Handle int when unexpectedly included in the lables e.g. CD07 has 0 i…

pipelines/utils.py

@@ -88,18 +88,21 @@ def prepare_lookup(data: str | list[str] | pd.Series | pd.DataFrame) -> pd.Serie
     """
     Prepare a Series or DataFrame for lookup operations by converting to lowercase strings and stripping whitespace.
     """
-    if isinstance(data, str):
-        result = pd.DataFrame([data])
+    if isinstance(data, pd.DataFrame):
+        result = data
     elif isinstance(data, (list, pd.Series)):
         result = pd.DataFrame(data)
     else:
-        result = data
+        # Handle other types (like str, int, float)
+        result = pd.DataFrame([data])
+
     result = result.map(str).map(str.strip).map(str.lower).replace(r"\s+", " ", regex=True)
-    if isinstance(data, str):
-        result = result.iloc[0, 0]
+
+    if isinstance(data, pd.DataFrame):
+        return result
     elif isinstance(data, (list, pd.Series)):
-        result = result.iloc[:, 0]
-    return result
+        return result.iloc[:, 0]
+    return result.iloc[0, 0]
 
 
 def verbose_pivot(df: pd.DataFrame, values: str | list[str], index: str | list[str], columns: str | list[str]):

pipelines_tests/test_utils/test_utils.py

@@ -0,0 +1,107 @@
+import pandas as pd
+from django.test import TestCase
+from pipelines.utils import prepare_lookup
+
+
+class PrepareLookupTestCase(TestCase):
+
+    def test_prepare_lookup_with_premitive_type_input(self):
+        # empty string
+        result = prepare_lookup("")
+        self.assertEqual(result, "")
+
+        # a simple string
+        result = prepare_lookup("nbr Mois")
+        self.assertEqual(result, "nbr mois")
+
+        # an int
+        result = prepare_lookup(0)
+        self.assertEqual(result, "0")
+
+        # a float
+        result = prepare_lookup(7.55)
+        self.assertEqual(result, "7.55")
+
+        # a simple string with spaces
+        result = prepare_lookup(" nbr Mois ")
+        self.assertEqual(result, "nbr mois")
+
+        # string with multiple internal spaces
+        result = prepare_lookup("Autre    revenu  (ex. crédit)")
+        self.assertEqual(result, "autre revenu (ex. crédit)")
+
+    def test_prepare_lookup_with_list_input(self):
+        # list with single element
+        result = prepare_lookup(["water"])
+        self.assertIsInstance(result, pd.Series)
+        self.assertEqual(result[0], "water")
+
+        # list with multiple elements
+        result = prepare_lookup(["Water", "inputs", "Social serv."])
+        self.assertIsInstance(result, pd.Series)
+        pd.testing.assert_series_equal(result, pd.Series(["water", "inputs", "social serv."], name=0))
+
+    def test_prepare_lookup_with_series_input(self):
+        # simple series
+        data = pd.Series(["Camel number owned", "Cattle number owned"])
+        result = prepare_lookup(data)
+        self.assertIsInstance(result, pd.Series)
+        pd.testing.assert_series_equal(result, pd.Series(["camel number owned", "cattle number owned"], name=0))
+
+        # test with irrigular spaces in elements
+        data = pd.Series(["Camel number   owned ", "  cattle number  Owned"])
+        result = prepare_lookup(data)
+        pd.testing.assert_series_equal(result, pd.Series(["camel number owned", "cattle number owned"], name=0))
+
+        # test with numeric elemnts
+        data = pd.Series([123, 456])
+        result = prepare_lookup(data)
+        pd.testing.assert_series_equal(result, pd.Series(["123", "456"], name=0))
+
+    def test_prepare_lookup_with_dataframe_input(self):
+        # single column dataframe
+        data = pd.DataFrame({"lables": ["Livestock products"]})
+        result = prepare_lookup(data)
+        self.assertIsInstance(result, pd.DataFrame)
+        pd.testing.assert_frame_equal(result, pd.DataFrame({"lables": ["livestock products"]}))
+
+        # multiple columns dataframe
+        data = pd.DataFrame({"lables": ["Livestock products"], "another": ["Payment  in kind  "]})
+        result = prepare_lookup(data)
+        assert isinstance(result, pd.DataFrame)
+        expected = pd.DataFrame({"lables": ["livestock products"], "another": ["payment in kind"]})
+        pd.testing.assert_frame_equal(result, expected)
+
+        # numeric values
+        data = pd.DataFrame({"column1": [123, 456], "column2": [78.9, 1011.12]})
+        result = prepare_lookup(data)
+        expected = pd.DataFrame({"column1": ["123", "456"], "column2": ["78.9", "1011.12"]})
+        pd.testing.assert_frame_equal(result, expected)
+
+        # empty df
+        data = pd.DataFrame()
+        result = prepare_lookup(data)
+        self.assertIsInstance(result, pd.DataFrame)
+        self.assertTrue(result.empty)
+
+        # test that datafarme preserves structure
+        data = pd.DataFrame(
+            {
+                "label": ["Cowpeas: kg produced", "Sorghum: kg produced"],
+                "product": ["Cowpeas", "Sorghum"],
+                "unit": ["kg", "kg"],
+            }
+        )
+        result = prepare_lookup(data)
+        self.assertEqual(result.shape, data.shape)
+        self.assertEqual(list(result.columns), list(data.columns))
+
+    def test_prepare_lookup_with_special_characters(self):
+        result = prepare_lookup("Autre nourriture: Poisson 2(sec)!@#$%")
+        self.assertEqual(result, "autre nourriture: poisson 2(sec)!@#$%")
+        # with tabs
+        result = prepare_lookup("Autre nourriture: \tPoisson")
+        self.assertEqual(result, "autre nourriture: poisson")
+        # some unicode characters
+        result = prepare_lookup("Revenu (Espèces)")
+        self.assertEqual(result, "revenu (espèces)")