From 47cdaf22e26745c9fd3bc6fbc312dcf41654f92a Mon Sep 17 00:00:00 2001
From: Alexander Griffen <alexander@mysociety.org>
Date: Tue, 27 Feb 2024 15:06:38 +0000
Subject: [PATCH] DATASET: Wildlife trusts regions

---
 .../import_wildlife_trusts_regions.py         | 52 +++++++++++++++++++
 1 file changed, 52 insertions(+)
 create mode 100644 hub/management/commands/import_wildlife_trusts_regions.py

diff --git a/hub/management/commands/import_wildlife_trusts_regions.py b/hub/management/commands/import_wildlife_trusts_regions.py
new file mode 100644
index 000000000..762086f97
--- /dev/null
+++ b/hub/management/commands/import_wildlife_trusts_regions.py
@@ -0,0 +1,52 @@
+from django.conf import settings
+
+import pandas as pd
+
+from hub.models import DataSet
+
+from .base_importers import BaseConstituencyGroupListImportCommand
+
+
+class Command(BaseConstituencyGroupListImportCommand):
+    help = "Import Wildlife trust reserves"
+
+    data_file = (
+        settings.BASE_DIR / "data" / "wildlife_trust_regions_in_constituencies.csv"
+    )
+    cons_row = "PCON19NM"
+    uses_gss = False
+    message = "Importing constituency Wildlife Trust regions data"
+
+    data_sets = {
+        "wildlife_trusts_regions": {
+            "defaults": {
+                "label": "Wildlife Trust Regions",
+                "description": "The Wildlife Trust Region(s) that a constituency falls under.",
+                "data_type": "json",
+                "category": "place",
+                "subcategory": "groups",
+                "source_label": "Data from The Widlife Trusts",
+                "source": "https://www.wildlifetrusts.org/",
+                "source_type": "csv",
+                "table": "areadata",
+                "is_filterable": True,
+                "comparators": DataSet.in_comparators(),
+            },
+            "col": "Trust",
+        }
+    }
+    group_data_type = "wildlife_trusts_regions"
+
+    def get_df(self):
+        return pd.read_csv(self.data_file, usecols=["Trust", "PCON19NM"]).rename(
+            columns={"PCON19NM": "constituency"}
+        )
+
+    def get_group_json(self, row):
+        return row[["Trust"]].dropna().rename({"Trust": "group_name"}).to_dict()
+
+    def update_averages(self):
+        pass
+
+    def update_max_min(self):
+        pass