Why Ibis?

Ibis defines a Python dataframe API that executes on any query engine – the frontend for any backend data platform, with nearly 20 backends today. This allows Ibis to have excellent performance – as good as the backend it is connected to – with a consistent user experience.

What is Ibis?

Ibis is the portable Python dataframe library.

We can demonstrate this with a simple example on a few local query engines:

import ibis

ibis.options.interactive = True

con = ibis.connect("duckdb://")

t = con.read_parquet("penguins.parquet")
t.limit(3)

1: Change only your connection to switch between backends.

┏━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━┓
┃ species ┃ island    ┃ bill_length_mm ┃ bill_depth_mm ┃ flipper_length_mm ┃ body_mass_g ┃ sex    ┃ year  ┃
┡━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━┩
│ string  │ string    │ float64        │ float64       │ int64             │ int64       │ string │ int64 │
├─────────┼───────────┼────────────────┼───────────────┼───────────────────┼─────────────┼────────┼───────┤
│ Adelie  │ Torgersen │           39.1 │          18.7 │               181 │        3750 │ male   │  2007 │
│ Adelie  │ Torgersen │           39.5 │          17.4 │               186 │        3800 │ female │  2007 │
│ Adelie  │ Torgersen │           40.3 │          18.0 │               195 │        3250 │ female │  2007 │
└─────────┴───────────┴────────────────┴───────────────┴───────────────────┴─────────────┴────────┴───────┘

t.group_by(["species", "island"]).agg(count=t.count()).order_by("count")

┏━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━┓
┃ species   ┃ island    ┃ count ┃
┡━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━┩
│ string    │ string    │ int64 │
├───────────┼───────────┼───────┤
│ Adelie    │ Biscoe    │    44 │
│ Adelie    │ Torgersen │    52 │
│ Adelie    │ Dream     │    56 │
│ Chinstrap │ Dream     │    68 │
│ Gentoo    │ Biscoe    │   124 │
└───────────┴───────────┴───────┘

con = ibis.connect("polars://")

t = con.read_parquet("penguins.parquet")
t.limit(3)

1: Change only your connection to switch between backends.

┏━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━┓
┃ species ┃ island    ┃ bill_length_mm ┃ bill_depth_mm ┃ flipper_length_mm ┃ body_mass_g ┃ sex    ┃ year  ┃
┡━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━┩
│ string  │ string    │ float64        │ float64       │ int64             │ int64       │ string │ int64 │
├─────────┼───────────┼────────────────┼───────────────┼───────────────────┼─────────────┼────────┼───────┤
│ Adelie  │ Torgersen │           39.1 │          18.7 │               181 │        3750 │ male   │  2007 │
│ Adelie  │ Torgersen │           39.5 │          17.4 │               186 │        3800 │ female │  2007 │
│ Adelie  │ Torgersen │           40.3 │          18.0 │               195 │        3250 │ female │  2007 │
└─────────┴───────────┴────────────────┴───────────────┴───────────────────┴─────────────┴────────┴───────┘

t.group_by(["species", "island"]).agg(count=t.count()).order_by("count")

┏━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━┓
┃ species   ┃ island    ┃ count ┃
┡━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━┩
│ string    │ string    │ int64 │
├───────────┼───────────┼───────┤
│ Adelie    │ Biscoe    │    44 │
│ Adelie    │ Torgersen │    52 │
│ Adelie    │ Dream     │    56 │
│ Chinstrap │ Dream     │    68 │
│ Gentoo    │ Biscoe    │   124 │
└───────────┴───────────┴───────┘

con = ibis.connect("datafusion://")

t = con.read_parquet("penguins.parquet")
t.limit(3)

1: Change only your connection to switch between backends.

