feat: Add geospatial post processing operations

requirements.txt

@@ -34,7 +34,7 @@ flask-talisman==0.7.0     # via apache-superset (setup.py)
 flask-wtf==0.14.2         # via apache-superset (setup.py), flask-appbuilder
 flask==1.1.1              # via apache-superset (setup.py), flask-appbuilder, flask-babel, flask-caching, flask-compress, flask-jwt-extended, flask-login, flask-migrate, flask-openid, flask-sqlalchemy, flask-wtf
 geographiclib==1.50       # via geopy
-geopy==1.20.0             # via apache-superset (setup.py)
+geopy==1.21.0             # via apache-superset (setup.py)
 gunicorn==20.0.4          # via apache-superset (setup.py)
 humanize==0.5.1           # via apache-superset (setup.py)
 importlib-metadata==1.4.0  # via jsonschema, kombu

superset/charts/schemas.py

@@ -265,15 +265,23 @@ class ChartDataSelectOptionsSchema(ChartDataPostProcessingOperationOptionsSchema
     columns = fields.List(
         fields.String(),
         description="Columns which to select from the input data, in the desired "
-        "order. If columns are renamed, the old column name should be "
+        "order. If columns are renamed, the original column name should be "
         "referenced here.",
         example=["country", "gender", "age"],
+        required=False,
+    )
+    drop = fields.List(
+        fields.String(),
+        description="Columns which to drop from selection.",
+        example=["my_temp_column"],
+        required=False,
     )
     rename = fields.List(
         fields.Dict(),
         description="columns which to rename, mapping source column to target column. "
         "For instance, `{'y': 'y2'}` will rename the column `y` to `y2`.",
         example=[{"age": "average_age"}],
+        required=False,
     )
 
 
@@ -335,12 +343,81 @@ class ChartDataPivotOptionsSchema(ChartDataPostProcessingOperationOptionsSchema)
     aggregates = ChartDataAggregateConfigField()
 
 
+class ChartDataGeohashDecodeOptionsSchema(
+    ChartDataPostProcessingOperationOptionsSchema
+):
+    """
+    Geohash decode operation config.
+    """
+
+    geohash = fields.String(
+        description="Name of source column containing geohash string", required=True,
+    )
+    latitude = fields.String(
+        description="Name of target column for decoded latitude", required=True,
+    )
+    longitude = fields.String(
+        description="Name of target column for decoded longitude", required=True,
+    )
+
+
+class ChartDataGeohashEncodeOptionsSchema(
+    ChartDataPostProcessingOperationOptionsSchema
+):
+    """
+    Geohash encode operation config.
+    """
+
+    latitude = fields.String(
+        description="Name of source latitude column", required=True,
+    )
+    longitude = fields.String(
+        description="Name of source longitude column", required=True,
+    )
+    geohash = fields.String(
+        description="Name of target column for encoded geohash string", required=True,
+    )
+
+
+class ChartDataGeodeticParseOptionsSchema(
+    ChartDataPostProcessingOperationOptionsSchema
+):
+    """
+    Geodetic point string parsing operation config.
+    """
+
+    geodetic = fields.String(
+        description="Name of source column containing geodetic point strings",
+        required=True,
+    )
+    latitude = fields.String(
+        description="Name of target column for decoded latitude", required=True,
+    )
+    longitude = fields.String(
+        description="Name of target column for decoded longitude", required=True,
+    )
+    altitude = fields.String(
+        description="Name of target column for decoded longitude. If omitted, "
+        "altitude information in geodetic string is ignored.",
+        required=False,
+    )
+
+
 class ChartDataPostProcessingOperationSchema(Schema):
     operation = fields.String(
         description="Post processing operation type",
         required=True,
         validate=validate.OneOf(
-            choices=("aggregate", "pivot", "rolling", "select", "sort")
+            choices=(
+                "aggregate",
+                "geodetic_parse",
+                "geohash_decode",
+                "geohash_encode",
+                "pivot",
+                "rolling",
+                "select",
+                "sort",
+            )
         ),
         example="aggregate",
     )
@@ -638,4 +715,7 @@ class ChartDataResponseSchema(Schema):
     ChartDataRollingOptionsSchema,
     ChartDataSelectOptionsSchema,
     ChartDataSortOptionsSchema,
+    ChartDataGeohashDecodeOptionsSchema,
+    ChartDataGeohashEncodeOptionsSchema,
+    ChartDataGeodeticParseOptionsSchema,
 )

superset/utils/pandas_postprocessing.py

@@ -15,10 +15,12 @@
 # specific language governing permissions and limitations
 # under the License.
 from functools import partial
-from typing import Any, Callable, Dict, List, Optional, Union
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
 
+import geohash as geohash_lib
 import numpy as np
 from flask_babel import gettext as _
+from geopy.point import Point
 from pandas import DataFrame, NamedAgg
 
 from superset.exceptions import QueryObjectValidationError
@@ -144,10 +146,7 @@ def _append_columns(
     :return: new DataFrame with combined data from `base_df` and `append_df`
     """
     return base_df.assign(
-        **{
-            target: append_df[append_df.columns[idx]]
-            for idx, target in enumerate(columns.values())
-        }
+        **{target: append_df[source] for source, target in columns.items()}
     )
 
 
@@ -325,23 +324,32 @@ def rolling(  # pylint: disable=too-many-arguments
 
 @validate_column_args("columns", "rename")
 def select(
-    df: DataFrame, columns: List[str], rename: Optional[Dict[str, str]] = None
+    df: DataFrame,
+    columns: Optional[List[str]] = None,
+    drop: Optional[List[str]] = None,
+    rename: Optional[Dict[str, str]] = None,
 ) -> DataFrame:
     """
     Only select a subset of columns in the original dataset. Can be useful for
     removing unnecessary intermediate results, renaming and reordering columns.
 
     :param df: DataFrame on which the rolling period will be based.
     :param columns: Columns which to select from the DataFrame, in the desired order.
-                    If columns are renamed, the old column name should be referenced
-                    here.
+                    If left undefined, all columns will be selected. If columns are
+                    renamed, the original column name should be referenced here.
+    :param drop: columns to drop from selection. If columns are renamed, the new
+                 column name should be referenced here.
     :param rename: columns which to rename, mapping source column to target column.
                    For instance, `{'y': 'y2'}` will rename the column `y` to
                    `y2`.
     :return: Subset of columns in original DataFrame
     :raises ChartDataValidationError: If the request in incorrect
     """
-    df_select = df[columns]
+    df_select = df.copy(deep=False)
+    if columns:
+        df_select = df_select[columns]
+    if drop:
+        df_select = df_select.drop(drop, axis=1)
     if rename is not None:
         df_select = df_select.rename(columns=rename)
     return df_select
@@ -350,6 +358,7 @@ def select(
 @validate_column_args("columns")
 def diff(df: DataFrame, columns: Dict[str, str], periods: int = 1,) -> DataFrame:
     """
+    Calculate row-by-row difference for select columns.
 
     :param df: DataFrame on which the diff will be based.
     :param columns: columns on which to perform diff, mapping source column to
@@ -369,6 +378,7 @@ def diff(df: DataFrame, columns: Dict[str, str], periods: int = 1,) -> DataFrame
 @validate_column_args("columns")
 def cum(df: DataFrame, columns: Dict[str, str], operator: str) -> DataFrame:
     """
+    Calculate cumulative sum/product/min/max for select columns.
 
