Support input/output to/from polars via python (not native rust)

rust/perspective-python/docs/client/to_pandas.md

@@ -0,0 +1 @@
+Serialize the data to a `pandas.DataFrame`.

rust/perspective-python/docs/client/to_polars.md

@@ -0,0 +1 @@
+Serialize the data to a `polars.DataFrame`.

rust/perspective-python/docs/lib.md

@@ -119,6 +119,7 @@ guide. In Python, however, Perspective supports additional data types that are
 commonly used when processing data:
 
 -   `pandas.DataFrame`
+-   `polars.DataFrame`
 -   `bytes` (encoding an Apache Arrow)
 -   `objects` (either extracting a repr or via reference)
 -   `str` (encoding as a CSV)

rust/perspective-python/perspective/tests/table/test_table_polars.py

@@ -0,0 +1,251 @@
+#  ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
+#  ┃ ██████ ██████ ██████       █      █      █      █      █ █▄  ▀███ █       ┃
+#  ┃ ▄▄▄▄▄█ █▄▄▄▄▄ ▄▄▄▄▄█  ▀▀▀▀▀█▀▀▀▀▀ █ ▀▀▀▀▀█ ████████▌▐███ ███▄  ▀█ █ ▀▀▀▀▀ ┃
+#  ┃ █▀▀▀▀▀ █▀▀▀▀▀ █▀██▀▀ ▄▄▄▄▄ █ ▄▄▄▄▄█ ▄▄▄▄▄█ ████████▌▐███ █████▄   █ ▄▄▄▄▄ ┃
+#  ┃ █      ██████ █  ▀█▄       █ ██████      █      ███▌▐███ ███████▄ █       ┃
+#  ┣━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┫
+#  ┃ Copyright (c) 2017, the Perspective Authors.                              ┃
+#  ┃ ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌ ┃
+#  ┃ This file is part of the Perspective library, distributed under the terms ┃
+#  ┃ of the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0). ┃
+#  ┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
+
+from datetime import date, datetime
+import numpy as np
+import polars as pl
+from pytest import mark
+import perspective as psp
+
+client = psp.Server().new_local_client()
+Table = client.table
+
+
+def arrow_bytes_to_polars(view):
+    import pyarrow
+
+    with pyarrow.ipc.open_stream(pyarrow.BufferReader(view.to_arrow())) as reader:
+        return pl.from_dataframe(reader.read_pandas())
+
+
+class TestTablePolars(object):
+    def test_empty_table(self):
+        tbl = Table([])
+        assert tbl.size() == 0
+        assert tbl.schema() == {}
+
+    def test_table_dataframe(self):
+        d = [{"a": 1, "b": 2}, {"a": 3, "b": 4}]
+        data = pl.DataFrame(d)
+        tbl = Table(data)
+        assert tbl.size() == 2
+        assert tbl.schema() == {"a": "integer", "b": "integer"}
+        assert tbl.view().to_records() == [
+            {"a": 1, "b": 2},
+            {"a": 3, "b": 4},
+        ]
+
+    def test_table_lazyframe(self):
+        d = [{"a": 1, "b": 2}, {"a": 3, "b": 4}]
+        data = pl.DataFrame(d).lazy()
+        tbl = Table(data)
+        assert tbl.size() == 2
+        assert tbl.schema() == {"a": "integer", "b": "integer"}
+        assert tbl.view().to_records() == [
+            {"a": 1, "b": 2},
+            {"a": 3, "b": 4},
+        ]
+
+    def test_table_dataframe_column_order(self):
+        d = [{"a": 1, "b": 2, "c": 3, "d": 4}, {"a": 3, "b": 4, "c": 5, "d": 6}]
+        data = pl.DataFrame(d).select(["b", "c", "a", "d"])
+        tbl = Table(data)
+        assert tbl.size() == 2
+        assert tbl.columns() == ["b", "c", "a", "d"]
+
+    def test_table_dataframe_selective_column_order(self):
+        d = [{"a": 1, "b": 2, "c": 3, "d": 4}, {"a": 3, "b": 4, "c": 5, "d": 6}]
+        data = pl.DataFrame(d).select(["b", "c", "a"])
+        tbl = Table(data)
+        assert tbl.size() == 2
+        assert tbl.columns() == ["b", "c", "a"]
+
+    def test_table_dataframe_does_not_mutate(self):
+        # make sure we don't mutate the dataframe that a user passes in
+        data = pl.DataFrame(
+            {
+                "a": [None, 1, None, 2],
+                "b": [1.5, None, 2.5, None],
+            }
+        )
+        assert data["a"].to_list() == [None, 1, None, 2]
+        assert data["b"].to_list() == [1.5, None, 2.5, None]
+
+        tbl = Table(data)
+        assert tbl.size() == 4
+        assert tbl.schema() == {"a": "integer", "b": "float"}
+
+        assert data["a"].to_list() == [None, 1, None, 2]
+        assert data["b"].to_list() == [1.5, None, 2.5, None]
+
+    def test_table_polars_from_schema_int(self):
+        data = [None, 1, None, 2, None, 3, 4]
+        df = pl.DataFrame({"a": data})
+        table = Table({"a": "integer"})
+        table.update(df)
+        assert table.view().to_columns()["a"] == data
+
+    def test_table_polars_from_schema_bool(self):
+        data = [True, False, True, False]
+        df = pl.DataFrame({"a": data})
+        table = Table({"a": "boolean"})
+        table.update(df)
+        assert table.view().to_columns()["a"] == data
+
+    def test_table_polars_from_schema_float(self):
+        data = [None, 1.5, None, 2.5, None, 3.5, 4.5]
+        df = pl.DataFrame({"a": data})
+        table = Table({"a": "float"})
+        table.update(df)
+        assert table.view().to_columns()["a"] == data
+
+    def test_table_polars_from_schema_float_all_nan(self):
+        data = [np.nan, np.nan, np.nan, np.nan]
+        df = pl.DataFrame({"a": data})
+        table = Table({"a": "float"})
+        table.update(df)
+        assert table.view().to_columns()["a"] == [None, None, None, None]
