feat: Implement sum, mean, min, max, std, and var Series methods

leanframe/core/series.py

@@ -94,6 +94,33 @@ def __mul__(self, other) -> Series:
     def __rmul__(self, other) -> Series:
         return Series(getattr(other, "_data", other) * self._data)
 
+    def __round__(self, n) -> Series:
+        return Series(self._data.round(n))
+
+    def sum(self):
+        """Return the sum of the Series."""
+        return self._data.sum().to_pyarrow().as_py()
+
+    def mean(self):
+        """Return the mean of the Series."""
+        return self._data.mean().to_pyarrow().as_py()
+
+    def min(self):
+        """Return the min of the Series."""
+        return self._data.min().to_pyarrow().as_py()
+
+    def max(self):
+        """Return the max of the Series."""
+        return self._data.max().to_pyarrow().as_py()
+
+    def std(self):
+        """Return the std of the Series."""
+        return self._data.std().to_pyarrow().as_py()
+
+    def var(self):
+        """Return the var of the Series."""
+        return self._data.var().to_pyarrow().as_py()
+
     def copy(self) -> Series:
         """Return a copy of the Series."""
         return Series(self._data)

specs/2025-09-16-series-methods.md

@@ -72,7 +72,7 @@ and check off the item with an x.
 - [ ] pandas.Series.rpow
 - [ ] pandas.Series.combine
 - [ ] pandas.Series.combine_first
-- [ ] pandas.Series.round
+- [x] pandas.Series.round
 - [ ] pandas.Series.lt
 - [ ] pandas.Series.gt
 - [ ] pandas.Series.le
@@ -108,10 +108,10 @@ and check off the item with an x.
 - [ ] pandas.Series.diff
 - [ ] pandas.Series.factorize
 - [ ] pandas.Series.kurt
-- [ ] pandas.Series.max
-- [ ] pandas.Series.mean
+- [x] pandas.Series.max
+- [x] pandas.Series.mean
 - [ ] pandas.Series.median
-- [ ] pandas.Series.min
+- [x] pandas.Series.min
 - [ ] pandas.Series.mode
 - [ ] pandas.Series.nlargest
 - [ ] pandas.Series.nsmallest
@@ -121,9 +121,9 @@ and check off the item with an x.
 - [ ] pandas.Series.rank
 - [ ] pandas.Series.sem
 - [ ] pandas.Series.skew
-- [ ] pandas.Series.std
-- [ ] pandas.Series.sum
-- [ ] pandas.Series.var
+- [x] pandas.Series.std
+- [x] pandas.Series.sum
+- [x] pandas.Series.var
 - [ ] pandas.Series.kurtosis
 - [ ] pandas.Series.unique
 - [ ] pandas.Series.nunique

tests/unit/test_series.py

@@ -38,6 +38,17 @@ def series_for_properties(session):
     return df_lf["int_col"], df_lf["float_col"]
 
 
+@pytest.fixture
+def numeric_series(session):
+    df_pd = pd.DataFrame(
+        {
+            "a": [1, 2, 3, 4, 5],
+            "b": [1.1, 2.2, 3.3, 4.4, 5.5],
+        }
+    )
+    return session.DataFrame(df_pd)
+
+
 def test_series_ndim(series_for_properties):
     series_int, series_float = series_for_properties
     assert series_int.ndim == 1
@@ -189,6 +200,52 @@ def test_series_arithmetic_scalar(session, op, other, expected_data):
     )
 
 
+def test_series_round(numeric_series):
+    series = numeric_series["b"]
+    result = round(series, 0)
+    expected = pd.Series(
+        [1.0, 2.0, 3.0, 4.0, 6.0],
+        dtype=pd.ArrowDtype(pa.float64()),
+    )
+    result_pd = result.to_pandas()
+    result_pd = result_pd.astype(pd.ArrowDtype(pa.float64()))
+    pd.testing.assert_series_equal(
+        result_pd,
+        expected,
+        check_names=False,
+    )
+
+
+def test_series_sum(numeric_series):
+    series = numeric_series["a"]
+    assert series.sum() == 15
+
+
+def test_series_mean(numeric_series):
+    series = numeric_series["a"]
+    assert series.mean() == 3.0
+
+
+def test_series_min(numeric_series):
+    series = numeric_series["a"]
+    assert series.min() == 1
+
+
+def test_series_max(numeric_series):
+    series = numeric_series["a"]
+    assert series.max() == 5
+
+
+def test_series_std(numeric_series):
+    series = numeric_series["b"]
+    assert round(series.std(), 2) == 1.74
+
+
+def test_series_var(numeric_series):
+    series = numeric_series["b"]
+    assert round(series.var(), 2) == 3.03
+
+
 def test_series_copy(session):
     df_pd = pd.DataFrame({"col1": [1, 2, 3]})
     df_lf = session.DataFrame(df_pd)