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Integrations: Harmonize pages about data frames (pandas, Dask, Polars)
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‎docs/integrate/dask/index.md‎

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(dask)=
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# Dask
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:::{div} .float-right .text-right
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[![Dask logo](https://github.com/crate/crate-clients-tools/assets/453543/99bd2234-c501-479b-ade7-bcc2bfc1f288){height=60px loading=lazy}][Dask]
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<br>
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[![Dask CI](https://img.shields.io/github/actions/workflow/status/crate/cratedb-examples/dataframe-dask.yml?branch=main)](https://github.com/crate/cratedb-examples/actions/workflows/dataframe-dask.yml)
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:::
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:::{div} .clearfix
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:::
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:::{rubric} About
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:::
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[Dask] is a parallel computing library for analytics with task scheduling.
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It is built on top of the Python programming language, making it easy to scale
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the Python libraries that you know and love, like NumPy, pandas, and scikit-learn.
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```{div}
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:style: "float: right"
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[![](https://github.com/crate/crate-clients-tools/assets/453543/99bd2234-c501-479b-ade7-bcc2bfc1f288){w=180px}](https://www.dask.org/)
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```
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- [Dask DataFrames] help you process large tabular data by parallelizing pandas,
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either on your laptop for larger-than-memory computing, or on a distributed
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cluster of computers.
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generic Python workflows across a Dask cluster with minimal code changes,
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by extending Python's `concurrent.futures` interface.
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```{div}
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:style: "clear: both"
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:::{rubric} Install
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:::
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```shell
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pip install 'dask[dataframe]' 'sqlalchemy-cratedb'
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```
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:::{rubric} Learn
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:::{rubric} Synopsis
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:::
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Write Dask dataframe to CrateDB.
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`example.py`
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```python
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import dask.dataframe as dd
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from sqlalchemy_cratedb import insert_bulk
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CRATEDB_URI = "crate://crate:crate@localhost:4200"
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TABLE_NAME = "example"
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df = makeTimeDataFrame(rows=500_000, freq="s")
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ddf = dd.from_pandas(df, npartitions=4)
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ddf.to_sql(
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TABLE_NAME,
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uri=CRATEDB_URI,
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index=False,
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if_exists="replace",
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chunksize=20_000,
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parallel=True,
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method=insert_bulk,
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)
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```
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:::{rubric} Quickstart example
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:::
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Create the file `example.py` including the synopsis code shared above.
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Complete the example by using the `makeTimeDataFrame()` function.
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:::{literalinclude} ../pandas/makeTimeDataFrame.py
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:::
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:::{include} /connect/_cratedb.md
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:::
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```shell
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pip install 'dask[dataframe]' 'sqlalchemy-cratedb'
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python example.py
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```
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:::{rubric} Full example
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:::
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:::{card}
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:link: https://github.com/crate/cratedb-examples/tree/main/by-dataframe/dask
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:link-type: url
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{material-regular}`play_arrow;2em`
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Connect to CrateDB and CrateDB Cloud using Dask.
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Includes basic examples of how to use Dask with CrateDB.
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:::
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:::{rubric} Guides
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:::
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- {ref}`dask-usage`
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- [Efficient batch/bulk INSERT operations with pandas, Dask, and SQLAlchemy]
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:::{rubric} Related sections
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:::
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- {ref}`Efficient batch/bulk INSERT operations for pandas, Dask, and Polars <sqlalchemy-cratedb:dataframe>`
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- {ref}`arrow-import-parquet`
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- [Import weather data using Dask]
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- [Dask code examples]
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:::{toctree}
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[Dask]: https://www.dask.org/
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[Dask code examples]: https://github.com/crate/cratedb-examples/tree/main/by-dataframe/dask
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[Dask DataFrames]: https://docs.dask.org/en/latest/dataframe.html
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[Dask Futures]: https://docs.dask.org/en/latest/futures.html
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[Efficient batch/bulk INSERT operations with pandas, Dask, and SQLAlchemy]: https://cratedb.com/docs/python/en/latest/by-example/sqlalchemy/dataframe.html
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[Import weather data using Dask]: https://github.com/crate/cratedb-examples/blob/main/topic/timeseries/dask-weather-data-import.ipynb

‎docs/integrate/pandas/efficient-ingest.md‎

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(pandas-bulk-import)=
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(pandas-efficient-import)=
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# Efficient bulk imports with pandas
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## Introduction

