--- a
+++ b/tests/anndata/test_feature_specifications.py
@@ -0,0 +1,174 @@
+import numpy as np
+import pandas as pd
+import pytest
+
+import ehrapy as ep
+from ehrapy.anndata import check_feature_types, df_to_anndata
+from ehrapy.anndata._constants import CATEGORICAL_TAG, DATE_TAG, FEATURE_TYPE_KEY, NUMERIC_TAG
+from tests.conftest import TEST_DATA_PATH
+
+IMPUTATION_DATA_PATH = TEST_DATA_PATH / "imputation"
+
+
+@pytest.fixture
+def adata():
+    df = pd.DataFrame(
+        {
+            "feature1": [1, 2, 2, 0],
+            "feature2": ["a", "b", "c", "d"],
+            "feature3": [1.0, 2.0, 3.0, 2.0],
+            "feature4": [0.0, 0.3, 0.5, 4.6],
+            "feature5": ["a", "b", np.nan, "d"],
+            "feature6": [1.4, 0.2, np.nan, np.nan],
+            "feature7": pd.to_datetime(["2021-01-01", "2024-04-16", "2021-01-03", "2067-07-02"]),
+        }
+    )
+    adata = df_to_anndata(df)
+
+    return adata
+
+
+def test_feature_type_inference(adata):
+    ep.ad.infer_feature_types(adata, output=None)
+    assert adata.var[FEATURE_TYPE_KEY]["feature1"] == CATEGORICAL_TAG
+    assert adata.var[FEATURE_TYPE_KEY]["feature2"] == CATEGORICAL_TAG
+    assert adata.var[FEATURE_TYPE_KEY]["feature3"] == CATEGORICAL_TAG
+    assert adata.var[FEATURE_TYPE_KEY]["feature4"] == NUMERIC_TAG
+    assert adata.var[FEATURE_TYPE_KEY]["feature5"] == CATEGORICAL_TAG
+    assert adata.var[FEATURE_TYPE_KEY]["feature6"] == NUMERIC_TAG
+    assert adata.var[FEATURE_TYPE_KEY]["feature7"] == DATE_TAG
+
+
+def test_check_feature_types(adata):
+    @check_feature_types
+    def test_func(adata):
+        pass
+
+    assert FEATURE_TYPE_KEY not in adata.var.keys()
+    test_func(adata)
+    assert FEATURE_TYPE_KEY in adata.var.keys()
+
+    ep.ad.infer_feature_types(adata, output=None)
+    test_func(adata)
+    assert FEATURE_TYPE_KEY in adata.var.keys()
+
+    @check_feature_types
+    def test_func_with_return(adata):
+        return adata
+
+    adata = test_func_with_return(adata)
+    assert FEATURE_TYPE_KEY in adata.var.keys()
+
+
+def test_feature_types_impute_num_adata(impute_num_adata):
+    ep.ad.infer_feature_types(impute_num_adata, output=None)
+    assert np.all(impute_num_adata.var[FEATURE_TYPE_KEY] == [NUMERIC_TAG, NUMERIC_TAG, NUMERIC_TAG])
+
+
+def test_feature_types_impute_adata(impute_adata):
+    ep.ad.infer_feature_types(impute_adata, output=None)
+    assert np.all(impute_adata.var[FEATURE_TYPE_KEY] == [NUMERIC_TAG, NUMERIC_TAG, CATEGORICAL_TAG, CATEGORICAL_TAG])
+
+
+def test_feature_types_impute_iris(impute_iris_adata):
+    ep.ad.infer_feature_types(impute_iris_adata, output=None)
+    assert np.all(
+        impute_iris_adata.var[FEATURE_TYPE_KEY] == [NUMERIC_TAG, NUMERIC_TAG, NUMERIC_TAG, NUMERIC_TAG, CATEGORICAL_TAG]
+    )
+
+
+def test_feature_types_impute_feature_types_titanic(impute_titanic_adata):
+    ep.ad.infer_feature_types(impute_titanic_adata, output=None)
+    impute_titanic_adata.var[FEATURE_TYPE_KEY] = [
