Merge branch 'feature/fix_enrichment_new_catalogue' of github.com:Car…

…toDB/cartoframes into feature/fix_enrichment_new_catalogue

cartoframes/data/enrichment/points_enrichment.py

@@ -12,6 +12,28 @@ def enrich_points(data, variables, data_geom_column='geometry', filters=dict(),
     your points with our geographies. Extra columns as area and population will be provided
     with the aims of normalize these columns.
 
+    Args:
+        data (:py:class:`Dataset <cartoframes.data.Dataset>`, DataFrame, GeoDataFrame):
+            a Dataset, DataFrame or GeoDataFrame object to be enriched.
+        variables (:py:class:`Variable <cartoframes.data.observatory.Catalog>`, CatalogList, list, str):
+            variable(s), discovered through Catalog, for enriching the `data` argument.
+        data_geom_column (str): string indicating the 4326 geometry column in `data`.
+        filters (dict, optional): dictionary with either a `column` key
+            with the name of the column to filter or a `value` value with the value to filter by.
+            Filters will be used using the `AND` operator
+        credentials (:py:class:`Credentials <cartoframes.auth.Credentials>`, optional):
+            credentials of user account. If not provided,
+            a default credentials (if set with :py:meth:`set_default_credentials
+            <cartoframes.auth.set_default_credentials>`) will attempted to be
+            used.
+
+    Returns:
+        A dataframe as the provided one but with the variables to enrich appended to it
+
+        Note that if the geometry of the `data` you provide intersects with more than one geometry
+        in the enrichment dataset, the number of rows of the returned dataframe could be different
+        than the `data` argument number of rows.
+
     Examples:
 
         Enrich a points dataset with Catalog classes:
@@ -55,28 +77,6 @@ def enrich_points(data, variables, data_geom_column='geometry', filters=dict(),
             variables = Catalog().country('usa').category('demographics').datasets[0].variables
             filters = {'do_date': '2019-09-01'}
             dataset_enrich = enrichment.enrich_points(dataset, variables, filters)
-
-    Args:
-        data (:py:class:`Dataset <cartoframes.data.Dataset>`, DataFrame, GeoDataFrame):
-            a Dataset, DataFrame or GeoDataFrame object to be enriched.
-        variables (:py:class:`Variable <cartoframes.data.observatory.Catalog>`, CatalogList, list, str):
-            variable(s), discovered through Catalog, for enriching the `data` argument.
-        data_geom_column (str): string indicating the 4326 geometry column in `data`.
-        filters (dict, optional): dictionary with either a `column` key
-            with the name of the column to filter or a `value` value with the value to filter by.
-            Filters will be used using the `AND` operator
-        credentials (:py:class:`Credentials <cartoframes.auth.Credentials>`, optional):
-            credentials of user account. If not provided,
-            a default credentials (if set with :py:meth:`set_default_credentials
-            <cartoframes.auth.set_default_credentials>`) will attempted to be
-            used.
-
-    Returns:
-        A dataframe as the provided one but with the variables to enrich appended to it
-
-        Note that if the geometry of the `data` you provide intersects with more than one geometry
-        in the enrichment dataset, the number of rows of the returned dataframe could be different
-        than the `data` argument number of rows.
     """
 
     data_enriched = enrich(_prepare_sql, data=data, variables=variables, data_geom_column=data_geom_column,

cartoframes/data/enrichment/polygons_enrichment.py

@@ -14,6 +14,32 @@ def enrich_polygons(data, variables, agg_operators=dict(), data_geom_column='geo
     dataset, the proportional part of the intersection will be used to interpolate the quantity of the
     polygon value intersected, aggregating them with the operator provided by `agg_operators` argument.
 
+    Args:
+        data (Dataset, DataFrame, GeoDataFrame): a Dataset, DataFrame or GeoDataFrame object to be enriched.
+        variables (Variable, CatalogList, list, str): variable(s), discovered through Catalog,
+            for enriching the `data` argument.
+        agg_operators (dict, str, None, optional): dictionary with either a `column` key
+            with the name of the column to aggregate or a `operator` value with the operator to group by.
+            If `agg_operators`' dictionary is empty (default argument value) then aggregation operators
+            will be retrieved from metadata column.
+            If `agg_operators` is a string then all columns will be aggregated by this operator.
+            If `agg_operators` is `None` then no aggregations will be computed. All the values which
+            data geometry intersects with will be returned.
+        data_geom_column (str): string indicating the 4326 geometry column in `data`.
+        filters (dict, optional): dictionary with either a `column` key
+            with the name of the column to filter or a `value` value with the value to filter by.
+        credentials (:py:class:`Credentials <cartoframes.auth.Credentials>`, optional):
+            credentials of user account. If not provided,
+            a default credentials (if set with :py:meth:`set_default_credentials
+            <cartoframes.auth.set_default_credentials>`) will attempted to be
+            used.
+
+    Returns:
+        A dataframe as the provided one but with the variables to enrich appended to it
+
+        Note that if the geometry of the `data` you provide intersects with more than one geometry
+        in the enrichment dataset, the number of rows of the returned dataframe could be different
+        than the `data` argument number of rows.
 
