MNT _convert_container "dataframe" -> "pandas" (scikit-learn#33956)

Author: lorentzenchr

sklearn/datasets/tests/test_openml.py

@@ -1042,7 +1042,7 @@ def test_fetch_openml_sparse_arff_error(monkeypatch, params, err_msg):
 @pytest.mark.parametrize(
     "data_id, data_type",
     [
-        (61, "dataframe"),  # iris dataset version 1
+        (61, "pandas"),  # iris dataset version 1
         (292, "sparse"),  # Australian dataset version 1
     ],
 )
@@ -1052,7 +1052,7 @@ def test_fetch_openml_auto_mode(monkeypatch, data_id, data_type):
 
     _monkey_patch_webbased_functions(monkeypatch, data_id, True)
     data = fetch_openml(data_id=data_id, as_frame="auto", cache=False)
-    klass = pd.DataFrame if data_type == "dataframe" else scipy.sparse.csr_matrix
+    klass = pd.DataFrame if data_type == "pandas" else scipy.sparse.csr_matrix
     assert isinstance(data.data, klass)
 
 

sklearn/ensemble/_hist_gradient_boosting/tests/test_monotonic_constraints.py

@@ -226,7 +226,7 @@ def test_predictions(global_random_seed, use_feature_names):
 
     X = np.c_[f_a, f_0, f_b, f_1, f_c]
     columns_name = ["f_a", "f_0", "f_b", "f_1", "f_c"]
-    constructor_name = "dataframe" if use_feature_names else "array"
+    constructor_name = "pandas" if use_feature_names else "array"
     X = _convert_container(X, constructor_name, columns_name=columns_name)
 
     noise = rng.normal(loc=0.0, scale=0.01, size=n_samples)

sklearn/impute/tests/test_base.py

@@ -90,7 +90,7 @@ def test_base_no_precomputed_mask_transform(data):
         imputer.fit_transform(data)
 
 
-@pytest.mark.parametrize("X1_type", ["array", "dataframe"])
+@pytest.mark.parametrize("X1_type", ["array", "pandas"])
 def test_assign_where(X1_type):
     """Check the behaviour of the private helpers `_assign_where`."""
     rng = np.random.RandomState(0)
@@ -102,6 +102,6 @@ def test_assign_where(X1_type):
 
     _assign_where(X1, X2, mask)
 
-    if X1_type == "dataframe":
+    if X1_type == "pandas":
         X1 = X1.to_numpy()
     assert_allclose(X1[mask], X2[mask])

sklearn/impute/tests/test_impute.py

@@ -286,12 +286,12 @@ def test_imputation_mean_median_error_invalid_type(strategy, dtype):
 
 
 @pytest.mark.parametrize("strategy", ["mean", "median"])
-@pytest.mark.parametrize("type", ["list", "dataframe"])
-def test_imputation_mean_median_error_invalid_type_list_pandas(strategy, type):
+@pytest.mark.parametrize("constructor_name", ["list", "pandas"])
+def test_imputation_mean_median_error_invalid_type_list_pandas(
+    strategy, constructor_name
+):
     X = [["a", "b", 3], [4, "e", 6], ["g", "h", 9]]
-    if type == "dataframe":
-        pd = pytest.importorskip("pandas")
-        X = pd.DataFrame(X)
+    X = _convert_container(X, constructor_name)
     msg = "non-numeric data:\ncould not convert string to float:"
     with pytest.raises(ValueError, match=msg):
         imputer = SimpleImputer(strategy=strategy)

sklearn/inspection/_plot/tests/test_plot_partial_dependence.py

@@ -178,17 +178,17 @@ def test_plot_partial_dependence_kind(
 @pytest.mark.parametrize(
     "input_type, feature_names_type",
     [
-        ("dataframe", None),
-        ("dataframe", "list"),
+        ("pandas", None),
+        ("pandas", "list"),
         ("list", "list"),
         ("array", "list"),
-        ("dataframe", "array"),
+        ("pandas", "array"),
         ("list", "array"),
         ("array", "array"),
-        ("dataframe", "series"),
+        ("pandas", "series"),
         ("list", "series"),
         ("array", "series"),
-        ("dataframe", "index"),
+        ("pandas", "index"),
         ("list", "index"),
         ("array", "index"),
     ],
@@ -205,13 +205,9 @@ def test_plot_partial_dependence_str_features(
     age = diabetes.data[:, diabetes.feature_names.index("age")]
     bmi = diabetes.data[:, diabetes.feature_names.index("bmi")]
 
