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docs: add notebooks to user guide page (#2505)
Thank you for opening a Pull Request! Before submitting your PR, there are a few things you can do to make sure it goes smoothly: - [ ] Make sure to open an issue as a [bug/issue](https://github.com/googleapis/python-bigquery-dataframes/issues/new/choose) before writing your code! That way we can discuss the change, evaluate designs, and agree on the general idea - [ ] Ensure the tests and linter pass - [ ] Code coverage does not decrease (if any source code was changed) - [ ] Appropriate docs were updated (if necessary) Fixes #<issue_number_goes_here> 🦕
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‎.github/workflows/docs.yml‎

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run: |
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python -m pip install --upgrade setuptools pip wheel
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python -m pip install nox
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- name: Run docfx
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run: |
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nox -s docfx

‎docs/conf.py‎

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"sphinx.ext.todo",
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"sphinx.ext.viewcode",
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"sphinx_sitemap",
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"myst_parser",
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"myst_nb",
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]
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# myst-nb configuration
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nb_execution_mode = "off"
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# autodoc/autosummary flags
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autoclass_content = "both"
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autodoc_default_options = {"members": True}
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suppress_warnings = [
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# Allow unknown mimetype so we can use widgets in tutorial notebooks.
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"mystnb.unknown_mime_type",
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# Temporarily suppress this to avoid "more than one target found for
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# cross-reference" warning, which are intractable for us to avoid while in
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# a mono-repo.
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# See https://github.com/sphinx-doc/sphinx/blob
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# /2a65ffeef5c107c19084fabdd706cdff3f52d93c/sphinx/domains/python.py#L843
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"ref.python"
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"ref.python",
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]
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# -- Options for LaTeX output ---------------------------------------------