┏━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━┓
┃ species ┃ island    ┃ bill_length_mm ┃ bill_depth_mm ┃ flipper_length_mm ┃ body_mass_g ┃ sex    ┃ year  ┃
┡━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━┩
│ string  │ string    │ float64        │ float64       │ int64             │ int64       │ string │ int64 │
├─────────┼───────────┼────────────────┼───────────────┼───────────────────┼─────────────┼────────┼───────┤
│ Adelie  │ Torgersen │           39.1 │          18.7 │               181 │        3750 │ male   │  2007 │
│ Adelie  │ Torgersen │           39.5 │          17.4 │               186 │        3800 │ female │  2007 │
│ Adelie  │ Torgersen │           40.3 │          18.0 │               195 │        3250 │ female │  2007 │
└─────────┴───────────┴────────────────┴───────────────┴───────────────────┴─────────────┴────────┴───────┘

t.group_by(["species", "island"]).agg(count=t.count()).order_by("count")

┏━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━┓
┃ species   ┃ island    ┃ count ┃
┡━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━┩
│ string    │ string    │ int64 │
├───────────┼───────────┼───────┤
│ Adelie    │ Biscoe    │    44 │
│ Adelie    │ Torgersen │    52 │
│ Adelie    │ Dream     │    56 │
│ Chinstrap │ Dream     │    68 │
│ Gentoo    │ Biscoe    │   124 │
└───────────┴───────────┴───────┘

con = ibis.connect("pyspark://")

t = con.read_parquet("penguins.parquet")
t.limit(3)

1: Change only your connection to switch between backends.

┏━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━┓
┃ species ┃ island    ┃ bill_length_mm ┃ bill_depth_mm ┃ flipper_length_mm ┃ body_mass_g ┃ sex    ┃ year  ┃
┡━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━┩
│ string  │ string    │ float64        │ float64       │ int64             │ int64       │ string │ int64 │
├─────────┼───────────┼────────────────┼───────────────┼───────────────────┼─────────────┼────────┼───────┤
│ Adelie  │ Torgersen │           39.1 │          18.7 │               181 │        3750 │ male   │  2007 │
│ Adelie  │ Torgersen │           39.5 │          17.4 │               186 │        3800 │ female │  2007 │
│ Adelie  │ Torgersen │           40.3 │          18.0 │               195 │        3250 │ female │  2007 │
└─────────┴───────────┴────────────────┴───────────────┴───────────────────┴─────────────┴────────┴───────┘

t.group_by(["species", "island"]).agg(count=t.count()).order_by("count")

┏━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━┓
┃ species   ┃ island    ┃ count ┃
┡━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━┩
│ string    │ string    │ int64 │
├───────────┼───────────┼───────┤
│ Adelie    │ Biscoe    │    44 │
│ Adelie    │ Torgersen │    52 │
│ Adelie    │ Dream     │    56 │
│ Chinstrap │ Dream     │    68 │
│ Gentoo    │ Biscoe    │   124 │
└───────────┴───────────┴───────┘

Who is Ibis for?

Ibis is for data engineers, data analysts, and data scientists (or any title that needs to work with data!) to use directly with their data platform(s) of choice. It also has benefits for data platforms, organizations, and library developers.

Ibis for practitioners

You can use Ibis at any stage of your data workflow, no matter your role.

Data engineers can use Ibis to:

write and maintain complex ETL/ELT jobs
replace fragile SQL string pipelines with a robust Python API
replace PySpark with a more Pythonic API that supports Spark and many other backends

Data analysts can use Ibis to:

use Ibis interactive mode for rapid exploration
perform rapid exploratory data analysis using interactive mode
create end-to-end analytics workflows
work in a general-purpose, yet easy to learn, programming language without the need for formatting SQL strings

Data scientists can use Ibis to:

extract a sample of data for local iteration with a fast local backend
prototype with the same API that will be used in production
preprocess and feature engineer data before training a machine learning model

Ibis for data platforms

Data platforms can use Ibis to quickly bring a fully-featured Python dataframe library with minimal effort to their platform. In addition to a great Python dataframe experience for their users, they also get integrations into the broader Python and ML ecosystem.

Often, data platforms evolve to support Python in some sequence like:

Develop a fast query engine with a SQL frontend
Gain popularity and need to support Python for data science and ML use cases
Develop a bespoke pandas or PySpark-like dataframe library and ML integrations

This third step is where Ibis comes in. Instead of spending a lot of time and money developing a bespoke Python dataframe library, you can create an Ibis backend for your data platform in as little as four hours for an experienced Ibis developer or, more typically, on the order of one or two months for a new contributor.

