To pandas - hierarchical multi header (#22)

examples/llm-claude-aggregate-query.py

@@ -1,7 +1,6 @@
 import os
 
 import anthropic
-import pandas as pd
 from anthropic.types import Message
 
 from datachain import Column, DataChain
@@ -55,6 +54,4 @@
     )
 )
 
-with pd.option_context("display.max_columns", None):
-    df = chain.to_pandas()
-    print(df)
+chain.show()

examples/llm-claude-simple-query.py

@@ -2,7 +2,6 @@
 import os
 
 import anthropic
-import pandas as pd
 from anthropic.types import Message
 from pydantic import BaseModel
 
@@ -62,6 +61,4 @@ class Rating(BaseModel):
     )
 )
 
-with pd.option_context("display.max_columns", None):
-    df = chain.to_pandas()
-    print(df)
+chain.show()

examples/llm-claude.py

@@ -1,7 +1,6 @@
 import os
 
 import anthropic
-import pandas as pd
 from anthropic.types import Message
 
 from datachain import Column, DataChain, File
@@ -37,6 +36,4 @@
     )
 )
 
-with pd.option_context("display.max_columns", None):
-    df = chain.to_pandas()
-    print(df)
+chain.show()

src/datachain/lib/dc.py

@@ -11,6 +11,7 @@
     Union,
 )
 
+import pandas as pd
 import sqlalchemy
 from pydantic import BaseModel, create_model
 
@@ -38,9 +39,9 @@
     detach,
 )
 from datachain.query.schema import Column, DatasetRow
+from datachain.utils import inside_notebook
 
 if TYPE_CHECKING:
-    import pandas as pd
     from typing_extensions import Self
 
 C = Column
@@ -731,6 +732,37 @@ def from_pandas(  # type: ignore[override]
 
         return cls.from_values(name, session, object_name=object_name, **fr_map)
 
+    def to_pandas(self, flatten=False) -> "pd.DataFrame":
+        headers, max_length = self.signals_schema.get_headers_with_length()
+        if flatten or max_length < 2:
+            df = pd.DataFrame.from_records(self.to_records())
+            if headers:
+                df.columns = [".".join(filter(None, header)) for header in headers]
+            return df
+
+        transposed_result = list(map(list, zip(*self.results())))
+        data = {tuple(n): val for n, val in zip(headers, transposed_result)}
+        return pd.DataFrame(data)
+
+    def show(self, limit: int = 20, flatten=False, transpose=False) -> None:
+        dc = self.limit(limit) if limit > 0 else self
+        df = dc.to_pandas(flatten)
+        if transpose:
+            df = df.T
+
+        with pd.option_context(
+            "display.max_columns", None, "display.multi_sparse", False
+        ):
+            if inside_notebook():
+                from IPython.display import display
+
+                display(df)
+            else:
+                print(df)
+
+        if len(df) == limit:
+            print(f"\n[Limited by {len(df)} rows]")
+
     def parse_tabular(
         self,
         output: OutputType = None,

src/datachain/lib/signal_schema.py

@@ -338,6 +338,16 @@ def print_tree(self, indent: int = 4, start_at: int = 0):
                     sub_schema = SignalSchema({"* list of": args[0]})
                     sub_schema.print_tree(indent=indent, start_at=total_indent + indent)
 
+    def get_headers_with_length(self):
+        paths = [
+            path for path, _, has_subtree, _ in self.get_flat_tree() if not has_subtree
+        ]
+        max_length = max([len(path) for path in paths], default=0)
+        return [
+            path + [""] * (max_length - len(path)) if len(path) < max_length else path
+            for path in paths
+        ], max_length
+
     def __or__(self, other):
         return self.__class__(self.values | other.values)
 

src/datachain/query/dataset.py

@@ -26,7 +26,6 @@
 )
 
 import attrs
-import pandas as pd
 import sqlalchemy
 from attrs import frozen
 from dill import dumps, source
@@ -53,10 +52,9 @@
 from datachain.dataset import DatasetStatus, RowDict
 from datachain.error import DatasetNotFoundError, QueryScriptCancelError
 from datachain.progress import CombinedDownloadCallback
-from datachain.query.schema import DEFAULT_DELIMITER
 from datachain.sql.functions import rand
 from datachain.storage import Storage, StorageURI
-from datachain.utils import batched, determine_processes, inside_notebook
+from datachain.utils import batched, determine_processes
 
 from .metrics import metrics
 from .schema import C, UDFParamSpec, normalize_param
@@ -1346,12 +1344,6 @@ async def get_params(row: RowDict) -> tuple:
     def to_records(self) -> list[dict[str, Any]]:
         return self.results(lambda cols, row: dict(zip(cols, row)))
 
