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feat: Refactor anywidget display to use _ipython_display_
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‎README.rst‎

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@@ -110,6 +110,19 @@ For details, see the `third_party
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directory.
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Display Enhancements
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--------------------
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**DataFrame `_repr_html_()` and `_ipython_display_()` Integration**
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The `bigframes.pandas.DataFrame._repr_html_()` method has been updated. When `bpd.options.display.repr_mode` is set to “anywidget”, it will:
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* Wrap the import of `anywidget` in a `try...except ImportError` block. If the dependency is not found, it will issue a `warnings.warn` message and fall back to returning the deferred representation.
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* If the import is successful, it instantiates a new `TableWidget`, passing the DataFrame's data.
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* Return the widget instance, which Jupyter automatically renders. A new widget instance is created for each `_repr_html_()` call to ensure cell outputs are isolated.
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A new `_ipython_display_()` method has been introduced to handle the actual widget rendering, separating concerns from `_repr_html_()`.
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Contact Us
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----------
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‎bigframes/dataframe.py‎

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Original file line numberDiff line numberDiff line change
@@ -831,10 +831,12 @@ def _repr_html_(self) -> str:
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"""
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opts = bigframes.options.display
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max_results = opts.max_rows
834-
if opts.repr_mode == "deferred":
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# For anywidget mode, return deferred representation
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# The actual widget display is handled by _ipython_display_()
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if opts.repr_mode in ("deferred", "anywidget"):
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return formatter.repr_query_job(self._compute_dry_run())
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837-
# Process blob columns first, regardless of display mode
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# Process blob columns first for non-deferred modes
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self._cached()
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df = self.copy()
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if bigframes.options.display.blob_display:
@@ -849,29 +851,6 @@ def _repr_html_(self) -> str:
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else:
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blob_cols = []
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852-
if opts.repr_mode == "anywidget":
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try:
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from IPython.display import display as ipython_display
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from bigframes import display
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# Always create a new widget instance for each display call
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# This ensures that each cell gets its own widget and prevents
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# unintended sharing between cells
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widget = display.TableWidget(df.copy())
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ipython_display(widget)
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return "" # Return empty string since we used display()
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except (AttributeError, ValueError, ImportError):
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# Fallback if anywidget is not available
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warnings.warn(
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"Anywidget mode is not available. "
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"Please `pip install anywidget traitlets` or `pip install 'bigframes[anywidget]'` to use interactive tables. "
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f"Falling back to deferred mode. Error: {traceback.format_exc()}"
872-
)
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return formatter.repr_query_job(self._compute_dry_run())
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875854
# Continue with regular HTML rendering for non-anywidget modes
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# TODO(swast): pass max_columns and get the true column count back. Maybe
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# get 1 more column than we have requested so that pandas can add the
@@ -931,6 +910,55 @@ def obj_ref_rt_to_html(obj_ref_rt) -> str:
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html_string += f"[{row_count} rows x {column_count} columns in total]"
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return html_string
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913+
def _ipython_display_(self):
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"""
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Custom display method for IPython/Jupyter environments.
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This is called by IPython's display system when the object is displayed.
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"""
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opts = bigframes.options.display
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# Only handle widget display in anywidget mode
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if opts.repr_mode == "anywidget":
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try:
923+
from bigframes import display
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# Process blob columns if needed
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self._cached()
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df = self.copy()
928+
if bigframes.options.display.blob_display:
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blob_cols = [
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series_name
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for series_name, series in df.items()
932+
if series.dtype == bigframes.dtypes.OBJ_REF_DTYPE
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]
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for col in blob_cols:
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df[col] = df[col].blob._get_runtime(
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mode="R", with_metadata=True
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)
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# Create and display the widget
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widget = display.TableWidget(df)
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# IPython will automatically display the widget
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# since we're returning it from _ipython_display_()
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from IPython.display import display as ipython_display
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ipython_display(widget)
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return # Important: return None to signal we handled display
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949+
except (AttributeError, ValueError, ImportError):
950+
# Fallback: let IPython use _repr_html_() instead
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warnings.warn(
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"Anywidget mode is not available. "
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"Please `pip install anywidget traitlets` or `pip install 'bigframes[anywidget]'` to use interactive tables. "
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f"Falling back to deferred mode. Error: {traceback.format_exc()}"
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)
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# Don't return anything - let IPython fall back to _repr_html_()
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return
958+
959+
# For other modes, don't handle display - let IPython use _repr_html_()
960+
return
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934962
def __delitem__(self, key: str):
935963
df = self.drop(columns=[key])
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self._set_block(df._get_block())

