local council action scorecards import

hub/management/commands/import_council_scorecards_score.py

@@ -0,0 +1,167 @@
+from django.conf import settings
+
+import pandas as pd
+from tqdm import tqdm
+
+from hub.import_utils import filter_authority_type
+from hub.models import DataSet, DataType
+
+from .base_importers import BaseImportFromDataFrameCommand, MultipleAreaTypesMixin
+
+declare_map = {
+    "Y": "Yes",
+    "N": "No",
+}
+
+
+class Command(MultipleAreaTypesMixin, BaseImportFromDataFrameCommand):
+    cons_row = "gss_code"
+    message = "Importing council scorecards data"
+    uses_gss = True
+    do_not_convert = True
+
+    data_file = settings.BASE_DIR / "data" / "2023_scorecards_data.csv"
+
+    area_types = ["STC", "DIS"]
+
+    defaults = {
+        "label": "Council Climate Action Scorecard",
+        "description": "",
+        "data_type": "percent",
+        "category": "place",
+        "release_date": "2023",
+        "source_label": "Data from Climate Emergency UK.",
+        "source": "https://councilclimatescorecards.uk/",
+        "source_type": "csv",
+        "table": "areadata",
+        "data_url": "",
+        "comparators": DataSet.numerical_comparators(),
+        "is_filterable": True,
+        "is_shadable": True,
+        "is_public": True,
+        "unit_type": "raw",
+        "unit_distribution": "physical_area",
+    }
+
+    data_sets = {
+        "council_action_scorecard_total": {
+            "defaults": defaults,
+            "col": "weighted_total",
+        },
+        "council_action_scorecard_bh": {
+            "defaults": {
+                **defaults,
+                "label": "Buildings & Heating",
+            },
+            "col": "Buildings & Heating",
+        },
+        "council_action_scorecard_transport": {
+            "defaults": {
+                **defaults,
+                "label": "Transport",
+            },
+            "col": "Transport",
+        },
+        "council_action_scorecard_planning": {
+            "defaults": {
+                **defaults,
+                "label": "Planning & Land Use",
+            },
+            "col": "Planning & Land Use",
+        },
+        "council_action_scorecard_governance": {
+            "defaults": {
+                **defaults,
+                "label": "Goverance & Finance",
+            },
+            "col": "Governance & Finance",
+        },
+        "council_action_scorecard_biodiversity": {
+            "defaults": {
+                **defaults,
+                "label": "Biodiversity",
+            },
+            "col": "Biodiversity",
+        },
+        "council_action_scorecard_collaboration": {
+            "defaults": {
+                **defaults,
+                "label": "Collaboration & Engagement",
+            },
+            "col": "Collaboration & Engagement",
+        },
+        "council_action_scorecard_waste": {
+            "defaults": {
+                **defaults,
+                "label": "Waste Reduction & Food",
+            },
+            "col": "Waste Reduction & Food",
+        },
+    }
+
+    # do not want to calculate averages as the comparisons are only relevant
+    # to councils of the same type
+    def update_averages(self):
+        pass
+
+    def add_data_sets(self, df):
+        if not self._quiet:
+            self.stdout.write("Creating dataset + types")
+
+        total_data_set, created = DataSet.objects.update_or_create(
+            name="council_action_scorecard_total", defaults=self.defaults
+        )
+
+        section_data_set, created = DataSet.objects.update_or_create(
+            name="council_action_scorecard_sections",
+            defaults={
+                **self.defaults,
+                "is_range": True,
+                "label": "Action Scorecards section scores",
+            },
+        )
+
+        total_data_set.areas_available.add(self.get_area_type())
+        section_data_set.areas_available.add(self.get_area_type())
+
+        for name in tqdm(self.data_sets.keys()):
+            if name == "council_action_scorecard_total":
+                data_set = total_data_set
+            else:
+                data_set = section_data_set
+
+            data_type, created = DataType.objects.update_or_create(
+                data_set=data_set,
+                name=name,
+                area_type=self.get_area_type(),
+                defaults={
+                    "data_type": "percent",
+                    "label": self.data_sets[name]["defaults"]["label"],
+                },
+            )
+            self.data_types[name] = data_type
+
+    def get_dataframe(self):
+        df = pd.read_csv(self.data_file)
+        df = filter_authority_type(df, self.area_type, "gss")
+
+        councils = []
+        for index, row in df.iterrows():
+            councils.append(
+                {
+                    "gss_code": row["gss"],
+                    "weighted_total": row["weighted_total"] * 100,
+                    "Buildings & Heating": row["Buildings & Heating"] * 100,
+                    "Transport": row["Transport"] * 100,
+                    "Governance & Finance": row["Governance & Finance"] * 100,
+                    "Biodiversity": row["Biodiversity"] * 100,
+                    "Planning & Land Use": row["Planning & Land Use"] * 100,
+                    "Waste Reduction & Food": row["Waste Reduction & Food"] * 100,
+                    "Collaboration & Engagement": row["Collaboration & Engagement"]
+                    * 100,
+                }
+            )
+
+        df = pd.DataFrame(councils)
+
+        return df