+
+    def test_table_polars_from_schema_float_to_int(self):
+        data = [None, 1.5, None, 2.5, None, 3.5, 4.5]
+        df = pl.DataFrame({"a": data})
+        table = Table({"a": "integer"})
+        table.update(df)
+        # truncates decimal
+        assert table.view().to_columns()["a"] == [None, 1, None, 2, None, 3, 4]
+
+    def test_table_polars_from_schema_int_to_float(self):
+        data = [None, 1, None, 2, None, 3, 4]
+        df = pl.DataFrame({"a": data})
+        table = Table({"a": "float"})
+        table.update(df)
+        assert table.view().to_columns()["a"] == [None, 1.0, None, 2.0, None, 3.0, 4.0]
+
+    def test_table_polars_from_schema_date(self, util):
+        data = [date(2019, 8, 15), None, date(2019, 8, 16)]
+        df = pl.DataFrame({"a": data})
+        table = Table({"a": "date"})
+        table.update(df)
+        assert table.view().to_columns()["a"] == [
+            util.to_timestamp(datetime(2019, 8, 15)),
+            None,
+            util.to_timestamp(datetime(2019, 8, 16)),
+        ]
+
+    def test_table_polars_from_schema_str(self):
+        data = ["a", None, "b", None, "c"]
+        df = pl.DataFrame({"a": data})
+        table = Table({"a": "string"})
+        table.update(df)
+        assert table.view().to_columns()["a"] == data
+
+    def test_table_polars_none(self):
+        data = [None, None, None]
+        df = pl.DataFrame({"a": data})
+        table = Table(df)
+        assert table.view().to_columns()["a"] == data
+
+    def test_table_polars_symmetric_table(self):
+        # make sure that updates are symmetric to table creation
+        df = pl.DataFrame({"a": [1, 2, 3, 4], "b": [1.5, 2.5, 3.5, 4.5]})
+        t1 = Table(df)
+        t2 = Table({"a": "integer", "b": "float"})
+        t2.update(df)
+        assert t1.view().to_columns() == {
+            "a": [1, 2, 3, 4],
+            "b": [1.5, 2.5, 3.5, 4.5],
+        }
+
+    def test_table_polars_symmetric_stacked_updates(self):
+        # make sure that updates are symmetric to table creation
+        df = pl.DataFrame({"a": [1, 2, 3, 4], "b": [1.5, 2.5, 3.5, 4.5]})
+
+        t1 = Table(df)
+        t1.update(df)
+
+        t2 = Table({"a": "integer", "b": "float"})
+        t2.update(df)
+        t2.update(df)
+
+        assert t1.view().to_columns() == {
+            "a": [1, 2, 3, 4, 1, 2, 3, 4],
+            "b": [1.5, 2.5, 3.5, 4.5, 1.5, 2.5, 3.5, 4.5],
+        }
+
+    @mark.skip(reason="Not supported, polars doesnt like input")
+    def test_table_polars_transitive(self):
+        # serialized output -> table -> serialized output
+        records = {
+            "a": [1, 2, 3, 4],
+            "b": [1.5, 2.5, 3.5, 4.5],
+            "c": [np.nan, np.nan, "abc", np.nan],
+            "d": [None, True, None, False],
+            "e": [
+                float("nan"),
+                datetime(2019, 7, 11, 12, 30),
+                float("nan"),
+                datetime(2019, 7, 11, 12, 30),
+            ],
+        }
+
+        df = pl.DataFrame(records, strict=False)
+        t1 = Table(df)
+        out1 = arrow_bytes_to_polars(t1.view(columns=["a", "b", "c", "d", "e"]))
+        t2 = Table(out1)
+        assert t1.schema() == t2.schema()
+        out2 = t2.view().to_columns()
+        assert t1.view().to_columns() == out2
+
+    # dtype=object should have correct inferred types
+
+    def test_table_polars_object_to_int(self):
+        df = pl.DataFrame({"a": [1, 2, None, 2, None, 3, 4]})
+        table = Table(df)
+        assert table.schema() == {"a": "integer"}
+        assert table.view().to_columns()["a"] == [1, 2, None, 2, None, 3, 4]
+
+    def test_table_polars_object_to_float(self):
+        df = pl.DataFrame({"a": [None, 1, None, 2, None, 3, 4]})
+        table = Table(df)
+        assert table.schema() == {"a": "integer"}
+        assert table.view().to_columns()["a"] == [None, 1.0, None, 2.0, None, 3.0, 4.0]
+
+    def test_table_polars_object_to_bool(self):
+        df = pl.DataFrame({"a": [True, False, True, False, True, False]})
+        table = Table(df)
+        assert table.schema() == {"a": "boolean"}
+        assert table.view().to_columns()["a"] == [True, False, True, False, True, False]
+
+    def test_table_polars_object_to_datetime(self):
+        df = pl.DataFrame(
+            {
+                "a": [
+                    datetime(2019, 7, 11, 1, 2, 3),
+                    datetime(2019, 7, 12, 1, 2, 3),
+                    None,
+                ]
+            }
+        )
+
+        table = Table(df)
+        assert table.schema() == {"a": "datetime"}
+        assert table.view().to_columns()["a"] == [
+            datetime(2019, 7, 11, 1, 2, 3).timestamp() * 1000,
+            datetime(2019, 7, 12, 1, 2, 3).timestamp() * 1000,
+            None,
+        ]
+
+    def test_table_polars_object_to_str(self):
+        df = pl.DataFrame({"a": np.array(["abc", "def", None, "ghi"], dtype=object)})
+        table = Table(df)
+        assert table.schema() == {"a": "string"}
+        assert table.view().to_columns()["a"] == ["abc", "def", None, "ghi"]