+        CATEGORICAL_TAG,
+        CATEGORICAL_TAG,
+        CATEGORICAL_TAG,
+        CATEGORICAL_TAG,
+        CATEGORICAL_TAG,
+        NUMERIC_TAG,
+        NUMERIC_TAG,
+        NUMERIC_TAG,
+        NUMERIC_TAG,
+        NUMERIC_TAG,
+        CATEGORICAL_TAG,
+        CATEGORICAL_TAG,
+    ]
+
+
+def test_date_detection():
+    df = pd.DataFrame(
+        {
+            "date1": pd.to_datetime(["2021-01-01", "2024-04-16", "2021-01-03"]),
+            "date2": ["2021-01-01", "2024-04-16", "2021-01-03"],
+            "date3": ["2024-04-16 07:45:13", "2024-04-16", "2024-04"],
+            "not_date": ["not_a_date", "2024-04-16", "2021-01-03"],
+        }
+    )
+    adata = df_to_anndata(df)
+    ep.ad.infer_feature_types(adata, output=None)
+    assert np.all(adata.var[FEATURE_TYPE_KEY] == [DATE_TAG, DATE_TAG, DATE_TAG, CATEGORICAL_TAG])
+
+
+def test_all_possible_types():
+    df = pd.DataFrame(
+        {
+            "f1": [42, 17, 93, 235],
+            "f2": ["apple", "banana", "cherry", "date"],
+            "f3": [1, 2, 3, 1],
+            "f4": [1.0, 2.0, 1.0, 2.0],
+            "f5": ["20200101", "20200102", "20200103", "20200104"],
+            "f6": [True, False, True, False],
+            "f7": [np.nan, 1, np.nan, 2],
+            "f8": ["apple", 1, "banana", 2],
+            "f9": ["001", "002", "003", "002"],
+            "f10": ["5", "5", "5", "5"],
+            "f11": ["A1", "A2", "B1", "B2"],
+            "f12": [90210, 10001, 60614, 80588],
+            "f13": [0.25, 0.5, 0.75, 1.0],
+            "f14": ["2125551234", "2125555678", "2125559012", "2125553456"],
+            "f15": ["$100", "€150", "£200", "¥250"],
+            "f16": [101, 102, 103, 104],
+            "f17": [1e3, 5e-2, 3.1e2, 2.7e-1],
+            "f18": ["23.5", "324", "4.5", "0.5"],
+            "f19": [1, 2, 3, 4],
+            "f20": ["001", "002", "003", "004"],
+        }
+    )
+
+    adata = df_to_anndata(df)
+    ep.ad.infer_feature_types(adata, output=None)
+
+    assert np.all(
+        adata.var[FEATURE_TYPE_KEY]
+        == [
+            NUMERIC_TAG,
+            CATEGORICAL_TAG,
+            CATEGORICAL_TAG,
+            CATEGORICAL_TAG,
+            DATE_TAG,
+            CATEGORICAL_TAG,
+            CATEGORICAL_TAG,
+            CATEGORICAL_TAG,
+            CATEGORICAL_TAG,
+            NUMERIC_TAG,
+            CATEGORICAL_TAG,
+            NUMERIC_TAG,
+            NUMERIC_TAG,
+            NUMERIC_TAG,
+            CATEGORICAL_TAG,
+            NUMERIC_TAG,
+            NUMERIC_TAG,
+            NUMERIC_TAG,
+            NUMERIC_TAG,
+            NUMERIC_TAG,
+        ]
+    )
+
+
+def test_partial_annotation(adata):
+    adata.var[FEATURE_TYPE_KEY] = ["dummy", np.nan, np.nan, NUMERIC_TAG, None, np.nan, None]
+    ep.ad.infer_feature_types(adata, output=None)
+    assert np.all(
+        adata.var[FEATURE_TYPE_KEY]
+        == ["dummy", CATEGORICAL_TAG, CATEGORICAL_TAG, NUMERIC_TAG, CATEGORICAL_TAG, NUMERIC_TAG, DATE_TAG]
+    )