diff --git a/hub/management/commands/import_2024_ppcs.py b/hub/management/commands/import_2024_ppcs.py
new file mode 100644
index 000000000..9bd69772c
--- /dev/null
+++ b/hub/management/commands/import_2024_ppcs.py
@@ -0,0 +1,108 @@
+from collections import Counter
+
+from django.core.management.base import BaseCommand
+
+import pandas as pd
+from tqdm import tqdm
+
+from hub.models import Area, DataSet, DataType, Person, PersonData
+
+party_shades = {
+    "Alba Party": "#005EB8",
+    "Alliance Party of Northern Ireland": "#F6CB2F",
+    "Conservative Party": "#0087DC",
+    "Democratic Unionist Party": "#D46A4C",
+    "Green Party of England and Wales": "#6AB023",
+    "Labour Co-operative": "#E4003B",
+    "Labour Party": "#E4003B",
+    "Liberal Democrats": "#FAA61A",
+    "Plaid Cymru": "#005B54",
+    "Scottish National Party": "#FDF38E",
+    "Sinn Féin": "#326760",
+    "Social Democratic and Labour Party": "#2AA82C",
+    "Speaker of the House of Commons": "#DCDCDC",
+    "independent politician": "#DCDCDC",
+}
+
+
+class Command(BaseCommand):
+    help = "Import Parliamentary Prospective Candidates for 2024 election"
+
+    area_type = "WMC23"
+
+    def add_arguments(self, parser):
+        parser.add_argument(
+            "-q", "--quiet", action="store_true", help="Silence progress bars."
+        )
+
+    def handle(self, quiet: bool = False, *args, **options):
+        self._quiet = quiet
+        self.import_ppcs()
+
+    def get_ppc_data(self):
+        csv = "https://docs.google.com/spreadsheets/d/e/2PACX-1vRhZbBrU2AdJDYyBZViMs6irvH7zVUiZm2rDoADw5B18drp6hILJBr-duSXCmHJ18SmYWm3iq0bbfoR/pub?gid=0&single=true&output=csv"
+        df = pd.read_csv(csv)
+
+        return df
+
+    def import_ppcs(self):
+        df = self.get_ppc_data()
+
+        party_dt = DataType.objects.get(name="party")
+
+        if not self._quiet:
+            print("Importing PPCs")
+
+        for _, ppc in tqdm(df.iterrows(), disable=self._quiet, total=df.shape[0]):
+
+            if pd.isna(ppc["Candidate Name"]) or ppc["Candidate Name"] == "":
+                continue
+
+            area = Area.get_by_name(ppc["Constituency"], area_type=self.area_type)
+            if area is None:  # pragma: no cover
+                print(
+                    f"Failed to add PPC {ppc['Candidate Name']} as area {ppc['Constituency']} does not exist"
+                )
+                continue
+
+            if area and "DC Candidate ID" in ppc:
+                person, created = Person.objects.update_or_create(
+                    person_type="PPC",
+                    external_id=ppc["DC Candidate ID"],
+                    id_type="dc_candidate_id",
+                    defaults={
+                        "name": ppc["Candidate Name"],
+                        "area": area,
+                    },
+                )
+
+            if person:
+                if not pd.isna(ppc["Party"]):
+                    try:
+                        PersonData.objects.get_or_create(
+                            person=person,
+                            data_type=party_dt,
+                            defaults={"data": ppc["Party"]},
+                        )
+                    except PersonData.MultipleObjectsReturned:  # pragma: no cover
+                        PersonData.objects.filter(
+                            person=person, person_type="PPC", data_type=party_dt
+                        ).delete()
+                        PersonData.objects.create(
+                            person=person, data_type=party_dt, data=ppc["Party"]
+                        )
+
+        dataset = DataSet.objects.filter(name="party", options=list())
+        if dataset:
+            parties = list()
+            all_parties = list(
+                PersonData.objects.filter(
+                    data_type__data_set__name="party"
+                ).values_list("data")
+            )
+
+            for party, count in Counter(all_parties).most_common():
+                shade = party_shades.get(party[0], "#DCDCDC")
+                parties.append(dict(title=party[0], shader=shade))
+
+            dataset.update(options=parties)