Updated script that can be controled by Nodejs web app

lib/python3.13/site-packages/pandas/tests/io/test_spss.py

@@ -0,0 +1,164 @@
+import datetime
+from pathlib import Path
+
+import numpy as np
+import pytest
+
+import pandas as pd
+import pandas._testing as tm
+from pandas.util.version import Version
+
+pyreadstat = pytest.importorskip("pyreadstat")
+
+
+# TODO(CoW) - detection of chained assignment in cython
+# https://github.com/pandas-dev/pandas/issues/51315
+@pytest.mark.filterwarnings("ignore::pandas.errors.ChainedAssignmentError")
+@pytest.mark.filterwarnings("ignore:ChainedAssignmentError:FutureWarning")
+@pytest.mark.parametrize("path_klass", [lambda p: p, Path])
+def test_spss_labelled_num(path_klass, datapath):
+    # test file from the Haven project (https://haven.tidyverse.org/)
+    # Licence at LICENSES/HAVEN_LICENSE, LICENSES/HAVEN_MIT
+    fname = path_klass(datapath("io", "data", "spss", "labelled-num.sav"))
+
+    df = pd.read_spss(fname, convert_categoricals=True)
+    expected = pd.DataFrame({"VAR00002": "This is one"}, index=[0])
+    expected["VAR00002"] = pd.Categorical(expected["VAR00002"])
+    tm.assert_frame_equal(df, expected)
+
+    df = pd.read_spss(fname, convert_categoricals=False)
+    expected = pd.DataFrame({"VAR00002": 1.0}, index=[0])
+    tm.assert_frame_equal(df, expected)
+
+
+@pytest.mark.filterwarnings("ignore::pandas.errors.ChainedAssignmentError")
+@pytest.mark.filterwarnings("ignore:ChainedAssignmentError:FutureWarning")
+def test_spss_labelled_num_na(datapath):
+    # test file from the Haven project (https://haven.tidyverse.org/)
+    # Licence at LICENSES/HAVEN_LICENSE, LICENSES/HAVEN_MIT
+    fname = datapath("io", "data", "spss", "labelled-num-na.sav")
+
+    df = pd.read_spss(fname, convert_categoricals=True)
+    expected = pd.DataFrame({"VAR00002": ["This is one", None]})
+    expected["VAR00002"] = pd.Categorical(expected["VAR00002"])
+    tm.assert_frame_equal(df, expected)
+
+    df = pd.read_spss(fname, convert_categoricals=False)
+    expected = pd.DataFrame({"VAR00002": [1.0, np.nan]})
+    tm.assert_frame_equal(df, expected)
+
+
+@pytest.mark.filterwarnings("ignore::pandas.errors.ChainedAssignmentError")
+@pytest.mark.filterwarnings("ignore:ChainedAssignmentError:FutureWarning")
+def test_spss_labelled_str(datapath):
+    # test file from the Haven project (https://haven.tidyverse.org/)
+    # Licence at LICENSES/HAVEN_LICENSE, LICENSES/HAVEN_MIT
+    fname = datapath("io", "data", "spss", "labelled-str.sav")
+
+    df = pd.read_spss(fname, convert_categoricals=True)
+    expected = pd.DataFrame({"gender": ["Male", "Female"]})
+    expected["gender"] = pd.Categorical(expected["gender"])
+    tm.assert_frame_equal(df, expected)
+
+    df = pd.read_spss(fname, convert_categoricals=False)
+    expected = pd.DataFrame({"gender": ["M", "F"]})
+    tm.assert_frame_equal(df, expected)
+
+
+@pytest.mark.filterwarnings("ignore::pandas.errors.ChainedAssignmentError")
+@pytest.mark.filterwarnings("ignore:ChainedAssignmentError:FutureWarning")
+def test_spss_umlauts(datapath):
+    # test file from the Haven project (https://haven.tidyverse.org/)
+    # Licence at LICENSES/HAVEN_LICENSE, LICENSES/HAVEN_MIT
+    fname = datapath("io", "data", "spss", "umlauts.sav")
+
+    df = pd.read_spss(fname, convert_categoricals=True)
