[GH-3475] Correct the GeoStats Python docstrings

python/sedona/spark/stats/autocorrelation/moran.py

@@ -22,12 +22,24 @@
 
 @dataclass
 class MoranResult:
+    """Result of the global Moran's I test.
+
+    Attributes:
+        i: Moran's I. Values near 1 mean that similar values are close to each other, values near -1
+            mean that neighbors tend to have dissimilar values, and values near 0 mean that the values
+            are randomly distributed.
+        p_norm: p-value of ``z_norm`` under the normality assumption. It is two-tailed unless
+            ``Moran.get_global`` was called with ``two_tailed=False``.
+        z_norm: z-score of ``i`` under the normality assumption.
+    """
+
     i: float
     p_norm: float
     z_norm: float
 
 
 class Moran:
+    """Global Moran's I spatial autocorrelation test."""
 
     @staticmethod
     def get_global(
@@ -36,6 +48,22 @@ def get_global(
         id_column: str = "id",
         value_column: str = "value",
     ) -> MoranResult:
+        """Computes the global Moran's I of a value column.
+
+        The dataframe needs an id column that uniquely identifies each row, a numeric value column, and a
+        column named ``weights`` that lists each row's neighbors and their weights, such as the one added by
+        ``sedona.spark.stats.weighting.add_distance_band_column``. Each neighbor must include the id and value
+        columns, so pass them to the weighting function in ``saved_attributes``.
+
+        Args:
+            df: dataframe with the id, value, and weights columns
+            two_tailed: whether to return a two-tailed p-value. Default is True
+            id_column: name of the column that identifies each row. Default is "id"
+            value_column: name of the numeric column to test. Default is "value"
+
+        Returns:
+            A MoranResult with Moran's I and its z-score and p-value under the normality assumption.
+        """
         sedona = SparkSession.getActiveSession()
 
         _jvm = sedona._jvm

python/sedona/spark/stats/clustering/dbscan.py

@@ -50,7 +50,7 @@ def dbscan(
         min_pts: minimum number of points parameter of DBSCAN algorithm
         geometry: name of the geometry column
         include_outliers: whether to return outlier points. If True, outliers are returned with a cluster value of -1.
-            Default is False
+            Default is True
         use_spheroid: whether to use a cartesian or spheroidal distance calculation. Default is false
         is_core_column_name: what the name of the column indicating if this is a core point should be. Default is "isCore"
         cluster_column_name: what the name of the column indicating the cluster id should be. Default is "cluster"

python/sedona/spark/stats/hotspot_detection/getis_ord.py

@@ -38,23 +38,25 @@ def g_local(
 
     Weights should be the neighbors of this row. The members of the weights should be comprised of structs containing a
     value column and a neighbor column. The neighbor column should be the contents of the neighbors with the same types
-    as the parent row (minus neighbors). You can use `wherobots.weighing.add_distance_band_column` to achieve this. To
-    calculate the Gi* statistic, ensure the focal observation is in the neighbors array (i.e. the row is in the weights
-    column) and `star=true`. Significance is calculated with a z score. Permutation tests are not yet implemented and
-    thus island weight does nothing. The following columns will be added: G, E[G], V[G], Z, P.
+    as the parent row (minus neighbors). You can use `sedona.spark.stats.weighting.add_distance_band_column` to
+    achieve this. To calculate the Gi* statistic, ensure the focal observation is in the neighbors array (pass
+    `include_self=True` to the weighting function) and set `star=True`. Significance is calculated with a z score.
+    Permutation tests are not yet implemented and thus island weight does nothing. The following columns will be
+    added: G, EG (the expected G), VG (the variance of G), Z, and P (1 minus the standard normal CDF of |Z|).
 
