lazily import pandas

pyproject.toml

@@ -13,7 +13,6 @@ dependencies = [
     "contourpy >=1.2",
     "numpy >=1.16",
     "packaging >=16.8",
-    "pandas >=1.2",
     "pillow >=7.1.0",
     "PyYAML >=3.10",
     "tornado >=6.2",

src/bokeh/models/sources.py

@@ -49,6 +49,7 @@
     String,
 )
 from ..model import Model
+from ..util.dependencies import get_pandas
 from ..util.deprecation import deprecated
 from ..util.serialization import convert_datetime_array
 from ..util.warnings import BokehUserWarning, warn
@@ -232,11 +233,11 @@ def __init__(self, *args: TAny, **kwargs: TAny) -> None:
         # TODO (bev) invalid to pass args and "data", check and raise exception
         raw_data: DataDict = kwargs.pop("data", {})
 
-        import pandas as pd
+        pd = get_pandas()
         if not isinstance(raw_data, dict):
-            if isinstance(raw_data, pd.DataFrame):
+            if pd is not None and isinstance(raw_data, pd.DataFrame):
                 raw_data = self._data_from_df(raw_data)
-            elif isinstance(raw_data, pd.core.groupby.GroupBy):
+            elif pd is not None and isinstance(raw_data, pd.core.groupby.GroupBy):
                 raw_data = self._data_from_groupby(raw_data)
             else:
                 raise ValueError(f"expected a dict or pandas.DataFrame, got {raw_data}")

src/bokeh/plotting/_plot.py

@@ -50,6 +50,7 @@
     Range1d,
     Scale,
 )
+from ..util.dependencies import get_pandas
 
 if TYPE_CHECKING:
     import pandas as pd
@@ -77,16 +78,16 @@
 #-----------------------------------------------------------------------------
 
 def get_range(range_input: Range | tuple[float, float] | Sequence[str] | pd.Series[Any] | GroupBy | None) -> Range:
-    import pandas as pd
-    from pandas.core.groupby import GroupBy
+
+    pandas = get_pandas()
 
     if range_input is None:
         return DataRange1d()
-    if isinstance(range_input, GroupBy):
+    if pandas is not None and isinstance(range_input, pandas.core.groupby.GroupBy):
         return FactorRange(factors=sorted(list(range_input.groups.keys())))
     if isinstance(range_input, Range):
         return range_input
-    if isinstance(range_input, pd.Series):
+    if pandas is not None and isinstance(range_input, pandas.Series):
         range_input = range_input.values
     if isinstance(range_input, (Sequence, np.ndarray)):
         if all(isinstance(x, str) for x in range_input):

src/bokeh/util/dependencies.py

@@ -23,7 +23,10 @@
 # Standard library imports
 from importlib import import_module
 from types import ModuleType
-from typing import Any
+from typing import TYPE_CHECKING, Any
+
+if TYPE_CHECKING:
+    import pandas as pd
 
 #-----------------------------------------------------------------------------
 # Globals and constants
@@ -32,6 +35,7 @@
 __all__ = (
     'import_optional',
     'import_required',
+    'get_pandas',
     'uses_pandas',
 )
 
@@ -92,6 +96,16 @@ def uses_pandas(obj: Any) -> bool:
     module = type(obj).__module__
     return module is not None and module.startswith("pandas.")
 
+def get_pandas() -> pd | None:
+    """
+    Import pandas (if available, else return None).
+    """
+    try:
+        import pandas
+    except ImportError:
+        return None
+    return pandas
+
 #-----------------------------------------------------------------------------
 # Dev API
 #-----------------------------------------------------------------------------

src/bokeh/util/serialization.py

@@ -40,6 +40,7 @@
 # Bokeh imports
 from ..core.types import ID
 from ..settings import settings
+from ..util.dependencies import get_pandas
 from .strings import format_docstring
 
 if TYPE_CHECKING:
@@ -52,13 +53,14 @@
 
 @lru_cache(None)
 def _compute_datetime_types() -> set[type]:
-    import pandas as pd
+    pd = get_pandas()
 
     result = {dt.time, dt.datetime, np.datetime64}
-    result.add(pd.Timestamp)
-    result.add(pd.Timedelta)
-    result.add(pd.Period)
-    result.add(type(pd.NaT))
+    if pd is not None:
+        result.add(pd.Timestamp)
+        result.add(pd.Timedelta)
+        result.add(pd.Period)
+        result.add(type(pd.NaT))
     return result
 
 def __getattr__(name: str) -> Any: