base on the portable Python dataframe library # Ibis [](http://ibis-project.org) [](https://ibis-project.zulipchat.com) [](https://anaconda.org/conda-forge/ibis-framework) [](https://pypi.org/project/ibis-framework) [](https://github.com/ibis-project/ibis/actions/workflows/ibis-main.yml?query=branch%3Amain) [](https://github.com/ibis-project/ibis/actions/workflows/ibis-backends.yml?query=branch%3Amain) [](https://codecov.io/gh/ibis-project/ibis) ## What is Ibis? Ibis is the portable Python dataframe library: - Fast local dataframes (via DuckDB by default) - Lazy dataframe expressions - Interactive mode for iterative data exploration - [Compose Python dataframe and SQL code](#python--sql-better-together) - Use the same dataframe API for [nearly 20 backends](#backends) - Iterate locally and deploy remotely by [changing a single line of code](#portability) See the documentation on ["Why Ibis?"](https://ibis-project.org/why) to learn more. ## Getting started You can `pip install` Ibis with a backend and example data: ```bash pip install 'ibis-framework[duckdb,examples]' ``` > 💡 **Tip** > > See the [installation guide](https://ibis-project.org/install) for more installation options. Then use Ibis: ```python >>> import ibis >>> ibis.options.interactive = True >>> t = ibis.examples.penguins.fetch() >>> t ┏━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━┓ ┃ 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 │ │ Adelie │ Torgersen │ NULL │ NULL │ NULL │ NULL │ NULL │ 2007 │ │ Adelie │ Torgersen │ 36.7 │ 19.3 │ 193 │ 3450 │ female │ 2007 │ │ Adelie │ Torgersen │ 39.3 │ 20.6 │ 190 │ 3650 │ male │ 2007 │ │ Adelie │ Torgersen │ 38.9 │ 17.8 │ 181 │ 3625 │ female │ 2007 │ │ Adelie │ Torgersen │ 39.2 │ 19.6 │ 195 │ 4675 │ male │ 2007 │ │ Adelie │ Torgersen │ 34.1 │ 18.1 │ 193 │ 3475 │ NULL │ 2007 │ │ Adelie │ Torgersen │ 42.0 │ 20.2 │ 190 │ 4250 │ NULL │ 2007 │ │ … │ … │ … │ … │ … │ … │ … │ … │ └─────────┴───────────┴────────────────┴───────────────┴───────────────────┴─────────────┴────────┴───────┘ >>> g = t.group_by("species", "island").agg(count=t.count()).order_by("count") >>> g ┏━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━┓ ┃ species ┃ island ┃ count ┃ ┡━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━┩ │ string │ string │ int64 │ ├───────────┼───────────┼───────┤ │ Adelie │ Biscoe │ 44 │ │ Adelie │ Torgersen │ 52 │ │ Adelie │ Dream │ 56 │ │ Chinstrap │ Dream │ 68 │ │ Gentoo │ Biscoe │ 124 │ └───────────┴───────────┴───────┘ ``` > 💡 **Tip** > > See the [getting started tutorial](https://ibis-project.org/tutorials/getting_started) for a full introduction to Ibis. ## Python + SQL: better together For most backends, Ibis works by compiling its dataframe expressions into SQL: ```python >>> ibis.to_sql(g) SELECT "t1"."species", "t1"."island", "t1"."count" FROM ( SELECT "t0"."species", "t0"."island", COUNT(*) AS "count" FROM "penguins" AS "t0" GROUP BY 1, 2 ) AS "t1" ORDER BY "t1"."count" ASC ``` You can mix SQL and Python code: ```python >>> a = t.sql("SELECT species, island, count(*) AS count FROM penguins GROUP BY 1, 2") >>> a ┏━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━┓ ┃ species ┃ island ┃ count ┃ ┡━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━┩ │ string │ string │ int64 │ ├───────────┼───────────┼───────┤ │ Adelie │ Torgersen │ 52 │ │ Adelie │ Biscoe │ 44 │ │ Adelie │ Dream │ 56 │ │ Gentoo │ Biscoe │ 124 │ │ Chinstrap │ Dream │ 68 │ └───────────┴───────────┴───────┘ >>> b = a.order_by("count") >>> b ┏━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━┓ ┃ 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. ## Backends Ibis supports nearly 20 backends: - [Apache DataFusion](https://ibis-project.org/backends/datafusion/) - [Apache Druid](https://ibis-project.org/backends/druid/) - [Apache Flink](https://ibis-project.org/backends/flink) - [Apache Impala](https://ibis-project.org/backends/impala/) - [Apache PySpark](https://ibis-project.org/backends/pyspark/) - [BigQuery](https://ibis-project.org/backends/bigquery/) - [ClickHouse](https://ibis-project.org/backends/clickhouse/) - [DuckDB](https://ibis-project.org/backends/duckdb/) - [Exasol](https://ibis-project.org/backends/exasol) - [MySQL](https://ibis-project.org/backends/mysql/) - [Oracle](https://ibis-project.org/backends/oracle/) - [Polars](https://ibis-project.org/backends/polars/) - [PostgreSQL](https://ibis-project.org/backends/postgresql/) - [RisingWave](https://ibis-project.org/backends/risingwave/) - [SQL Server](https://ibis-project.org/backends/mssql/) - [SQLite](https://ibis-project.org/backends/sqlite/) - [Snowflake](https://ibis-project.org/backends/snowflake) - [Trino](https://ibis-project.org/backends/trino/) ## How it works 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: 1. SQL-generating backends 2. DataFrame-generating backends  ## Portability To use different backends, you can set the backend Ibis uses: ```python >>> ibis.set_backend("duckdb") >>> ibis.set_backend("polars") >>> ibis.set_backend("datafusion") ``` Typically, you'll create a connection object: ```python >>> con = ibis.duckdb.connect() >>> con = ibis.polars.connect() >>> con = ibis.datafusion.connect() ``` And work with tables in that backend: ```python >>> con.list_tables() ['penguins'] >>> t = con.table("penguins") ``` You can also read from common file formats like CSV or Apache Parquet: ```python >>> t = con.read_csv("penguins.csv") >>> t = con.read_parquet("penguins.parquet") ``` This allows you to iterate locally and deploy remotely by changing a single line of code. > 💡 **Tip** > > Check out [the blog on backend agnostic arrays](https://ibis-project.org/posts/backend-agnostic-arrays/) for one example using the same code across DuckDB and BigQuery. ## Community and contributing Ibis is an open source project and welcomes contributions from anyone in the community. - Read [the contributing guide](https://github.com/ibis-project/ibis/blob/main/docs/CONTRIBUTING.md). - We care about keeping the community welcoming for all. Check out [the code of conduct](https://github.com/ibis-project/ibis/blob/main/CODE_OF_CONDUCT.md). - The Ibis project is open sourced under the [Apache License](https://github.com/ibis-project/ibis/blob/main/LICENSE.txt). Join our community by interacting on GitHub or chatting with us on [Zulip](https://ibis-project.zulipchat.com/). For more information visit https://ibis-project.org/. ## Governance The Ibis project is an [independently governed](https://github.com/ibis-project/governance/blob/main/governance.md) open source community project to build and maintain the portable Python dataframe library. Ibis has contributors across a range of data companies and institutions. ", Assign "at most 3 tags" to the expected json: {"id":"7850","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"