‎docs/notebooks‎

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../notebooks

‎docs/user_guide/index.rst‎

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Getting Started <https://docs.cloud.google.com/bigquery/docs/dataframes-quickstart>
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Cloud Docs User Guides <https://docs.cloud.google.com/bigquery/docs/bigquery-dataframes-introduction>
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.. toctree::
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:caption: Getting Started
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:maxdepth: 1
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Quickstart Template <../notebooks/getting_started/bq_dataframes_template.ipynb>
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Getting Started <../notebooks/getting_started/getting_started_bq_dataframes.ipynb>
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Magics <../notebooks/getting_started/magics.ipynb>
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ML Fundamentals <../notebooks/getting_started/ml_fundamentals_bq_dataframes.ipynb>
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.. toctree::
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:caption: DataFrames
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:maxdepth: 1
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Anywidget Mode <../notebooks/dataframes/anywidget_mode.ipynb>
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Dataframe <../notebooks/dataframes/dataframe.ipynb>
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Index Col Null <../notebooks/dataframes/index_col_null.ipynb>
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Integrations <../notebooks/dataframes/integrations.ipynb>
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Pypi <../notebooks/dataframes/pypi.ipynb>
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.. toctree::
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:caption: Data Types
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:maxdepth: 1
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Array <../notebooks/data_types/array.ipynb>
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Json <../notebooks/data_types/json.ipynb>
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Struct <../notebooks/data_types/struct.ipynb>
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Timedelta <../notebooks/data_types/timedelta.ipynb>
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.. toctree::
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:caption: Generative AI
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:maxdepth: 1
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AI Functions <../notebooks/generative_ai/ai_functions.ipynb>
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AI Forecast <../notebooks/generative_ai/bq_dataframes_ai_forecast.ipynb>
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LLM Code Generation <../notebooks/generative_ai/bq_dataframes_llm_code_generation.ipynb>
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LLM KMeans <../notebooks/generative_ai/bq_dataframes_llm_kmeans.ipynb>
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LLM Output Schema <../notebooks/generative_ai/bq_dataframes_llm_output_schema.ipynb>
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LLM Vector Search <../notebooks/generative_ai/bq_dataframes_llm_vector_search.ipynb>
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Drug Name Generation <../notebooks/generative_ai/bq_dataframes_ml_drug_name_generation.ipynb>
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Large Language Models <../notebooks/generative_ai/large_language_models.ipynb>
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.. toctree::
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:caption: Machine Learning
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:maxdepth: 1
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ML Cross Validation <../notebooks/ml/bq_dataframes_ml_cross_validation.ipynb>
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Linear Regression <../notebooks/ml/bq_dataframes_ml_linear_regression.ipynb>
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Linear Regression BBQ <../notebooks/ml/bq_dataframes_ml_linear_regression_bbq.ipynb>
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Linear Regression Big <../notebooks/ml/bq_dataframes_ml_linear_regression_big.ipynb>
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Easy Linear Regression <../notebooks/ml/easy_linear_regression.ipynb>
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Sklearn Linear Regression <../notebooks/ml/sklearn_linear_regression.ipynb>
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Timeseries Analysis <../notebooks/ml/timeseries_analysis.ipynb>
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.. toctree::
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:caption: Visualization
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:maxdepth: 1
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COVID Line Graphs <../notebooks/visualization/bq_dataframes_covid_line_graphs.ipynb>
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Tutorial <../notebooks/visualization/tutorial.ipynb>
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.. toctree::
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:caption: Geospatial Data
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:maxdepth: 1
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Geoseries <../notebooks/geo/geoseries.ipynb>
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.. toctree::
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:caption: Regionalized BigQuery
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:maxdepth: 1
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Regionalized <../notebooks/location/regionalized.ipynb>
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.. toctree::
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:caption: Multimodal
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:maxdepth: 1
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Multimodal Dataframe <../notebooks/multimodal/multimodal_dataframe.ipynb>
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.. toctree::
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:caption: Remote Functions
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:maxdepth: 1
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Remote Function <../notebooks/remote_functions/remote_function.ipynb>
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Remote Function Usecases <../notebooks/remote_functions/remote_function_usecases.ipynb>
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Remote Function Vertex Claude Model <../notebooks/remote_functions/remote_function_vertex_claude_model.ipynb>
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.. toctree::
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:caption: Streaming
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:maxdepth: 1
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Streaming Dataframe <../notebooks/streaming/streaming_dataframe.ipynb>
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.. toctree::
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:caption: Experimental
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:maxdepth: 1
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AI Operators <../notebooks/experimental/ai_operators.ipynb>
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Semantic Operators <../notebooks/experimental/semantic_operators.ipynb>
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.. toctree::
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:caption: Apps
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:maxdepth: 1
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Synthetic Data Generation <../notebooks/apps/synthetic_data_generation.ipynb>
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.. toctree::
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:caption: Kaggle
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:maxdepth: 1
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AI Forecast <../notebooks/kaggle/bq_dataframes_ai_forecast.ipynb>
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Describe Product Images <../notebooks/kaggle/describe-product-images-with-bigframes-multimodal.ipynb>
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Vector Search Over National Jukebox <../notebooks/kaggle/vector-search-with-bigframes-over-national-jukebox.ipynb>

‎notebooks/dataframes/dataframe.ipynb‎

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"id": "13861abc-120c-4db6-ad0c-e414b85d3443",
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"metadata": {},
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"source": [
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"### Select a subset of the DF"
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"## Select a subset of the DF"
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},
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{

‎notebooks/dataframes/index_col_null.ipynb‎

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"id": "13861abc-120c-4db6-ad0c-e414b85d3443",
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"metadata": {},
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"source": [
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"### Select a subset of the DataFrame\n",
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"## Select a subset of the DataFrame",
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"\n",
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"Filter columns by selecting a list of columns from the DataFrame.\n",
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‎notebooks/experimental/ai_operators.ipynb‎

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"cells": [
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{
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"cell_type": "markdown",
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"id": "title-cell",
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"metadata": {},
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"source": [
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"# AI Operators (Experimental)"
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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": 1,

‎notebooks/experimental/semantic_operators.ipynb‎

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"cells": [
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"cell_type": "markdown",
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"id": "title-cell",
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"metadata": {},
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"source": [
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"# Semantic Operators (Experimental)"
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]
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},
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"cell_type": "code",
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"execution_count": null,

‎notebooks/generative_ai/bq_dataframes_ai_forecast.ipynb‎

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"source": [
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"### Setup"
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"## Setup"
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},
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{

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