Delete pandas/Test_Pandas.py

pandas/Test_Pandas.py

@@ -1,282 +0,0 @@
-"""
-Generated by RIMI
-"""
-import sys
-import traceback
-
-passed = 0
-failed = 0
-skipped = 0
-
-def test(name, func):
-    global passed, failed, skipped
-    try:
-        result = func()
-        if isinstance(result, str) and result == "SKIP":
-            skipped += 1
-            print(f"  SKIP  #{passed+failed+skipped:02d} {name}")
-        else:
-            passed += 1
-            print(f"  OK    #{passed+failed+skipped:02d} {name}")
-    except Exception as e:
-        failed += 1
-        print(f"  FAIL  #{passed+failed+skipped:02d} {name}: {e}")
-        traceback.print_exc()
-
-print("=" * 60)
-print("pandas 2.3.3 — Android norelro test")
-print("Python", sys.version)
-print("=" * 60)
-
-# 1. import pandas
-test("import pandas", lambda: __import__("pandas"))
-
-# 2. version check
-test("pandas.__version__", lambda: None if __import__("pandas").__version__ == "2.3.3" else (_ for _ in ()).throw(Exception(f"wrong version")))
-
-# 3. import numpy (bundled dep)
-test("import numpy (bundled dep)", lambda: __import__("numpy"))
-
-# 4. DataFrame basics
-test("DataFrame create", lambda: __import__("pandas").DataFrame({"a": [1, 2], "b": [3, 4]}))
-
-# 5. DataFrame shape
-def test_shape():
-    import pandas as pd
-    df = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
-    assert df.shape == (3, 2), f"wrong shape: {df.shape}"
-test("DataFrame shape", test_shape)
-
-# 6. DataFrame head/tail
-def test_head_tail():
-    import pandas as pd
-    df = pd.DataFrame({"x": range(100)})
-    assert len(df.head(5)) == 5
-    assert len(df.tail(5)) == 5
-test("DataFrame head/tail", test_head_tail)
-
-# 7. DataFrame dtypes
-def test_dtypes():
-    import pandas as pd
-    df = pd.DataFrame({"a": [1, 2], "b": [1.0, 2.0], "c": ["x", "y"]})
-    assert df.dtypes["a"].name == "int64"
-    assert df.dtypes["b"].name == "float64"
-    assert df.dtypes["c"].name == "object"
-test("DataFrame dtypes", test_dtypes)
-
-# 8. DataFrame describe
-def test_describe():
-    import pandas as pd
-    df = pd.DataFrame({"a": [1, 2, 3, 4, 5]})
-    desc = df.describe()
-    assert desc.loc["mean", "a"] == 3.0
-test("DataFrame describe", test_describe)
-
-# 9. DataFrame groupby
-def test_groupby():
-    import pandas as pd
-    df = pd.DataFrame({"g": ["a", "a", "b"], "v": [1, 2, 3]})
-    result = df.groupby("g")["v"].sum()
-    assert result["a"] == 3
-    assert result["b"] == 3
-test("DataFrame groupby", test_groupby)
-
-# 10. DataFrame sort
-def test_sort():
-    import pandas as pd
-    df = pd.DataFrame({"a": [3, 1, 2]})
-    df_sorted = df.sort_values("a")
-    assert list(df_sorted["a"]) == [1, 2, 3]
-test("DataFrame sort_values", test_sort)
-
-# 11. DataFrame apply
-def test_apply():
-    import pandas as pd
-    df = pd.DataFrame({"a": [1, 2, 3]})
-    result = df["a"].apply(lambda x: x * 2)
-    assert list(result) == [2, 4, 6]
-test("DataFrame apply", test_apply)
-
-# 12. DataFrame merge
-def test_merge():
-    import pandas as pd
-    a = pd.DataFrame({"k": [1, 2], "v": ["a", "b"]})
-    b = pd.DataFrame({"k": [1, 2], "w": ["c", "d"]})
-    m = a.merge(b, on="k")
-    assert list(m.columns) == ["k", "v", "w"]
-    assert len(m) == 2
-test("DataFrame merge", test_merge)
-
-# 13. DataFrame concat
-def test_concat():
-    import pandas as pd
-    a = pd.DataFrame({"a": [1, 2]})
-    b = pd.DataFrame({"a": [3, 4]})
-    c = pd.concat([a, b], ignore_index=True)
-    assert list(c["a"]) == [1, 2, 3, 4]
-test("DataFrame concat", test_concat)
-
-# 14. DataFrame fillna/dropna
-def test_fillna():
-    import pandas as pd
-    df = pd.DataFrame({"a": [1, None, 3]})
-    filled = df.fillna(0)
-    assert list(filled["a"]) == [1.0, 0.0, 3.0]
-    dropped = df.dropna()
-    assert len(dropped) == 2
-test("DataFrame fillna/dropna", test_fillna)
-
-# 15. DataFrame pivot
-def test_pivot():
-    import pandas as pd
-    df = pd.DataFrame({"r": ["a", "a"], "c": ["x", "y"], "v": [1, 2]})
-    p = df.pivot(index="r", columns="c", values="v")
-    assert p.loc["a", "x"] == 1
-    assert p.loc["a", "y"] == 2
-test("DataFrame pivot", test_pivot)
-
-# 16. DataFrame melt
-def test_melt():
-    import pandas as pd