Why not the pandas or PySpark APIs?

The pandas API inherently does not scale due to its single-threaded design, ordered index, and a lot of API baggage. The creator of pandas (and Ibis!) has talked about the issues with pandas publicly. While there have been projects attempting to scale the pandas API, they always result in a dubious support matrix. You can see that with Modin or pandas on Spark (formerly known as Koalas).

Google BigQuery DataFrames is a more modern attempt to scale the pandas API built on top of Ibis. If you are going to build a pandas API we recommend you take a look at this project.

PySpark is a great API for Spark, but not very Pythonic and tightly coupled to the Spark execution engine.

Ibis takes inspiration from pandas and PySpark – and R and SQL – but is designed to be scalable from the start. If offers a neutral, self-governed open source option for your data platform.

Ibis for organizations

Organizations can use Ibis to standardize the interface for SQL and Python data practitioners. It also allows organizations to:

transfer data between systems
transform, analyze, and prepare data where it lives
benchmark your workload(s) across data systems using the same code
mix SQL and Python code seamlessly, with all the benefits of a general-purpose programming language, type checking, and expression validation

Ibis for library developers

Python developers creating libraries can use Ibis to:

instantly support nearly 20 data backends
instantly support pandas, PyArrow, and Polars objects
read and write from all common file formats (depending on the backend)
trace column-level lineage through Ibis expressions
compile Ibis expressions to SQL or Substrait
perform cross-dialect SQL transpilation (powered by SQLGlot)

How does Ibis work?

Most Python dataframes are tightly coupled to their execution engine. And many databases only support SQL, with no Python API. Ibis solves this problem by providing a common API for data manipulation in Python, and compiling that API into the backend’s native language. This means you can learn a single API and use it across any supported backend (execution engine).

Ibis broadly supports two types of backend:

SQL-generating backends
DataFrame-generating backends

As you can see, most backends generate SQL. Ibis uses SQLGlot to transform Ibis expressions into SQL strings. You can also use the .sql() methods to mix in SQL strings, compiling them to Ibis expressions.

While portability with Ibis isn’t perfect, commonalities across backends and SQL dialects combined with years of engineering effort produce a full-featured and robust framework for data manipulation in Python.

In the long-term, we aim for a standard query plan Intermediate Representation (IR) like Substrait to simplify this further.

Python + SQL: better together

For most backends, Ibis works by compiling Python expressions into SQL:

g = t.group_by(["species", "island"]).agg(count=t.count()).order_by("count")
ibis.to_sql(g)

SELECT
  *
FROM (
  SELECT
    `t0`.`species`,
    `t0`.`island`,
    COUNT(*) AS `count`
  FROM `ibis_read_parquet_izpvsttzjvhypgg6iie7ftrvsu` AS `t0`
  GROUP BY
    1,
    2
) AS `t1`
ORDER BY
  `t1`.`count` ASC NULLS LAST

You can mix and match Python and SQL code:

sql = """
SELECT
  species,
  island,
  COUNT(*) AS count
FROM penguins
GROUP BY species, island
""".strip()

con = ibis.connect("duckdb://")
t = con.read_parquet("penguins.parquet")
g = t.alias("penguins").sql(sql)
g

┏━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━┓
┃ species   ┃ island    ┃ count ┃
┡━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━┩
│ string    │ string    │ int64 │
├───────────┼───────────┼───────┤
│ Adelie    │ Torgersen │    52 │
│ Adelie    │ Biscoe    │    44 │
│ Adelie    │ Dream     │    56 │
│ Gentoo    │ Biscoe    │   124 │
│ Chinstrap │ Dream     │    68 │
└───────────┴───────────┴───────┘

g.order_by("count")

┏━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━┓
┃ species   ┃ island    ┃ count ┃
┡━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━┩
│ string    │ string    │ int64 │
├───────────┼───────────┼───────┤
│ Adelie    │ Biscoe    │    44 │
│ Adelie    │ Torgersen │    52 │
│ Adelie    │ Dream     │    56 │
│ Chinstrap │ Dream     │    68 │
│ Gentoo    │ Biscoe    │   124 │
└───────────┴───────────┴───────┘

con = ibis.connect("datafusion://")
t = con.read_parquet("penguins.parquet")
g = t.alias("penguins").sql(sql)
g

