DATASET: Hope For The Future trained constituents

hub/management/commands/import_hftf_trained_constituents.py

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+from django.conf import settings
+
+import pandas as pd
+
+from hub.models import AreaData, DataSet
+
+from .base_importers import BaseImportFromDataFrameCommand
+
+
+class Command(BaseImportFromDataFrameCommand):
+    help = "Import data about number of active HFTF constituents per constituency"
+
+    data_file = (
+        settings.BASE_DIR
+        / "data"
+        / "HFTF_ Project Groundgame data 07_02_2023.xlsx - Main Sheet.csv"
+    )
+    cons_row = "Constituency"
+    message = "Importing HFTF trained constituents data"
+    uses_gss = False
+
+    defaults = {
+        "data_type": "integer",
+        "category": "movement",
+        "subcategory": "supporters_and_activists",
+        "description": "These constituents have been trained by Hope For The Future, to have more frequent and more powerful conversations about climate and nature, with their elected representatives.",
+        "release_date": "February 2023",
+        "source_label": "Data from Hope For The Future.",
+        "source": "https://www.hftf.org.uk/",
+        "source_type": "google sheet",
+        "table": "areadata",
+        "default_value": 1,
+        "data_url": "",
+        "unit_type": "raw",
+        "unit_distribution": "people_in_area",
+        "comparators": DataSet.numerical_comparators(),
+    }
+
+    data_sets = {
+        "hftf_constituents_count": {
+            "defaults": defaults,
+            "col": "constituents_count",
+        },
+    }
+
+    def get_dataframe(self):
+        df = pd.read_csv(
+            self.data_file,
+            usecols=["Constituency", "Group / Individual"],
+        )
+        df = (
+            df.dropna(subset="Constituency")
+            .groupby("Constituency")
+            .count()["Group / Individual"]
+            .reset_index()
+            .rename(columns={"Group / Individual": "constituents_count"})
+        )
+        df.constituents_count = df.constituents_count.astype(int)
+        return df
+
+    def get_label(self, defaults):
+        return "Number of Hope For The Future trained constituents"
+
+    def delete_data(self):
+        AreaData.objects.filter(data_type__in=self.data_types.values()).delete()