-    def to_pandas(self) -> "pd.DataFrame":
-        records = self.to_records()
-        df = pd.DataFrame.from_records(records)
-        df.columns = [c.replace(DEFAULT_DELIMITER, ".") for c in df.columns]
-        return df
-
     def shuffle(self) -> "Self":
         # ToDo: implement shaffle based on seed and/or generating random column
         return self.order_by(C.sys__rand)
@@ -1370,22 +1362,6 @@ def sample(self, n) -> "Self":
 
         return sampled.limit(n)
 
-    def show(self, limit=20) -> None:
-        df = self.limit(limit).to_pandas()
-
-        options = ["display.max_colwidth", 50, "display.show_dimensions", False]
-        with pd.option_context(*options):
-            if inside_notebook():
-                from IPython.display import display
-
-                display(df)
-
-            else:
-                print(df.to_string())
-
-        if len(df) == limit:
-            print(f"[limited by {limit} objects]")
-
     def clone(self, new_table=True) -> "Self":
         obj = copy(self)
         obj.steps = obj.steps.copy()

tests/func/test_dataset_query.py

@@ -3213,19 +3213,6 @@ def test_to_records(simple_ds_query):
     assert simple_ds_query.to_records() == SIMPLE_DS_QUERY_RECORDS
 
 
-@pytest.mark.parametrize(
-    "cloud_type,version_aware",
-    [("s3", True)],
-    indirect=True,
-)
-def test_to_pandas(simple_ds_query):
-    import pandas as pd
-
-    df = simple_ds_query.to_pandas()
-    expected = pd.DataFrame.from_records(SIMPLE_DS_QUERY_RECORDS)
-    assert (df == expected).all(axis=None)
-
-
 @pytest.mark.parametrize("method", ["to_records", "extract"])
 @pytest.mark.parametrize("save", [True, False])
 @pytest.mark.parametrize(

tests/unit/lib/test_datachain.py

@@ -112,11 +112,8 @@ def test_from_features(catalog):
         params="parent",
         output={"file": File, "t1": MyFr},
     )
-    df1 = ds.to_pandas()
-
-    assert df1[["t1.nnn", "t1.count"]].equals(
-        pd.DataFrame({"t1.nnn": ["n1", "n2", "n1"], "t1.count": [3, 5, 1]})
-    )
+    for i, (_, t1) in enumerate(ds.iterate()):
+        assert t1 == features[i]
 
 
 def test_preserve_feature_schema(catalog):
@@ -212,33 +209,33 @@ class _TestFr(BaseModel):
         params="t1",
         output={"x": _TestFr},
     )
+    # assert ds.collect() == 1
 
-    df = ds.to_pandas()
+    for i, (x,) in enumerate(ds.iterate()):
+        assert isinstance(x, _TestFr)
 
-    assert df["x.my_name"].tolist() == ["n1", "n2", "n1"]
-    assert np.allclose(df["x.sqrt"], [math.sqrt(x) for x in [3, 5, 1]])
-    with pytest.raises(KeyError):
-        df["x.t1.nnn"]
+        fr = features[i]
+        test_fr = _TestFr(file=File(name=""), sqrt=math.sqrt(fr.count), my_name=fr.nnn)
+        assert x == test_fr
 
 
 def test_map(catalog):
     class _TestFr(BaseModel):
         sqrt: float
         my_name: str
 
-    ds = DataChain.from_values(t1=features)
-
-    df = ds.map(
+    dc = DataChain.from_values(t1=features).map(
         x=lambda m_fr: _TestFr(
             sqrt=math.sqrt(m_fr.count),
             my_name=m_fr.nnn + "_suf",
         ),
         params="t1",
         output={"x": _TestFr},
-    ).to_pandas()
+    )
 
-    assert df["x.my_name"].tolist() == ["n1_suf", "n2_suf", "n1_suf"]
-    assert np.allclose(df["x.sqrt"], [math.sqrt(x) for x in [3, 5, 1]])
+    assert dc.collect_one("x") == [
+        _TestFr(sqrt=math.sqrt(fr.count), my_name=fr.nnn + "_suf") for fr in features
+    ]
 
 
 def test_agg(catalog):
@@ -247,26 +244,31 @@ class _TestFr(BaseModel):
         cnt: int
         my_name: str
 
