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notebook and testcase updates
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‎bigframes/display/anywidget.py‎

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@@ -227,7 +227,7 @@ def _set_table_html(self) -> None:
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break
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# Get the data for the current page
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page_data = cached_data[start:end]
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page_data = cached_data.iloc[start:end]
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# Generate HTML table
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self.table_html = bigframes.display.html.render_html(

‎bigframes/display/html.py‎

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@@ -46,12 +46,16 @@ def render_html(
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# Add index headers
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for name in dataframe.index.names:
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table_html.append(
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f' <th style="text-align: left;"><div style="resize: horizontal; overflow: auto; box-sizing: border-box; width: 100%; height: 100%; padding: 0.5em;">{html.escape(str(name))}</div></th>'
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f' <th style="text-align: left;">'
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f'<div style="resize: horizontal; overflow: auto; box-sizing: border-box; width: 100%; height: 100%; padding: 0.5em;">'
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f"{html.escape(str(name))}</div></th>"
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)
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for col in dataframe.columns:
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table_html.append(
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f' <th style="text-align: left;"><div style="resize: horizontal; overflow: auto; box-sizing: border-box; width: 100%; height: 100%; padding: 0.5em;">{html.escape(str(col))}</div></th>'
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f' <th style="text-align: left;">'
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f'<div style="resize: horizontal; overflow: auto; box-sizing: border-box; width: 100%; height: 100%; padding: 0.5em;">'
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f"{html.escape(str(col))}</div></th>"
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)
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table_html.append(" </tr>")
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table_html.append(" </thead>")

‎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",
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" 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": "e2e84120b65f4a6d8bff7c7bfbf813e8",
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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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"application/vnd.jupyter.widget-view+json": {
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"model_id": "8354ce0f82d3495a9b630dfc362f73ee",
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"model_id": "fc58c5471b8f444b85e35dd5b93a17ca",
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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",
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") 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 2 seconds of slot time.\n",
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" "
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],
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"text/plain": [
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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": "16ef1baae8934457a4d9e032262cac54",
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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 16 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",
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"/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": "42d656c30fc34855a8fa5e398de412eb",
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"version_major": 2,
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"version_minor": 1
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},
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" LIMIT 5;\n",
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"\"\"\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "multi-index-display-markdown",
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"metadata": {},
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"source": [
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"## Display Multi-Index DataFrame in anywidget mode\n",
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"This section demonstrates how BigFrames can display a DataFrame with multiple levels of indexing (a \"multi-index\") when using the `anywidget` display mode."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"id": "ad7482aa",
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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/bigframes/session/_io/bigquery/read_gbq_table.py:483: DefaultIndexWarning: Table 'bigquery-public-data.pypi.file_downloads' is clustered and/or\n",
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"partitioned, but BigQuery DataFrames was not able to find a suitable\n",
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"index. To avoid this warning, set at least one of: `index_col` or\n",
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"`filters`.\n",
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" warnings.warn(msg, category=bfe.DefaultIndexWarning)\n"
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]
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},
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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 469.9 GB in 55 minutes of slot time. [<a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=bigframes-dev&j=bq:US:27cb1a99-ccf8-4237-b5a6-65cc1a63f6ee&page=queryresults\">Job bigframes-dev:US.27cb1a99-ccf8-4237-b5a6-65cc1a63f6ee details</a>]\n",
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" "
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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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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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"✅ Completed. \n",
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" Query processed 110.6 MB in 6 seconds of slot time. [<a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=bigframes-dev&j=bq:US:job_Fu0VQ9m67G-hhsbXQne-UAqqJddU&page=queryresults\">Job bigframes-dev:US.job_Fu0VQ9m67G-hhsbXQne-UAqqJddU details</a>]\n",
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" "
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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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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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"application/vnd.jupyter.widget-view+json": {
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"model_id": "e11b0c862f8d45b5aa68487678d1d2d9",
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"version_major": 2,
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"version_minor": 1
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},
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"text/plain": [
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"TableWidget(page_size=10, row_count=3534819, table_html='<table border=\"1\" class=\"dataframe table table-stripe…"
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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 506.0 GB"
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]
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},
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"execution_count": 11,
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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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"import datetime\n",
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"\n",
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" # Read the PyPI downloads dataset\n",
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"pypi_df = bpd.read_gbq(\"bigquery-public-data.pypi.file_downloads\")\n",
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"\n",
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"# Filter for the last 7 days to reduce the data size for this example\n",
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"seven_days_ago = datetime.datetime.now(datetime.timezone.utc) - datetime.timedelta(days=7)\n",
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"pypi_df_recent = pypi_df[pypi_df[\"timestamp\"] > seven_days_ago]\n",
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" \n",
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"# Create a multi-index by grouping by date and project\n",
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"pypi_df_recent['date'] = pypi_df_recent['timestamp'].dt.date\n",
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"multi_index_df = pypi_df_recent.groupby([\"date\", \"project\"]).size().to_frame(\"downloads\")\n",
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" \n",
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"# Display the DataFrame with the multi-index\n",
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"multi_index_df"
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]
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}
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],
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"metadata": {

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