Ibis is a Python library that provides a lightweight, universal interface for data wrangling. It helps Python users explore and transform data of any size, stored anywhere.
Ibis has three primary components:
- A dataframe API for Python. This means that Python users can write Ibis code to manipulate tabular data.
- Interfaces to 10+ query engines. This means that wherever data is stored, data scientists can use Ibis as their API of choice to communicate with any of those query engines.
- Deferred execution. Ibis uses deferred execution, meaning that execution of code is pushed to the query engine. This means users can execute at the speed of their backend, not their local computer.
Ibis aims to be a future-proof solution to interacting with data using Python and can accomplish this goal through its main features:
- Familiar API: Ibis’s API design borrows from popular APIs like pandas and dplyr that most users already know and like to use.
- Consistent syntax: Ibis aims to be universal Python API for tabular data, big or small.
- Deferred execution: Ibis pushes code execution to the query engine and only moves required data into memory when it has to. This leads to more faster, more efficient analytics workflows
- Interactive mode: Ibis also provides an interactive mode, in which users can quickly diagnose problems, do exploratory data analysis, and mock up workflows locally.
- 10+ supported backends: Ibis supports multiple query engines and DataFrame APIs. Use one interface to transform with your data wherever it lives: from DataFrames in pandas to parquet files through DuckDB to tables in BigQuery.
- Minimize rewrites: Depending on backend capabilities, teams can often keep most of their Ibis code the same whether a team changes anything on the backend, like increasing or decreasing computing power, changing the number or size of their databases, or switching backend engines.
- Speed up prototype to production. Scale code written and tested locally to the cloud of distributed systems with minimum rewrites.
- Boost performance of existing Python or pandas code. For example a general rule of thumb for pandas is "Have 5 to 10 times as much RAM as the size of your dataset". When a dataset exceeds this rule, using in-memory frameworks, like pandas, can be slow. Instead, using Ibis will significantly speed up your workflows because of its deferred execution. Ibis also empowers you to switch to a faster database engine, without changing much of your code.
- Get rid of long, error-prone, fstrings. Ibis provides one syntax for multiple query engines and dataframe APIs that lets you avoid learning new flavors of SQL or other framework-specific code. Learn the syntax once and use that syntax anywhere.
Ibis acts as a universal frontend to the following systems:
- Apache Impala
- ClickHouse
- Dask
- DuckDB
- Google BigQuery
- HeavyAI
- MySQL
- Microsoft SQL Server
- Pandas
- Polars
- PostgreSQL
- PySpark
- Snowflake (experimental)
- SQLite
The list of supported backends is continuously growing. Anyone can get involved in adding new ones! Learn more about contributing to ibis in our contributing docs at https://github.com/ibis-project/ibis/blob/master/docs/CONTRIBUTING.md
Install Ibis from PyPI with:
pip install ibis-framework
Or from conda-forge with:
conda install ibis-framework -c conda-forge
(It’s a common mistake to pip install ibis. If you try to use Ibis and get errors early on try uninstalling ibis and installing ibis-framework)
For specific backends, include the backend name in brackets for PyPI:
pip install ibis-framework[duckdb]
Or use ibis-$BACKEND where $BACKEND the specific backend you want to use:
conda install ibis-postgres -c conda-forge
You can find a number of helpful tutorials on the Ibis website here including:
You can also get started analyzing any dataset, anywhere with just a few lines of Ibis code. Here’s an example of how to use Ibis with an SQLite database.
Download the SQLite database from the ibis-tutorial-data GCS (Google Cloud Storage) bucket, then connect to it using ibis.
# make a directory called geo_dir and add the geography database to that folder
mkdir -p geo_dir
curl -LsS -o geo_dir/geography.db 'https://storage.googleapis.com/ibis-tutorial-data/geography.db'Connect to the the database and show the available tables
>>> import ibis
>>> ibis.options.interactive = True
>>> connection = ibis.sqlite.connect('geo_dir/geography.db')
>>> connection.list_tables()
['countries', 'gdp', 'independence']Choose the countries table and preview its first few rows
>>> countries = connection.table('countries')
countries.head()| iso_alpha2 | iso_alpha3 | iso_numeric | fips | name | capital | area_km2 | population | continent | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | AD | AND | 20 | AN | Andorra | Andorra la Vella | 468 | 84000 | EU |
| 1 | AE | ARE | 784 | AE | United Arab Emirates | Abu Dhabi | 82880 | 4975593 | AS |
| 2 | AF | AFG | 4 | AF | Afghanistan | Kabul | 647500 | 29121286 | AS |
| 3 | AG | ATG | 28 | AC | Antigua and Barbuda | St. Johns | 443 | 86754 | NA |
| 4 | AI | AIA | 660 | AV | Anguilla | The Valley | 102 | 13254 | NA |
# Select the name, continent and population columns and filter them to only return countries from Asia
asian_countries = countries['name', 'continent', 'population'].filter(countries['continent'] == 'AS')
asian_countries.limit(6)| name | continent | population | |
|---|---|---|---|
| 0 | United Arab Emirates | AS | 4975593 |
| 1 | Afghanistan | AS | 29121286 |
| 2 | Armenia | AS | 2968000 |
| 3 | Azerbaijan | AS | 8303512 |
| 4 | Bangladesh | AS | 156118464 |
| 5 | Bahrain | AS | 738004 |
Ibis is an open source project and welcomes contributions from anyone in the community. Read more about how you can contribute here. We care about keeping our community welcoming for all to participate and have a code of conduct to ensure this. The Ibis project is open sourced under the Apache License.
Join our community here:
- Twitter: https://twitter.com/IbisData
- Gitter: https://gitter.im/ibis-dev/Lobby
- StackOverflow: https://stackoverflow.com/questions/tagged/ibis
For more information visit our official website here.