-    df = (
-        DataChain.from_values(t1=features)
-        .agg(
-            x=lambda frs: [
-                _TestFr(
-                    f=File(name=""),
-                    cnt=sum(f.count for f in frs),
-                    my_name="-".join([fr.nnn for fr in frs]),
-                )
-            ],
-            partition_by=C.t1.nnn,
-            params="t1",
-            output={"x": _TestFr},
-        )
-        .to_pandas()
+    dc = DataChain.from_values(t1=features).agg(
+        x=lambda frs: [
+            _TestFr(
+                f=File(name=""),
+                cnt=sum(f.count for f in frs),
+                my_name="-".join([fr.nnn for fr in frs]),
+            )
+        ],
+        partition_by=C.t1.nnn,
+        params="t1",
+        output={"x": _TestFr},
     )
 
-    assert len(df) == 2
-    assert df["x.my_name"].tolist() == ["n1-n1", "n2"]
-    assert df["x.cnt"].tolist() == [4, 5]
+    assert dc.collect_one("x") == [
+        _TestFr(
+            f=File(name=""),
+            cnt=sum(fr.count for fr in features if fr.nnn == "n1"),
+            my_name="-".join([fr.nnn for fr in features if fr.nnn == "n1"]),
+        ),
+        _TestFr(
+            f=File(name=""),
+            cnt=sum(fr.count for fr in features if fr.nnn == "n2"),
+            my_name="-".join([fr.nnn for fr in features if fr.nnn == "n2"]),
+        ),
+    ]
 
 
 def test_agg_two_params(catalog):
@@ -294,10 +296,8 @@ class _TestFr(BaseModel):
         output={"x": _TestFr},
     )
 
-    df = ds.to_pandas()
-    assert len(df) == 2
-    assert df["x.my_name"].tolist() == ["n1-n1", "n2"]
-    assert df["x.cnt"].tolist() == [12, 15]
+    assert ds.collect_one("x.my_name") == ["n1-n1", "n2"]
+    assert ds.collect_one("x.cnt") == [12, 15]
 
 
 def test_agg_simple_iterator(catalog):
@@ -356,10 +356,8 @@ def func(key, val) -> Iterator[tuple[File, _ImageGroup]]:
     values = [1, 5, 9]
     ds = DataChain.from_values(key=keys, val=values).agg(x=func, partition_by=C("key"))
 
-    df = ds.to_pandas()
-    assert len(df) == 2
-    assert df["x_1.name"].tolist() == ["n1-n1", "n2"]
-    assert df["x_1.size"].tolist() == [10, 5]
+    assert ds.collect_one("x_1.name") == ["n1-n1", "n2"]
+    assert ds.collect_one("x_1.size") == [10, 5]
 
 
 def test_agg_tuple_result_generator(catalog):
@@ -376,10 +374,8 @@ def func(key, val) -> Generator[tuple[File, _ImageGroup], None, None]:
     values = [1, 5, 9]
     ds = DataChain.from_values(key=keys, val=values).agg(x=func, partition_by=C("key"))
 
-    df = ds.to_pandas()
-    assert len(df) == 2
-    assert df["x_1.name"].tolist() == ["n1-n1", "n2"]
-    assert df["x_1.size"].tolist() == [10, 5]
+    assert ds.collect_one("x_1.name") == ["n1-n1", "n2"]
+    assert ds.collect_one("x_1.size") == [10, 5]
 
 
 def test_iterate(catalog):
@@ -829,15 +825,15 @@ def test_from_features_object_name(tmp_dir, catalog):
     values = ["odd" if num % 2 else "even" for num in fib]
 
     dc = DataChain.from_values(fib=fib, odds=values, object_name="custom")
-    assert "custom.fib" in dc.to_pandas().columns
+    assert "custom.fib" in dc.to_pandas(flatten=True).columns
 
 
 def test_parse_tabular_object_name(tmp_dir, catalog):
     df = pd.DataFrame(DF_DATA)
     path = tmp_dir / "test.parquet"
     df.to_parquet(path)
-    dc = DataChain.from_storage(path.as_uri()).parse_tabular(object_name="name")
-    assert "name.first_name" in dc.to_pandas().columns
+    dc = DataChain.from_storage(path.as_uri()).parse_tabular(object_name="tbl")
+    assert "tbl.first_name" in dc.to_pandas(flatten=True).columns
 
 
 def test_sys_feature(tmp_dir, catalog):
@@ -868,3 +864,12 @@ def test_sys_feature(tmp_dir, catalog):
         MyFr(nnn="n1", count=1),
     ]
     assert "sys" not in ds_no_sys.catalog.get_dataset("ds_no_sys").feature_schema
+
+
+def test_to_pandas_multi_level():
+    df = DataChain.from_values(t1=features).to_pandas()
+
+    assert "t1" in df.columns
+    assert "nnn" in df["t1"].columns
+    assert "count" in df["t1"].columns
+    assert df["t1"]["count"].tolist() == [3, 5, 1]