+    expected = pd.DataFrame(
+        {"var1": ["the ä umlaut", "the ü umlaut", "the ä umlaut", "the ö umlaut"]}
+    )
+    expected["var1"] = pd.Categorical(expected["var1"])
+    tm.assert_frame_equal(df, expected)
+
+    df = pd.read_spss(fname, convert_categoricals=False)
+    expected = pd.DataFrame({"var1": [1.0, 2.0, 1.0, 3.0]})
+    tm.assert_frame_equal(df, expected)
+
+
+def test_spss_usecols(datapath):
+    # usecols must be list-like
+    fname = datapath("io", "data", "spss", "labelled-num.sav")
+
+    with pytest.raises(TypeError, match="usecols must be list-like."):
+        pd.read_spss(fname, usecols="VAR00002")
+
+
+def test_spss_umlauts_dtype_backend(datapath, dtype_backend):
+    # test file from the Haven project (https://haven.tidyverse.org/)
+    # Licence at LICENSES/HAVEN_LICENSE, LICENSES/HAVEN_MIT
+    fname = datapath("io", "data", "spss", "umlauts.sav")
+
+    df = pd.read_spss(fname, convert_categoricals=False, dtype_backend=dtype_backend)
+    expected = pd.DataFrame({"var1": [1.0, 2.0, 1.0, 3.0]}, dtype="Int64")
+
+    if dtype_backend == "pyarrow":
+        pa = pytest.importorskip("pyarrow")
+
+        from pandas.arrays import ArrowExtensionArray
+
+        expected = pd.DataFrame(
+            {
+                col: ArrowExtensionArray(pa.array(expected[col], from_pandas=True))
+                for col in expected.columns
+            }
+        )
+
+    tm.assert_frame_equal(df, expected)
+
+
+def test_invalid_dtype_backend():
+    msg = (
+        "dtype_backend numpy is invalid, only 'numpy_nullable' and "
+        "'pyarrow' are allowed."
+    )
+    with pytest.raises(ValueError, match=msg):
+        pd.read_spss("test", dtype_backend="numpy")
+
+
+@pytest.mark.filterwarnings("ignore::pandas.errors.ChainedAssignmentError")
+@pytest.mark.filterwarnings("ignore:ChainedAssignmentError:FutureWarning")
+def test_spss_metadata(datapath):
+    # GH 54264
+    fname = datapath("io", "data", "spss", "labelled-num.sav")
+
+    df = pd.read_spss(fname)
+    metadata = {
+        "column_names": ["VAR00002"],
+        "column_labels": [None],
+        "column_names_to_labels": {"VAR00002": None},
+        "file_encoding": "UTF-8",
+        "number_columns": 1,
+        "number_rows": 1,
+        "variable_value_labels": {"VAR00002": {1.0: "This is one"}},
+        "value_labels": {"labels0": {1.0: "This is one"}},
+        "variable_to_label": {"VAR00002": "labels0"},
+        "notes": [],
+        "original_variable_types": {"VAR00002": "F8.0"},
+        "readstat_variable_types": {"VAR00002": "double"},
+        "table_name": None,
+        "missing_ranges": {},
+        "missing_user_values": {},
+        "variable_storage_width": {"VAR00002": 8},
+        "variable_display_width": {"VAR00002": 8},
+        "variable_alignment": {"VAR00002": "unknown"},
+        "variable_measure": {"VAR00002": "unknown"},
+        "file_label": None,
+        "file_format": "sav/zsav",
+    }
+    if Version(pyreadstat.__version__) >= Version("1.2.4"):
+        metadata.update(
+            {
+                "creation_time": datetime.datetime(2015, 2, 6, 14, 33, 36),
+                "modification_time": datetime.datetime(2015, 2, 6, 14, 33, 36),
+            }
+        )
+    assert df.attrs == metadata