     Examples:
 
@@ -96,34 +122,6 @@ def enrich_polygons(data, variables, agg_operators=dict(), data_geom_column='geo
 
             agg_operators = None
             dataset_enrich = enrichment.enrich_polygons(dataset, variables, agg_operators=agg_operators)
-
-
-    Args:
-        data (Dataset, DataFrame, GeoDataFrame): a Dataset, DataFrame or GeoDataFrame object to be enriched.
-        variables (Variable, CatalogList, list, str): variable(s), discovered through Catalog,
-            for enriching the `data` argument.
-        agg_operators (dict, str, None, optional): dictionary with either a `column` key
-            with the name of the column to aggregate or a `operator` value with the operator to group by.
-            If `agg_operators`' dictionary is empty (default argument value) then aggregation operators
-            will be retrieved from metadata column.
-            If `agg_operators` is a string then all columns will be aggregated by this operator.
-            If `agg_operators` is `None` then no aggregations will be computed. All the values which
-            data geometry intersects with will be returned.
-        data_geom_column (str): string indicating the 4326 geometry column in `data`.
-        filters (dict, optional): dictionary with either a `column` key
-            with the name of the column to filter or a `value` value with the value to filter by.
-        credentials (:py:class:`Credentials <cartoframes.auth.Credentials>`, optional):
-            credentials of user account. If not provided,
-            a default credentials (if set with :py:meth:`set_default_credentials
-            <cartoframes.auth.set_default_credentials>`) will attempted to be
-            used.
-
-    Returns:
-        A dataframe as the provided one but with the variables to enrich appended to it
-
-        Note that if the geometry of the `data` you provide intersects with more than one geometry
-        in the enrichment dataset, the number of rows of the returned dataframe could be different
-        than the `data` argument number of rows.
     """
 
     data_enriched = enrich(_prepare_sql, data=data, variables=variables, agg_operators=agg_operators,

cartoframes/viz/legend.py

@@ -115,14 +115,14 @@ def _check_type(self, _type):
         if _type and _type not in constants.LEGEND_TYPES:
             raise ValueError(
                 'Legend type "{}" is not valid. Valid legend types are: {}.'.format(
-                    ', '.join(_type, constants.LEGEND_TYPES)
+                    _type, ', '.join(constants.LEGEND_TYPES)
                 ))
 
     def _check_prop(self, _prop):
         if _prop and _prop not in constants.LEGEND_PROPERTIES:
             raise ValueError(
                 'Legend property "{}" is not valid. Valid legend properties are: {}.'.format(
-                    ', '.join(_prop, constants.LEGEND_PROPERTIES)
+                    _prop, ', '.join(constants.LEGEND_PROPERTIES)
                 ))
 
     def _infer_prop(self, _type):

docs/includes/data_enrichment.rst

@@ -2,7 +2,12 @@ Data Enrichment
 ---------------
 
 With CARTOframes it is possible to enrich your data by using our `Data Observatory <#data-observatory>`__
-Catalog through the enrichment methods.
+Catalogue through the enrichment methods.
+
+**Important:** Since the data enrichment depends on the Data Observatory 2.0,
+is only accessible to selected CARTO Enterprise users in a private beta.
+We're still open to more beta testers, so if you're interested,
+`please get in touch. <#https://carto.com/contact/?campaign_details=data-observatory-betatester>`__
 
 .. automodule:: cartoframes.data.enrichment
     :members:

docs/includes/data_observatory.rst

@@ -1,6 +1,10 @@
 Data Observatory
 ----------------
 
+**Important:** The new Data Observatory 2.0 is accessible to selected CARTO Enterprise
+users in a private beta. We're still open to more beta testers, so if you're interested,
+`please get in touch. <#https://carto.com/contact/?campaign_details=data-observatory-betatester>`__
+
 .. automodule:: cartoframes.data.observatory
     :members:
     :member-order: bysource