‎notebooks/dataframes/anywidget_mode.ipynb‎

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"execution_count": 2,
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"id": "ca22f059",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/usr/local/google/home/shuowei/src/python-bigquery-dataframes/venv/lib/python3.10/site-packages/google/api_core/_python_version_support.py:266: FutureWarning: You are using a Python version (3.10.15) which Google will stop supporting in new releases of google.api_core once it reaches its end of life (2026-10-04). Please upgrade to the latest Python version, or at least Python 3.11, to continue receiving updates for google.api_core past that date.\n",
44-
" warnings.warn(message, FutureWarning)\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"import bigframes.pandas as bpd"
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]
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "47795eaa10f149aeb99574232c0936eb",
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"model_id": "4cc789c49be246bb94967e625986900a",
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"version_major": 2,
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"version_minor": 1
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},
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/html": [],
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"text/plain": [
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"Computation deferred. Computation will process 171.4 MB"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "8354ce0f82d3495a9b630dfc362f73ee",
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"model_id": "b1089664ce03440d81ef206da1210229",
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"version_major": 2,
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"version_minor": 1
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},
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{
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"data": {
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"text/html": [
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"\n",
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" Query started with request ID bigframes-dev:US.c45952fb-01b4-409c-9da4-f7c5bfc0d47d.<details><summary>SQL</summary><pre>SELECT\n",
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"`state` AS `state`,\n",
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"`gender` AS `gender`,\n",
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"`year` AS `year`,\n",
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"`name` AS `name`,\n",
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"`number` AS `number`\n",
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"FROM\n",
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"(SELECT\n",
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" *\n",
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"FROM (\n",
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" SELECT\n",
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" `state`,\n",
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" `gender`,\n",
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" `year`,\n",
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" `name`,\n",
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" `number`\n",
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" FROM `bigquery-public-data.usa_names.usa_1910_2013` FOR SYSTEM_TIME AS OF TIMESTAMP(&#x27;2025-10-30T21:48:48.979701+00:00&#x27;)\n",
314-
") AS `t0`)\n",
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"ORDER BY `name` ASC NULLS LAST ,`year` ASC NULLS LAST ,`state` ASC NULLS LAST\n",
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"LIMIT 5</pre></details>\n",
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"✅ Completed. \n",
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" Query processed 171.4 MB in a moment of slot time.\n",
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" "
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],
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"text/plain": [
@@ -333,7 +294,7 @@
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "59461286a17d4a42b6be6d9d9c7bf7e3",
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"model_id": "f18925fc13304fb2ae34056f2cb1c68b",
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"version_major": 2,
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"version_minor": 1
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},
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"data": {
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"text/html": [
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"✅ Completed. \n",
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" Query processed 85.9 kB in 14 seconds of slot time.\n",
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" Query processed 85.9 kB in 15 seconds of slot time.\n",
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" "
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],
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"text/plain": [
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/usr/local/google/home/shuowei/src/python-bigquery-dataframes/bigframes/dtypes.py:969: JSONDtypeWarning: JSON columns will be represented as pandas.ArrowDtype(pyarrow.json_())\n",
351+
"/usr/local/google/home/shuowei/src/python-bigquery-dataframes/bigframes/dtypes.py:987: JSONDtypeWarning: JSON columns will be represented as pandas.ArrowDtype(pyarrow.json_())\n",
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"instead of using `db_dtypes` in the future when available in pandas\n",
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"(https://github.com/pandas-dev/pandas/issues/60958) and pyarrow.\n",
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" warnings.warn(msg, bigframes.exceptions.JSONDtypeWarning)\n"
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "d1794b42579542a8980bd158e521bd3e",
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"model_id": "2335d3161b704a6da85165dbebf5ca0f",
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"version_major": 2,
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"version_minor": 1
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},
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/html": [],
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"text/plain": [
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"Computation deferred. Computation will process 0 Bytes"
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]
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},
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"execution_count": 10,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [

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