     :param df: DataFrame on which the cumulative operation will be based.
     :param columns: columns on which to perform a cumulative operation, mapping source
@@ -377,7 +387,7 @@ def cum(df: DataFrame, columns: Dict[str, str], operator: str) -> DataFrame:
            `y2` based on cumulative values calculated from `y`, leaving the original
            column `y` unchanged.
     :param operator: cumulative operator, e.g. `sum`, `prod`, `min`, `max`
-    :return:
+    :return: DataFrame with cumulated columns
     """
     df_cum = df[columns.keys()]
     operation = "cum" + operator
@@ -388,3 +398,92 @@ def cum(df: DataFrame, columns: Dict[str, str], operator: str) -> DataFrame:
             _("Invalid cumulative operator: %(operator)s", operator=operator)
         )
     return _append_columns(df, getattr(df_cum, operation)(), columns)
+
+
+def geohash_decode(
+    df: DataFrame, geohash: str, longitude: str, latitude: str
+) -> DataFrame:
+    """
+    Decode a geohash column into longitude and latitude
+
+    :param df: DataFrame containing geohash data
+    :param geohash: Name of source column containing geohash location.
+    :param longitude: Name of new column to be created containing longitude.
+    :param latitude: Name of new column to be created containing latitude.
+    :return: DataFrame with decoded longitudes and latitudes
+    """
+    try:
+        lonlat_df = DataFrame()
+        lonlat_df["latitude"], lonlat_df["longitude"] = zip(
+            *df[geohash].apply(geohash_lib.decode)
+        )
+        return _append_columns(
+            df, lonlat_df, {"latitude": latitude, "longitude": longitude}
+        )
+    except ValueError:
+        raise QueryObjectValidationError(_("Invalid geohash string"))
+
+
+def geohash_encode(
+    df: DataFrame, geohash: str, longitude: str, latitude: str,
+) -> DataFrame:
+    """
+    Encode longitude and latitude into geohash
+
+    :param df: DataFrame containing longitude and latitude data
+    :param geohash: Name of new column to be created containing geohash location.
+    :param longitude: Name of source column containing longitude.
+    :param latitude: Name of source column containing latitude.
+    :return: DataFrame with decoded longitudes and latitudes
+    """
+    try:
+        encode_df = df[[latitude, longitude]]
+        encode_df.columns = ["latitude", "longitude"]
+        encode_df["geohash"] = encode_df.apply(
+            lambda row: geohash_lib.encode(row["latitude"], row["longitude"]), axis=1,
+        )
+        return _append_columns(df, encode_df, {"geohash": geohash})
+    except ValueError:
+        QueryObjectValidationError(_("Invalid longitude/latitude"))
+
+
+def geodetic_parse(
+    df: DataFrame,
+    geodetic: str,
+    longitude: str,
+    latitude: str,
+    altitude: Optional[str] = None,
+) -> DataFrame:
+    """
+    Parse a column containing a geodetic point string
+    [Geopy](https://geopy.readthedocs.io/en/stable/#geopy.point.Point).
+
+    :param df: DataFrame containing geodetic point data
+    :param geodetic: Name of source column containing geodetic point string.
+    :param longitude: Name of new column to be created containing longitude.
+    :param latitude: Name of new column to be created containing latitude.
+    :param altitude: Name of new column to be created containing altitude.
+    :return: DataFrame with decoded longitudes and latitudes
+    """
+
+    def _parse_location(location: str) -> Tuple[float, float, float]:
+        """
+        Parse a string containing a geodetic point and return latitude, longitude
+        and altitude
+        """
+        point = Point(location)  # type: ignore
+        return point[0], point[1], point[2]
+
+    try:
+        geodetic_df = DataFrame()
+        (
+            geodetic_df["latitude"],
+            geodetic_df["longitude"],
+            geodetic_df["altitude"],
+        ) = zip(*df[geodetic].apply(_parse_location))
+        columns = {"latitude": longitude, "longitude": latitude}
+        if altitude:
+            columns["altitude"] = altitude
+        return _append_columns(df, geodetic_df, columns)
+    except ValueError:
+        raise QueryObjectValidationError(_("Invalid geodetic string"))

tests/fixtures/dataframes.py

@@ -119,3 +119,17 @@
     index=to_datetime(["2019-01-01", "2019-01-02", "2019-01-05", "2019-01-07"]),
     data={"label": ["x", "y", "z", "q"], "y": [1.0, 2.0, 3.0, 4.0]},
 )
+
+lonlat_df = DataFrame(
+    {
+        "city": ["New York City", "Sydney"],
+        "geohash": ["dr5regw3pg6f", "r3gx2u9qdevk"],
+        "latitude": [40.71277496, -33.85598011],
+        "longitude": [-74.00597306, 151.20666526],
+        "altitude": [5.5, 0.012],
+        "geodetic": [
+            "40.71277496, -74.00597306, 5.5km",
+            "-33.85598011, 151.20666526, 12m",
+        ],
+    }
+)