‎docs/integrate/pandas/index.md‎

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(pandas)=
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# pandas
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```{div}
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:style: "float: right"
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[![](https://pandas.pydata.org/static/img/pandas.svg){w=180px}](https://pandas.pydata.org/)
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```
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```{div} .clearfix
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```
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:::{div} .float-right .text-right
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[![pandas logo](https://pandas.pydata.org/static/img/pandas.svg){height=60px loading=lazy}][pandas]
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<br>
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[![pandas CI](https://img.shields.io/github/actions/workflow/status/crate/cratedb-examples/dataframe-pandas.yml?branch=main)](https://github.com/crate/cratedb-examples/actions/workflows/dataframe-pandas.yml)
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:::
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:::{div} .clearfix
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:::
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:::{rubric} About
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:::
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and manipulation tool, built on top of the Python programming language. It offers
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data structures and operations for manipulating numerical tables and time series.
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:::{rubric} Data Model
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Pandas is built around data structures called Series and DataFrames. Data for these
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collections can be imported from various file formats such as comma-separated values,
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JSON, Parquet, SQL database tables or queries, and Microsoft Excel.
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A Series is a 1-dimensional data structure built on top of NumPy's array.
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:::{rubric} Install
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:::
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- Pandas is built around data structures called Series and DataFrames. Data for these
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collections can be imported from various file formats such as comma-separated values,
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JSON, Parquet, SQL database tables or queries, and Microsoft Excel.
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- A Series is a 1-dimensional data structure built on top of NumPy's array.
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- Pandas includes support for time series, such as the ability to interpolate values
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and filter using a range of timestamps.
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- By default, a Pandas index is a series of integers ascending from 0, similar to the
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indices of Python arrays. However, indices can use any NumPy data type, including
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floating point, timestamps, or strings.
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- Pandas supports hierarchical indices with multiple values per data point. An index
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with this structure, called a "MultiIndex", allows a single DataFrame to represent
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multiple dimensions, similar to a pivot table in Microsoft Excel. Each level of a
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MultiIndex can be given a unique name.
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```shell
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pip install pandas sqlalchemy-cratedb
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```
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:::{rubric} Learn
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:::{rubric} Synopsis
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:::
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Write pandas dataframe to CrateDB.
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`example.py`
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```python
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import sqlalchemy as sa
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from sqlalchemy_cratedb import insert_bulk
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CRATEDB_URI = "crate://crate:crate@localhost:4200"
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TABLE_NAME = "example"
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df = makeTimeDataFrame(rows=500_000, freq="s")
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engine = sa.create_engine(CRATEDB_URI)
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df.to_sql(
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name=TABLE_NAME,
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con=engine,
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if_exists="replace",
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index=False,
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chunksize=20_000,
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method=insert_bulk,
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)
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```
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:::{rubric} Quickstart example
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:::
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Create the file `example.py` including the synopsis code shared above.
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Complete the example by using the `makeTimeDataFrame()` function.
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:::{literalinclude} ../pandas/makeTimeDataFrame.py
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:::
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:::{include} /connect/_cratedb.md
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:::
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```shell
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pip install pandas sqlalchemy-cratedb
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python example.py
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```
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:::{rubric} Full example
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:::
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:::{card}
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:link: https://github.com/crate/cratedb-examples/tree/main/by-dataframe/pandas
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:link-type: url
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{material-regular}`play_arrow;2em`
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Connect to CrateDB and CrateDB Cloud using pandas.
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+++
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Includes basic examples of how to use pandas with CrateDB.
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:::
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:::{rubric} Guides
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:::
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- {ref}`pandas-efficient-import`
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- {ref}`pandas-tutorial-start`
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- {ref}`pandas-tutorial-jupyter`
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- {ref}`arrow-import-parquet`
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- {ref}`pandas-bulk-import`
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- See also: {ref}`dask-bulk-import`
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- See also: [Efficient batch/bulk INSERT operations with pandas, Dask, and SQLAlchemy]
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:::{rubric} Code examples
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:::{rubric} Related sections
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:::
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- [pandas code examples]
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- {ref}`Efficient batch/bulk INSERT operations for pandas, Dask, and Polars <sqlalchemy-cratedb:dataframe>`
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- {ref}`arrow-import-parquet`
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:::{toctree}
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:::
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[Efficient batch/bulk INSERT operations with pandas, Dask, and SQLAlchemy]: https://cratedb.com/docs/python/en/latest/by-example/sqlalchemy/dataframe.html
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[pandas]: https://pandas.pydata.org/
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[pandas code examples]: https://github.com/crate/cratedb-examples/tree/main/by-dataframe/pandas