     Args:
         dataframe: the dataframe to perform the G statistic on
         x: The column name we want to perform hotspot analysis on
         weights: The column name containing the neighbors array. The neighbor column should be the contents of
             the neighbors with the same types as the parent row (minus neighbors). You can use
-            `wherobots.weighing.add_distance_band_column` to achieve this.
+            `sedona.spark.stats.weighting.add_distance_band_column` to achieve this.
         permutations: Not used. Permutation tests are not supported yet. The number of permutations to use for the
             significance test.
         star: Whether the focal observation is in the neighbors array. If true this calculates Gi*, otherwise Gi
         island_weight: Not used. The weight for the simulated neighbor used for records without a neighbor in perm tests
+
     Returns:
-        A dataframe with the original columns plus the columns G, E[G], V[G], Z, P.
+        A dataframe with the original columns plus the columns G, EG, VG, Z, P.
     """
     sedona = SparkSession.getActiveSession()
 

python/sedona/spark/stats/weighting.py

@@ -40,21 +40,22 @@ def add_distance_band_column(
     The dataframe should contain at least one GeometryType column. Rows must be unique. If one
     geometry column is present it will be used automatically. If two are present, the one named
     'geometry' will be used. If more than one are present and neither is named 'geometry', the
-    column name must be provided. The new column will be named 'cluster'.
+    column name must be provided. The new column is named by result_name, 'weights' by default.
 
     Args:
         dataframe: DataFrame with geometry column
         threshold: Distance threshold for considering neighbors
         binary: whether to use binary weights or inverse distance weights for neighbors (dist^alpha)
         alpha: alpha to use for inverse distance weights ignored when binary is true
         include_zero_distance_neighbors: whether to include neighbors that are 0 distance. If 0 distance neighbors are
-            included and binary is false, values are infinity as per the floating point spec (divide by 0)
+            included, binary is false, and alpha is negative, their weight is infinite (division by zero)
         include_self: whether to include self in the list of neighbors
         self_weight: the value to use for the self weight
         geometry: name of the geometry column
         use_spheroid: whether to use a cartesian or spheroidal distance calculation. Default is false
         saved_attributes: the attributes to save in the neighbor column. Default is all columns.
         result_name: the name of the resulting column. Default is 'weights'.
+
     Returns:
         The input DataFrame with a weight column added containing neighbors and their weights added to each row.
 
@@ -93,13 +94,12 @@ def add_binary_distance_band_column(
     Weights will always be 1.0. The dataframe should contain at least one GeometryType column. Rows must be unique. If
     one geometry column is present it will be used automatically. If two are present, the one named 'geometry' will be
     used. If more than one are present and neither is named 'geometry', the column name must be provided. The new column
-    will be named 'cluster'.
+    is named by result_name, 'weights' by default.
 
     Args:
         dataframe: DataFrame with geometry column
         threshold: Distance threshold for considering neighbors
-        include_zero_distance_neighbors: whether to include neighbors that are 0 distance. If 0 distance neighbors are
-            included and binary is false, values are infinity as per the floating point spec (divide by 0)
+        include_zero_distance_neighbors: whether to include neighbors that are 0 distance. Default is True
         include_self: whether to include self in the list of neighbors
         geometry: name of the geometry column
         use_spheroid: whether to use a cartesian or spheroidal distance calculation. Default is false
@@ -148,14 +148,15 @@ def add_weighted_distance_band_column(
     Weights will be distance^alpha. The dataframe should contain at least one GeometryType column. Rows must be unique. If
     one geometry column is present it will be used automatically. If two are present, the one named 'geometry' will be
     used. If more than one are present and neither is named 'geometry', the column name must be provided. The new column
-    will be named 'cluster'.
+    is named by result_name, 'weights' by default.
 
     Args:
         dataframe: DataFrame with geometry column
         threshold: Distance threshold for considering neighbors
-        alpha: alpha to use for inverse distance weights. Computation is dist^alpha. Default is -1.0
-        include_zero_distance_neighbors: whether to include neighbors that are 0 distance. If 0 distance neighbors are
-            included and binary is false, values are infinity as per the floating point spec (divide by 0)
+        alpha: exponent of the distance weights, computed as dist^alpha. Use a negative value, such as -1.0, for
+            inverse distance weights
+        include_zero_distance_neighbors: whether to include neighbors that are 0 distance. Default is True. With a
+            negative alpha, their weight is infinite (division by zero)
         include_self: whether to include self in the list of neighbors
         self_weight: the value to use for the self weight. Default is 1.0
         geometry: name of the geometry column
@@ -164,8 +165,8 @@ def add_weighted_distance_band_column(
         result_name: the name of the resulting column. Default is 'weights'.
 
     Returns:
-        The input DataFrame with a weight column added containing neighbors and their weights (always 1) added to each
-        row.
+        The input DataFrame with a weight column added containing neighbors and their weights (dist^alpha) added to
+        each row.
 
     """
     sedona = SparkSession.getActiveSession()