pandas/Test_Pandas.py

"""
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)