diff --git a/superset/charts/schemas.py b/superset/charts/schemas.py
index 9916c0b221ec..9a41c54896d2 100644
--- a/superset/charts/schemas.py
+++ b/superset/charts/schemas.py
@@ -749,6 +749,7 @@ class ChartDataPostProcessingOperationSchema(Schema):
                 "sort",
                 "diff",
                 "compare",
+                "resample",
             )
         ),
         example="aggregate",
diff --git a/superset/utils/pandas_postprocessing.py b/superset/utils/pandas_postprocessing.py
index ad6c6afedade..23761216a6b0 100644
--- a/superset/utils/pandas_postprocessing.py
+++ b/superset/utils/pandas_postprocessing.py
@@ -916,3 +916,29 @@ def outliers(series: Series) -> Set[float]:
         for metric in metrics
     }
     return aggregate(df, groupby=groupby, aggregates=aggregates)
+
+
+def resample(
+    df: DataFrame,
+    rule: str,
+    method: str,
+    time_column: str,
+    fill_value: Optional[Union[float, int]] = None,
+) -> DataFrame:
+    """
+    resample a timeseries dataframe.
+
+    :param df: DataFrame to resample.
+    :param rule: The offset string representing target conversion.
+    :param method: How to fill the NaN value after resample.
+    :param time_column: existing columns in DataFrame.
+    :param fill_value: What values do fill missing.
+    :return: DataFrame after resample
+    :raises QueryObjectValidationError: If the request in incorrect
+    """
+    df = df.set_index(time_column)
+    if method == "asfreq" and fill_value is not None:
+        df = df.resample(rule).asfreq(fill_value=fill_value)
+    else:
+        df = getattr(df.resample(rule), method)()
+    return df.reset_index()
diff --git a/tests/integration_tests/pandas_postprocessing_tests.py b/tests/integration_tests/pandas_postprocessing_tests.py
index 1763dad33614..3e8fdcecda82 100644
--- a/tests/integration_tests/pandas_postprocessing_tests.py
+++ b/tests/integration_tests/pandas_postprocessing_tests.py
@@ -870,3 +870,22 @@ def test_boxplot_percentile_incorrect_params(self):
                 metrics=["cars"],
                 percentiles=[10, 90, 10],
             )
+
+    def test_resample(self):
+        df = timeseries_df.copy()
+        df.index.name = "time_column"
+        df.reset_index(inplace=True)
+
+        post_df = proc.resample(
+            df=df, rule="1D", method="ffill", time_column="time_column",
+        )
+        self.assertListEqual(
+            post_df["label"].tolist(), ["x", "y", "y", "y", "z", "z", "q"]
+        )
+        self.assertListEqual(post_df["y"].tolist(), [1.0, 2.0, 2.0, 2.0, 3.0, 3.0, 4.0])
+
+        post_df = proc.resample(
+            df=df, rule="1D", method="asfreq", time_column="time_column", fill_value=0,
+        )
+        self.assertListEqual(post_df["label"].tolist(), ["x", "y", 0, 0, "z", 0, "q"])
+        self.assertListEqual(post_df["y"].tolist(), [1.0, 2.0, 0, 0, 3.0, 0, 4.0])