feat: linear imputation in Resample (#19393)

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 interpolation'],
             ['ffill', 'Forward values'],
             ['bfill', 'Backward values'],
             ['median', 'Median values'],

superset/utils/pandas_postprocessing/resample.py

@@ -20,6 +20,7 @@ import pandas as pd
 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 +92,8 @@ PROPHET_TIME_GRAIN_MAP = {
     "P1W/1970-01-04T00:00:00Z": "W",
 }
 
+RESAMPLE_METHOD = ("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],
+            },
+        )
+    )