┏━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━┓
┃ species   ┃ island    ┃ count  ┃
┡━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━┩
│ string    │ string    │ !int64 │
├───────────┼───────────┼────────┤
│ Adelie    │ Biscoe    │     44 │
│ Adelie    │ Dream     │     56 │
│ Adelie    │ Torgersen │     52 │
│ Gentoo    │ Biscoe    │    124 │
│ Chinstrap │ Dream     │     68 │
└───────────┴───────────┴────────┘

g.order_by("count")

┏━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━┓
┃ species   ┃ island    ┃ count  ┃
┡━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━┩
│ string    │ string    │ !int64 │
├───────────┼───────────┼────────┤
│ Adelie    │ Biscoe    │     44 │
│ Adelie    │ Torgersen │     52 │
│ Adelie    │ Dream     │     56 │
│ Chinstrap │ Dream     │     68 │
│ Gentoo    │ Biscoe    │    124 │
└───────────┴───────────┴────────┘

con = ibis.connect("pyspark://")
t = con.read_parquet("penguins.parquet")
g = t.alias("penguins").sql(sql)
g

┏━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━┓
┃ species   ┃ island    ┃ count ┃
┡━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━┩
│ string    │ string    │ int64 │
├───────────┼───────────┼───────┤
│ Adelie    │ Biscoe    │    44 │
│ Chinstrap │ Dream     │    68 │
│ Adelie    │ Torgersen │    52 │
│ Adelie    │ Dream     │    56 │
│ Gentoo    │ Biscoe    │   124 │
└───────────┴───────────┴───────┘

g.order_by("count")

┏━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━┓
┃ species   ┃ island    ┃ count ┃
┡━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━┩
│ string    │ string    │ int64 │
├───────────┼───────────┼───────┤
│ Adelie    │ Biscoe    │    44 │
│ Adelie    │ Torgersen │    52 │
│ Adelie    │ Dream     │    56 │
│ Chinstrap │ Dream     │    68 │
│ Gentoo    │ Biscoe    │   124 │
└───────────┴───────────┴───────┘

This allows you to combine the flexibility of Python with the scale and performance of modern SQL.

Scaling up and out

Out of the box, Ibis offers a great local experience for working with many file formats. You can scale up with DuckDB (the default backend) or choose from other great options like Polars and DataFusion to work locally with large datasets. Once you hit scaling issues on a local machine, you can continue scaling up with a larger machine in the cloud using the same backend and same code.

If you hit scaling issues on a large single-node machine, you can switch to a distributed backend like PySpark, BigQuery, or Trino by simply changing your connection string.

Stream-batch unification

As of Ibis 8.0, the first stream processing backends have been added. Since these systems tend to support SQL, we can with minimal changes to Ibis support both batch and streaming workloads with a single API. We aim to further unify the batch and streaming paradigms going forward.

Ecosystem

Ibis is part of a larger ecosystem of Python data tools. It is designed to work well with other tools in this ecosystem, and we continue to make it easier to use Ibis with other tools over time.

Ibis already works with other Python dataframes like:

Ibis already works well with visualization libraries like:

Ibis already works well with dashboarding libraries like:

Ibis already works well with machine learning libraries like:

Supported backends

You can install Ibis and a supported backend with pip, conda, mamba, or pixi.

Install with the bigquery extra:

pip install 'ibis-framework[bigquery]'

Connect using ibis.bigquery.connect.

Warning

Note that the ibis-framework package is not the same as the ibis package in PyPI. These two libraries cannot coexist in the same Python environment, as they are both imported with the ibis module name.

Install with the clickhouse extra:

pip install 'ibis-framework[clickhouse]'

Connect using ibis.clickhouse.connect.

Warning

Install with the datafusion extra:

pip install 'ibis-framework[datafusion]'

Connect using ibis.datafusion.connect.

Warning

Install with the druid extra:

pip install 'ibis-framework[druid]'

Connect using ibis.druid.connect.