-    df = pd.DataFrame({"id": [1], "x": [2], "y": [3]})
-    m = pd.melt(df, id_vars=["id"])
-    assert len(m) == 2
-test("DataFrame melt", test_melt)
-
-# 17. Series operations
-def test_series():
-    import pandas as pd
-    s = pd.Series([1, 2, 3, 4])
-    assert s.sum() == 10
-    assert s.mean() == 2.5
-    assert s.max() == 4
-    assert s.min() == 1
-test("Series agg ops", test_series)
-
-# 18. DatetimeIndex
-def test_datetime():
-    import pandas as pd
-    dates = pd.date_range("2024-01-01", periods=5, freq="D")
-    assert len(dates) == 5
-    assert dates[0].year == 2024
-    assert dates[0].month == 1
-    assert dates[0].day == 1
-test("DatetimeIndex", test_datetime)
-
-# 19. Timedelta
-def test_timedelta():
-    import pandas as pd
-    td = pd.Timedelta("1 day 2 hours")
-    assert td.total_seconds() == 93600.0
-test("Timedelta", test_timedelta)
-
-# 20. read_csv / to_csv roundtrip
-def test_csv():
-    import pandas as pd, os, tempfile
-    df = pd.DataFrame({"a": [1, 2, 3], "b": ["x", "y", "z"]})
-    with tempfile.NamedTemporaryFile(suffix=".csv", delete=False, mode="w") as f:
-        df.to_csv(f, index=False)
-        tmp = f.name
-    df2 = pd.read_csv(tmp)
-    os.unlink(tmp)
-    assert list(df2["a"]) == [1, 2, 3]
-    assert list(df2["b"]) == ["x", "y", "z"]
-test("read_csv / to_csv roundtrip", test_csv)
-
-# 21. read_json / to_json roundtrip
-def test_json():
-    import pandas as pd, os, tempfile
-    df = pd.DataFrame({"a": [1, 2], "b": [3.0, 4.0]})
-    with tempfile.NamedTemporaryFile(suffix=".json", delete=False) as f:
-        df.to_json(f, orient="records")
-        tmp = f.name
-    df2 = pd.read_json(tmp, orient="records")
-    os.unlink(tmp)
-    assert list(df2["a"]) == [1, 2]
-test("read_json / to_json roundtrip", test_json)
-
-# 22. DataFrame value_counts
-def test_value_counts():
-    import pandas as pd
-    s = pd.Series(["a", "b", "a", "a", "b"])
-    vc = s.value_counts()
-    assert vc["a"] == 3
-    assert vc["b"] == 2
-test("Series value_counts", test_value_counts)
-
-# 23. DataFrame corr
-def test_corr():
-    import pandas as pd
-    df = pd.DataFrame({"a": [1, 2, 3], "b": [2, 4, 6]})
-    corr = df["a"].corr(df["b"])
-    assert abs(corr - 1.0) < 1e-10
-test("DataFrame corr", test_corr)
-
-# 24. DataFrame map/replace
-def test_replace():
-    import pandas as pd
-    s = pd.Series([1, 2, 3])
-    r = s.replace({1: "a", 2: "b", 3: "c"})
-    assert list(r) == ["a", "b", "c"]
-test("Series replace", test_replace)
-
-# 25. MultiIndex
-def test_multiindex():
-    import pandas as pd
-    arrays = [["a", "a", "b", "b"], [1, 2, 1, 2]]
-    idx = pd.MultiIndex.from_arrays(arrays, names=["l1", "l2"])
-    df = pd.DataFrame({"v": [10, 20, 30, 40]}, index=idx)
-    assert df.loc["a", 1].iloc[0] == 10
-test("MultiIndex", test_multiindex)
-
-# 26. DataFrame to_numpy
-def test_to_numpy():
-    import pandas as pd
-    df = pd.DataFrame({"a": [1, 2], "b": [3, 4]})
-    arr = df.to_numpy()
-    assert arr.shape == (2, 2)
-    assert arr[0, 0] == 1
-    assert arr[1, 1] == 4
-test("DataFrame to_numpy", test_to_numpy)
-
-# 27. Categorical
-def test_categorical():
-    import pandas as pd
-    s = pd.Categorical(["a", "b", "a", "c"])
-    assert len(s) == 4
-    assert s.categories.tolist() == ["a", "b", "c"]
-test("Categorical", test_categorical)
-
-# 28. DataFrame assign
-def test_assign():
-    import pandas as pd
-    df = pd.DataFrame({"a": [1, 2]})
-    df2 = df.assign(b=df["a"] * 10)
-    assert list(df2["b"]) == [10, 20]
-test("DataFrame assign", test_assign)
-
-# 29. DataFrame pipe
-def test_pipe():
-    import pandas as pd
-    df = pd.DataFrame({"a": [1, 2, 3]})
-    def add_one(data):
-        return data.assign(b=data["a"] + 1)
-    df2 = df.pipe(add_one)
-    assert list(df2["b"]) == [2, 3, 4]
-test("DataFrame pipe", test_pipe)
-
-# 30. DataFrame nunique/nlargest
-def test_nlargest():
-    import pandas as pd
-    df = pd.DataFrame({"a": [10, 1, 5, 20, 3]})
-    top = df.nlargest(2, "a")
-    assert list(top["a"]) == [20, 10]
-test("DataFrame nlargest", test_nlargest)
-
-print()
-print("=" * 60)
-print(f"RESULT: {passed} PASS, {failed} FAIL, {skipped} SKIP")
-print("=" * 60)
-
-if failed > 0:
-    sys.exit(1)