-    if input_type == "dataframe":
-        pd = pytest.importorskip("pandas")
-        X = pd.DataFrame(diabetes.data, columns=diabetes.feature_names)
-    elif input_type == "list":
-        X = diabetes.data.tolist()
-    else:
-        X = diabetes.data
+    X = _convert_container(
+        diabetes.data, input_type, columns_name=diabetes.feature_names
+    )
 
     if feature_names_type is None:
         feature_names = None
@@ -803,7 +799,7 @@ def test_plot_partial_dependence_does_not_override_ylabel(
 @pytest.mark.parametrize(
     "categorical_features, array_type",
     [
-        (["col_A", "col_C"], "dataframe"),
+        (["col_A", "col_C"], "pandas"),
         ([0, 2], "array"),
         ([True, False, True], "array"),
     ],
@@ -985,7 +981,7 @@ def test_partial_dependence_overwrite_labels(
 @pytest.mark.parametrize(
     "categorical_features, array_type",
     [
-        (["col_A", "col_C"], "dataframe"),
+        (["col_A", "col_C"], "pandas"),
         ([0, 2], "array"),
         ([True, False, True], "array"),
     ],

sklearn/inspection/tests/test_pd_utils.py

@@ -9,7 +9,7 @@
     "feature_names, array_type, expected_feature_names",
     [
         (None, "array", ["x0", "x1", "x2"]),
-        (None, "dataframe", ["a", "b", "c"]),
+        (None, "pandas", ["a", "b", "c"]),
         (np.array(["a", "b", "c"]), "array", ["a", "b", "c"]),
     ],
 )

sklearn/inspection/tests/test_permutation_importance.py

@@ -369,7 +369,7 @@ def test_permutation_importance_equivalence_array_dataframe(n_jobs, max_samples)
     )
 
 
-@pytest.mark.parametrize("input_type", ["array", "dataframe"])
+@pytest.mark.parametrize("input_type", ["array", "pandas"])
 def test_permutation_importance_large_memmaped_data(input_type):
     # Smoke, non-regression test for:
     # https://github.com/scikit-learn/scikit-learn/issues/15810

sklearn/linear_model/tests/test_bayes.py

@@ -240,7 +240,7 @@ def test_ard_accuracy_on_easy_problem(global_random_seed, n_samples, n_features)
     assert abs_coef_error < 1e-10
 
 
-@pytest.mark.parametrize("constructor_name", ["array", "dataframe"])
+@pytest.mark.parametrize("constructor_name", ["array", "pandas"])
 def test_return_std(constructor_name, global_random_seed):
     # Test return_std option for both Bayesian regressors
     rng = np.random.RandomState(global_random_seed)

sklearn/preprocessing/tests/test_encoders.py

@@ -1394,7 +1394,7 @@ def test_ohe_infrequent_user_cats_unknown_training_errors(kwargs):
 @pytest.mark.parametrize(
     "input_dtype, category_dtype", ["OO", "OU", "UO", "UU", "SO", "SU", "SS"]
 )
-@pytest.mark.parametrize("array_type", ["list", "array", "dataframe"])
+@pytest.mark.parametrize("array_type", ["list", "array", "pandas"])
 def test_encoders_string_categories(input_dtype, category_dtype, array_type):
     """Check that encoding work with object, unicode, and byte string dtypes.
     Non-regression test for:

sklearn/tests/test_base.py

@@ -914,7 +914,7 @@ class Estimator(BaseEstimator, WithSlots):
 @pytest.mark.parametrize(
     "constructor_name, minversion",
     [
-        ("dataframe", "1.5.0"),
+        ("pandas", "1.5.0"),
         ("pyarrow", "13.0.0"),
         ("polars", "0.20.23"),
     ],