Warning

Install with the duckdb extra:

pip install 'ibis-framework[duckdb]'

Connect using ibis.duckdb.connect.

Warning

Install with the exasol extra:

pip install 'ibis-framework[exasol]'

Connect using ibis.exasol.connect.

Warning

Install alongside the apache-flink package:

pip install ibis-framework apache-flink

Connect using ibis.flink.connect.

Warning

Install with the impala extra:

pip install 'ibis-framework[impala]'

Connect using ibis.impala.connect.

Warning

Install with the mssql extra:

pip install 'ibis-framework[mssql]'

Connect using ibis.mssql.connect.

Warning

Install with the mysql extra:

pip install 'ibis-framework[mysql]'

Connect using ibis.mysql.connect.

Warning

Install with the oracle extra:

pip install 'ibis-framework[oracle]'

Connect using ibis.oracle.connect.

Warning

Install with the polars extra:

pip install 'ibis-framework[polars]'

Connect using ibis.polars.connect.

Warning

Install with the postgres extra:

pip install 'ibis-framework[postgres]'

Connect using ibis.postgres.connect.

Warning

Install with the pyspark extra:

pip install 'ibis-framework[pyspark]'

Connect using ibis.pyspark.connect.

Warning

Install with the risingwave extra:

pip install 'ibis-framework[risingwave]'

Connect using ibis.risingwave.connect.

Warning

Install with the snowflake extra:

pip install 'ibis-framework[snowflake]'

Connect using ibis.snowflake.connect.

Warning

Install with the sqlite extra:

pip install 'ibis-framework[sqlite]'

Connect using ibis.sqlite.connect.

Warning

Install with the trino extra:

pip install 'ibis-framework[trino]'

Connect using ibis.trino.connect.

Warning

Install the ibis-bigquery package:

conda install -c conda-forge ibis-bigquery

Connect using ibis.bigquery.connect.

Install the ibis-clickhouse package:

conda install -c conda-forge ibis-clickhouse

Connect using ibis.clickhouse.connect.

Install the ibis-datafusion package:

conda install -c conda-forge ibis-datafusion

Connect using ibis.datafusion.connect.

Install the ibis-druid package:

conda install -c conda-forge ibis-druid

Connect using ibis.druid.connect.

Install the ibis-duckdb package:

conda install -c conda-forge ibis-duckdb

Connect using ibis.duckdb.connect.

Install the ibis-exasol package:

conda install -c conda-forge ibis-exasol

Connect using ibis.exasol.connect.

Install the ibis-flink package:

conda install -c conda-forge ibis-flink

Connect using ibis.flink.connect.

Install the ibis-impala package:

conda install -c conda-forge ibis-impala

Connect using ibis.impala.connect.

Install the ibis-mssql package:

conda install -c conda-forge ibis-mssql

Connect using ibis.mssql.connect.

Install the ibis-mysql package:

conda install -c conda-forge ibis-mysql

Connect using ibis.mysql.connect.

Install the ibis-oracle package:

conda install -c conda-forge ibis-oracle

Connect using ibis.oracle.connect.

Install the ibis-polars package:

conda install -c conda-forge ibis-polars

Connect using ibis.polars.connect.

Install the ibis-postgres package:

conda install -c conda-forge ibis-postgres

Connect using ibis.postgres.connect.

Install the ibis-pyspark package:

conda install -c conda-forge ibis-pyspark

Connect using ibis.pyspark.connect.

Install the ibis-risingwave package:

conda install -c conda-forge ibis-risingwave

Connect using ibis.risingwave.connect.

Install the ibis-snowflake package:

conda install -c conda-forge ibis-snowflake

Connect using ibis.snowflake.connect.

Install the ibis-sqlite package:

conda install -c conda-forge ibis-sqlite

Connect using ibis.sqlite.connect.

Install the ibis-trino package:

conda install -c conda-forge ibis-trino

Connect using ibis.trino.connect.

Install the ibis-bigquery package:

mamba install -c conda-forge ibis-bigquery

Connect using ibis.bigquery.connect.

Install the ibis-clickhouse package:

mamba install -c conda-forge ibis-clickhouse

Connect using ibis.clickhouse.connect.

