typing fixes

src/data_common/dataset/resource_management.py

@@ -1042,9 +1042,9 @@ def build_coversheet(
         desc = self.get_datapackage()
         settings = get_settings()
 
-        bold = writer.book.add_format({"bold": True})
+        bold = writer.book.add_format({"bold": True})  # type: ignore
 
-        ws = writer.book.add_worksheet("package_description")
+        ws = writer.book.add_worksheet("package_description")  # type: ignore
         ws.set_column(2, 2, 40)
         ws.set_column(3, 3, 30)
         ws.write(2, 2, "Dataset", bold)
@@ -1200,7 +1200,7 @@ def build_excel(self, is_geodata: bool = False):
 
         writer = pd.ExcelWriter(excel_path)
         writer = self.build_coversheet(writer, allowed_sheets=allowed_resource_slugs)
-        text_wrap = writer.book.add_format({"text_wrap": True})
+        text_wrap = writer.book.add_format({"text_wrap": True})  # type: ignore
 
         for sheet_name, df in sheets.items():
             short_sheet_name = sheet_name[-31:]  # only allow 31 characters
@@ -1210,7 +1210,7 @@ def build_excel(self, is_geodata: bool = False):
             df.to_excel(writer, sheet_name=short_sheet_name, index=False)
 
             for column in df:
-                column_length = max(df[column].astype(str).map(len).max(), len(column))
+                column_length = max(df[column].astype(str).map(len).max(), len(column))  # type: ignore
                 column_length += 4
 
                 col_idx = df.columns.get_loc(column)
@@ -1223,7 +1223,7 @@ def build_excel(self, is_geodata: bool = False):
                         col_idx, col_idx, 50, text_wrap
                     )
 
-        writer.save()
+        writer.save()  # type: ignore
 
     def build_sqlite(self, is_geodata: bool = False):
         """

src/data_common/dataset/table_management.py

@@ -111,7 +111,7 @@ def get_table_schema(
         Produce a table data schema for the dataframe
         https://specs.frictionlessdata.io/table-schema/
         """
-        data: SchemaValidator = build_table_schema(df, index=False, version=False)
+        data: SchemaValidator = build_table_schema(df, index=False, version=False)  # type: ignore
         data["fields"] = [
             cls.enhance_field(df, field, descriptions, enums)
             for field in data["fields"]

src/data_common/pandas/df_extensions/common.py

@@ -12,7 +12,7 @@ def __init__(self, pandas_obj: pd.DataFrame):
         self._obj = pandas_obj
 
     def update_from_map(self, map: dict) -> pd.Series:
-        return self._obj.apply(lambda x: map.get(x, x))
+        return self._obj.apply(lambda x: map.get(x, x))  # type:ignore
 
 
 @pd_api.extensions.register_dataframe_accessor("common")

src/data_common/pandas/df_extensions/la.py

@@ -180,7 +180,7 @@ def get_council_info(
         adf = get_la_df(include_historical=include_historical, as_of_date=as_of_date)
         if items:
             adf = adf[["local-authority-code"] + items]
-        return df.merge(adf, how=merge_type)
+        return df.merge(adf, how=merge_type)  # type: ignore
 
     def just_lower_tier(self) -> pd.DataFrame:
         """

src/data_common/pandas/df_extensions/space.py

@@ -14,7 +14,6 @@
 from IPython.display import display
 from ipywidgets import interactive
 from matplotlib.colors import Colormap
-from numpy.typing import ArrayLike
 from scipy.spatial.distance import pdist, squareform
 from sklearn.cluster import KMeans
 from sklearn.metrics import silhouette_score
@@ -198,7 +197,7 @@ def map_from_anchor(self, anchor: pd.DataFrame | Path) -> dict[int, int]:
 
         # create mapping of the new (and possibly random numbers) to the ones we've previously assigned labels.
 
-        return mapping.to_dict()
+        return mapping.to_dict()  # type: ignore
 
     def get_label_options(self) -> list:
         return [self.get_label_name(x) for x in range(1, self.k + 1)]
@@ -209,7 +208,7 @@ def get_cluster_label_ids(self) -> pd.Series:
             labels = labels.map(self.cluster_no_mapping)
         return labels
 
-    def get_cluster_labels(self, include_short=True) -> ArrayLike:
+    def get_cluster_labels(self, include_short=True) -> np.ndarray:
         labels = self.get_cluster_label_ids()
 
         def f(x):
@@ -220,7 +219,7 @@ def f(x):
 
     label_array = get_cluster_labels
 
-    def get_cluster_descs(self) -> ArrayLike:
+    def get_cluster_descs(self) -> np.ndarray:
         labels = self.get_cluster_label_ids()
         labels = labels.apply(lambda x: self.get_label_desc(n=x))
         return np.array(labels)
@@ -350,7 +349,7 @@ def s_score(kmeans):
         df = pd.DataFrame({"n": range(start, stop, step)})
         df["k_means"] = df["n"].apply(self.get_clusters)
         df["sum_squares"] = df["k_means"].apply(lambda x: x.inertia_)
-        df["silhouette"] = df["k_means"].apply(s_score)
+        df["silhouette"] = df["k_means"].apply(s_score)  # type: ignore
 
         plt.rcParams["figure.figsize"] = (10, 5)  # type: ignore
         plt.subplot(1, 2, 1)