‎docs/integrate/polars/index.md‎

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(polars)=
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# Polars
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```{div}
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:style: "float: right; margin-left: 0.5em"
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[![Polars logo](https://github.com/pola-rs/polars-static/raw/master/logos/polars-logo-dark.svg){w=180px}][Polars]
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```
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```{div} .clearfix
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```
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:::{div} .float-right .text-right
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[![Polars logo](https://github.com/pola-rs/polars-static/raw/master/logos/polars-logo-dark.svg){height=60px loading=lazy}][Polars]
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<br>
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[![Polars CI](https://img.shields.io/github/actions/workflow/status/crate/cratedb-examples/dataframe-polars.yml?branch=main)](https://github.com/crate/cratedb-examples/actions/workflows/dataframe-polars.yml)
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:::
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:::{div} .clearfix
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:::
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:::{rubric} About
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:::
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Rust, Python, Node.js, and R, plus a SQL context. It is powered by a
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multithreaded, vectorized query engine and written in Rust.
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:::{dropdown} Features and data formats
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Polars is an open-source library for data manipulation, known for being
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one of the fastest data processing solutions on a single machine.
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It features a well-structured, typed API that is both expressive and
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easy to use.
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- **Fast:** Written from scratch in Rust and with performance in mind,
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designed close to the machine, and without external dependencies.
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- **Out of Core:** The streaming API allows you to process your results without
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requiring all your data to be in memory at the same time.
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- **Parallel:** Polars' multi-threaded query engine utilizes the power of your
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- **Parallel:** Polars' multithreaded query engine utilizes the power of your
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machine by dividing the workload among the available CPU cores without any
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additional configuration.
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community of developers. Everyone is encouraged to add new features and contribute.
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It is free to use under the MIT license.
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:::{rubric} Data formats
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Polars supports reading and writing to many common data formats.
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This allows you to easily integrate Polars into your existing data stack.
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- Databases: MySQL, PostgreSQL, SQLite, Redshift, SQL Server, etc. (via ConnectorX)
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- Cloud storage: Amazon S3, Azure Blob/ADLS (via fsspec‑compatible backends)
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:::{rubric} Learn
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- [Polars code examples]
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:::{rubric} Install
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:::
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```shell
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pip install 'polars[pyarrow]' sqlalchemy-cratedb
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```
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:::{rubric} Synopsis
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:::
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Write Polars dataframe to CrateDB.
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`example.py`
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```python
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import polars as pl
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import sqlalchemy as sa
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from sqlalchemy_cratedb import insert_bulk
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CRATEDB_URI = "crate://crate:crate@localhost:4200"
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TABLE_NAME = "example"
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df = pl.from_pandas(makeTimeDataFrame(rows=500_000, freq="s"))
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engine = sa.create_engine(CRATEDB_URI)
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df.write_database(
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engine="sqlalchemy",
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connection=engine,
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table_name=TABLE_NAME,
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if_table_exists="replace",
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engine_options={
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"method": insert_bulk,
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"chunksize": 20_000,
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},
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)
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```
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:::{rubric} Quickstart example
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:::
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Create the file `example.py` including the synopsis code shared above.
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Complete the example by using the `makeTimeDataFrame()` function.
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:::{literalinclude} ../pandas/makeTimeDataFrame.py
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:::
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:::{include} /connect/_cratedb.md
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:::
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```shell
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pip install 'polars[pyarrow]' sqlalchemy-cratedb pandas
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python example.py
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```
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:::{rubric} Full example
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:::
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:::{card}
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:link: https://github.com/crate/cratedb-examples/tree/main/by-dataframe/polars
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:link-type: url
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{material-regular}`play_arrow;2em`
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Connect to CrateDB and CrateDB Cloud using Polars.
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Includes basic examples of how to use Polars with CrateDB.
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:::
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[Apache Arrow]: https://arrow.apache.org/
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[Polars]: https://pola.rs/
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[Polars code examples]: https://github.com/crate/cratedb-examples/tree/main/by-dataframe/polars

‎docs/integrate/status.md‎

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<img src="https://img.shields.io/github/actions/workflow/status/crate/cratedb-examples/dataframe-dask.yml?branch=main&label=Dask" loading="lazy"></a>
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<a href="https://github.com/crate/cratedb-examples/actions/workflows/dataframe-pandas.yml" target="_blank">
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<img src="https://img.shields.io/github/actions/workflow/status/crate/cratedb-examples/dataframe-pandas.yml?branch=main&label=pandas" loading="lazy"></a>
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<a href="https://github.com/crate/cratedb-examples/actions/workflows/dataframe-polars.yml" target="_blank">
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<img src="https://img.shields.io/github/actions/workflow/status/crate/cratedb-examples/dataframe-polars.yml?branch=main&label=Polars" loading="lazy"></a>
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</td>
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</tr>
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