rust/perspective-python/requirements.txt

@@ -8,6 +8,7 @@ maturin==1.6.0
 numpy==2.0.0
 packaging==24.1
 pandas==2.2.2
+polars==1.13.1
 pyarrow==16.1.0
 psutil==6.0.0
 pytest==8.2.2

rust/perspective-python/src/client/client_sync.rs

@@ -346,11 +346,25 @@ impl View {
         self.0.to_csv(window).py_block_on(py)
     }
 
+    #[doc = include_str!("../../docs/client/to_pandas.md")]
     #[pyo3(signature = (**window))]
+    // #[deprecated(since="3.2.0", note="Please use `View::to_pandas`")]
     pub fn to_dataframe(&self, py: Python<'_>, window: Option<Py<PyDict>>) -> PyResult<Py<PyAny>> {
         self.0.to_dataframe(window).py_block_on(py)
     }
 
+    #[doc = include_str!("../../docs/client/to_pandas.md")]
+    #[pyo3(signature = (**window))]
+    pub fn to_pandas(&self, py: Python<'_>, window: Option<Py<PyDict>>) -> PyResult<Py<PyAny>> {
+        self.0.to_dataframe(window).py_block_on(py)
+    }
+
+    #[doc = include_str!("../../docs/client/to_polars.md")]
+    #[pyo3(signature = (**window))]
+    pub fn to_polars(&self, py: Python<'_>, window: Option<Py<PyDict>>) -> PyResult<Py<PyAny>> {
+        self.0.to_polars(window).py_block_on(py)
+    }
+
     #[doc = crate::inherit_docs!("view/to_arrow.md")]
     #[pyo3(signature = (**window))]
     pub fn to_arrow(&self, py: Python<'_>, window: Option<Py<PyDict>>) -> PyResult<Py<PyBytes>> {

rust/perspective-python/src/client/mod.rs

@@ -12,6 +12,7 @@
 
 pub mod client_sync;
 mod pandas;
+mod polars;
 mod pyarrow;
 pub mod python;
 pub mod table_data;