python: add log, log10, exp

py-polars/docs/source/reference/expression.rst

@@ -147,6 +147,9 @@ Computations
     Expr.arcsin
     Expr.arccos
     Expr.arctan
+    Expr.log
+    Expr.log10
+    Expr.exp
 
 Manipulation/ selection
 -----------------------

py-polars/docs/source/reference/series.rst

@@ -121,6 +121,9 @@ Computations
     Series.arcsin
     Series.arccos
     Series.arctan
+    Series.log
+    Series.log10
+    Series.exp
 
 Manipulation/ selection
 -----------------------

py-polars/polars/internals/expr.py

@@ -197,6 +197,27 @@ def sqrt(self) -> "Expr":
         """
         return self ** 0.5
 
+    def log(self) -> "Expr":
+        """
+        Natural logarithm, element-wise.
+
+        The natural logarithm log is the inverse of the exponential function, so that log(exp(x)) = x.
+        The natural logarithm is logarithm in base e.
+        """
+        return np.log(self)  # type: ignore
+
+    def log10(self) -> "Expr":
+        """
+        Return the base 10 logarithm of the input array, element-wise.
+        """
+        return np.log10(self)  # type: ignore
+
+    def exp(self) -> "Expr":
+        """
+        Return the exponential element-wise
+        """
+        return np.exp(self)  # type: ignore
+
     def alias(self, name: str) -> "Expr":
         """
         Rename the output of an expression.

py-polars/polars/internals/lazy_functions.py

@@ -1135,7 +1135,7 @@ def collect_all(
 
 
 def select(
-    exprs: Union[str, "pli.Expr", Sequence[str], Sequence["pli.Expr"]]
+    exprs: Union[str, "pli.Expr", Sequence[str], Sequence["pli.Expr"], "pli.Series"]
 ) -> "pli.DataFrame":
     """
     Run polars expressions without a context.

py-polars/polars/internals/series.py

@@ -559,6 +559,27 @@ def sqrt(self) -> "Series":
         """
         return self ** 0.5
 
+    def log(self) -> "Series":
+        """
+        Natural logarithm, element-wise.
+
+        The natural logarithm log is the inverse of the exponential function, so that log(exp(x)) = x.
+        The natural logarithm is logarithm in base e.
+        """
+        return np.log(self)  # type: ignore
+
+    def log10(self) -> "Series":
+        """
+        Return the base 10 logarithm of the input array, element-wise.
+        """
+        return np.log10(self)  # type: ignore
+
+    def exp(self) -> "Series":
+        """
+        Return the exponential element-wise
+        """
+        return np.exp(self)  # type: ignore
+
     def drop_nulls(self) -> "Series":
         """
         Create a new Series that copies data from this Series without null values.

py-polars/tests/test_exprs.py

@@ -1,4 +1,7 @@
+import numpy as np
+
 import polars as pl
+from polars import testing
 
 
 def test_horizontal_agg(fruits_cars: pl.DataFrame) -> None:
@@ -31,3 +34,18 @@ def test_cumcount() -> None:
 
     assert out["foo"][0].to_list() == [0, 1, 2, 3]
     assert out["foo"][1].to_list() == [0, 1]
+
+
+def test_log_exp() -> None:
+    a = pl.Series("a", [1, 100, 1000])
+    out = pl.select(a.log10()).to_series()
+    expected = pl.Series("a", [0.0, 2.0, 3.0])
+    testing.assert_series_equal(out, expected)
+
+    out = pl.select(a.log()).to_series()
+    expected = pl.Series("a", np.log(a.to_numpy()))
+    testing.assert_series_equal(out, expected)
+
+    out = pl.select(a.exp()).to_series()
+    expected = pl.Series("a", np.exp(a.to_numpy()))
+    testing.assert_series_equal(out, expected)

py-polars/tests/test_series.py

@@ -1076,3 +1076,20 @@ def test_nested_list_types_preserved() -> None:
     srs1 = pl.Series([pl.Series([3, 4, 5, 6], dtype=expected_dtype) for _ in range(5)])
     for srs2 in srs1:
         assert srs2.dtype == expected_dtype
+
+
+def test_log_exp() -> None:
+    a = pl.Series("a", [1, 100, 1000])
+
+    out = a.log10()
+    expected = pl.Series("a", [0.0, 2.0, 3.0])
+    testing.assert_series_equal(out, expected)
+    a = pl.Series("a", [1, 100, 1000])
+
+    out = a.log()
+    expected = pl.Series("a", np.log(a.to_numpy()))
+    testing.assert_series_equal(out, expected)
+
+    out = a.exp()
+    expected = pl.Series("a", np.exp(a.to_numpy()))
+    testing.assert_series_equal(out, expected)