Install the ibis-datafusion package:

mamba install -c conda-forge ibis-datafusion

Connect using ibis.datafusion.connect.

Install the ibis-druid package:

mamba install -c conda-forge ibis-druid

Connect using ibis.druid.connect.

Install the ibis-duckdb package:

mamba install -c conda-forge ibis-duckdb

Connect using ibis.duckdb.connect.

Install the ibis-exasol package:

mamba install -c conda-forge ibis-exasol

Connect using ibis.exasol.connect.

Install the ibis-flink package:

mamba install -c conda-forge ibis-flink

Connect using ibis.flink.connect.

Install the ibis-impala package:

mamba install -c conda-forge ibis-impala

Connect using ibis.impala.connect.

Install the ibis-mssql package:

mamba install -c conda-forge ibis-mssql

Connect using ibis.mssql.connect.

Install the ibis-mysql package:

mamba install -c conda-forge ibis-mysql

Connect using ibis.mysql.connect.

Install the ibis-oracle package:

mamba install -c conda-forge ibis-oracle

Connect using ibis.oracle.connect.

Install the ibis-polars package:

mamba install -c conda-forge ibis-polars

Connect using ibis.polars.connect.

Install the ibis-postgres package:

mamba install -c conda-forge ibis-postgres

Connect using ibis.postgres.connect.

Install the ibis-pyspark package:

mamba install -c conda-forge ibis-pyspark

Connect using ibis.pyspark.connect.

Install the ibis-risingwave package:

mamba install -c conda-forge ibis-risingwave

Connect using ibis.risingwave.connect.

Install the ibis-snowflake package:

mamba install -c conda-forge ibis-snowflake

Connect using ibis.snowflake.connect.

Install the ibis-sqlite package:

mamba install -c conda-forge ibis-sqlite

Connect using ibis.sqlite.connect.

Install the ibis-trino package:

mamba install -c conda-forge ibis-trino

Connect using ibis.trino.connect.

Add the ibis-bigquery package:

pixi add ibis-bigquery

Connect using ibis.bigquery.connect.

Add the ibis-clickhouse package:

pixi add ibis-clickhouse

Connect using ibis.clickhouse.connect.

Add the ibis-datafusion package:

pixi add ibis-datafusion

Connect using ibis.datafusion.connect.

Add the ibis-druid package:

pixi add ibis-druid

Connect using ibis.druid.connect.

Add the ibis-duckdb package:

pixi add ibis-duckdb

Connect using ibis.duckdb.connect.

Add the ibis-exasol package:

pixi add ibis-exasol

Connect using ibis.exasol.connect.

Add the ibis-flink package:

pixi add ibis-flink

Connect using ibis.flink.connect.

Add the ibis-impala package:

pixi add ibis-impala

Connect using ibis.impala.connect.

Add the ibis-mssql package:

pixi add ibis-mssql

Connect using ibis.mssql.connect.

Add the ibis-mysql package:

pixi add ibis-mysql

Connect using ibis.mysql.connect.

Add the ibis-oracle package:

pixi add ibis-oracle

Connect using ibis.oracle.connect.

Add the ibis-polars package:

pixi add ibis-polars

Connect using ibis.polars.connect.

Add the ibis-postgres package:

pixi add ibis-postgres

Connect using ibis.postgres.connect.

Add the ibis-pyspark package:

pixi add ibis-pyspark

Connect using ibis.pyspark.connect.

Add the ibis-risingwave package:

pixi add ibis-risingwave

Connect using ibis.risingwave.connect.

Add the ibis-snowflake package:

pixi add ibis-snowflake

Connect using ibis.snowflake.connect.

Add the ibis-sqlite package:

pixi add ibis-sqlite

Connect using ibis.sqlite.connect.

Add the ibis-trino package:

pixi add ibis-trino

Connect using ibis.trino.connect.

See the backend support matrix for details on operations supported. Open a feature request if you’d like to see support for an operation in a given backend. If the backend supports it, we’ll do our best to add it quickly!

Community

Community discussions primarily take place on GitHub and Zulip.

Getting started

If you’re interested in trying Ibis we recommend the getting started tutorial.