:
+ raise InvalidPostProcessingError(
+ _("Resample method should in ") + ", ".join(RESAMPLE_METHOD) + "."
+ )
 
  if method == "asfreq" and fill_value is not None:
  _df = df.resample(rule).asfreq(fill_value=fill_value)
+ elif method == "linear":
+ _df = df.resample(rule).interpolate()
  else:
  _df = getattr(df.resample(rule), method)()
  return _df

superset/utils/pandas_postprocessing/utils.py

@@ -92,6 +92,10 @@
  "P1W/1970-01-04T00:00:00Z": "W",
 }
 
+RESAMPLE_METHOD = tuple(
+ ["asfreq", "bfill", "ffill", "linear", "median", "mean", "sum",]
+)
+
 FLAT_COLUMN_SEPARATOR = ", "
 
 

tests/unit_tests/pandas_postprocessing/test_resample.py

@@ -14,8 +14,10 @@
 # KIND, either express or implied. See the License for the
 # specific language governing permissions and limitations
 # under the License.
+import numpy as np
 import pandas as pd
 import pytest
+from pandas import to_datetime
 
 from superset.exceptions import InvalidPostProcessingError
 from superset.utils import pandas_postprocessing as pp
@@ -151,3 +153,47 @@ def test_resample_should_raise_ex():
  pp.resample(
  df=categories_df, rule="1D", method="asfreq",
  )
+
+ with pytest.raises(InvalidPostProcessingError):
+ pp.resample(
+ df=timeseries_df, rule="1D", method="foobar",
+ )
+
+
+def test_resample_linear():
+ df = pd.DataFrame(
+ index=to_datetime(["2019-01-01", "2019-01-05", "2019-01-08"]),
+ data={"label": ["a", "e", "j"], "y": [1.0, 5.0, 8.0]},
+ )
+ post_df = pp.resample(df=df, rule="1D", method="linear")
+ """
+ label y
+ 2019-01-01 a 1.0
+ 2019-01-02 NaN 2.0
+ 2019-01-03 NaN 3.0
+ 2019-01-04 NaN 4.0
+ 2019-01-05 e 5.0
+ 2019-01-06 NaN 6.0
+ 2019-01-07 NaN 7.0
+ 2019-01-08 j 8.0
+ """
+ assert post_df.equals(
+ pd.DataFrame(
+ index=pd.to_datetime(
+ [
+ "2019-01-01",
+ "2019-01-02",
+ "2019-01-03",
+ "2019-01-04",
+ "2019-01-05",
+ "2019-01-06",
+ "2019-01-07",
+ "2019-01-08",
+ ]
+ ),
+ data={
+ "label": ["a", np.NaN, np.NaN, np.NaN, "e", np.NaN, np.NaN, "j"],
+ "y": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0],
+ },
+ )
+ )