Ibis

the portable Python dataframe library

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Why Ibis? Installation Tutorial: getting started GitHub Chat RSS

Install

We recommend starting with the default backend (DuckDB).

pip install 'ibis-framework[duckdb]'

1

Install Ibis with optional dependencies (DuckDB in this case).

Show supported backends

Backends

Need to use Ibis with a backend that isn’t currently supported? Let us know!

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

pip

BigQuery

Install with the bigquery extra:

pip install 'ibis-framework[bigquery]'

Connect using ibis.bigquery.connect.

ClickHouse

Install with the clickhouse extra:

pip install 'ibis-framework[clickhouse]'

Connect using ibis.clickhouse.connect.

Dask

Install with the dask extra:

pip install 'ibis-framework[dask]'

Connect using ibis.dask.connect.

DataFusion

Install with the datafusion extra:

pip install 'ibis-framework[datafusion]'

Connect using ibis.datafusion.connect.

Druid

Install with the druid extra:

pip install 'ibis-framework[druid]'

Connect using ibis.druid.connect.

DuckDB

Install with the duckdb extra:

pip install 'ibis-framework[duckdb]'

Connect using ibis.duckdb.connect.

Impala

Install with the impala extra:

pip install 'ibis-framework[impala]'

Connect using ibis.impala.connect.

MSSQL

Install with the mssql extra:

pip install 'ibis-framework[mssql]'

Connect using ibis.mssql.connect.

MySQL

Install with the mysql extra:

pip install 'ibis-framework[mysql]'

Connect using ibis.mysql.connect.

Oracle

Install with the oracle extra:

pip install 'ibis-framework[oracle]'

Connect using ibis.oracle.connect.

pandas

Install with the pandas extra:

pip install 'ibis-framework[pandas]'

Connect using ibis.pandas.connect.

Polars

Install with the polars extra:

pip install 'ibis-framework[polars]'

Connect using ibis.polars.connect.

PostgreSQL

Install with the postgres extra:

pip install 'ibis-framework[postgres]'

Connect using ibis.postgres.connect.

PySpark

Install with the pyspark extra:

pip install 'ibis-framework[pyspark]'

Connect using ibis.pyspark.connect.

Snowflake

Install with the snowflake extra:

pip install 'ibis-framework[snowflake]'

Connect using ibis.snowflake.connect.

SQLite

Install with the sqlite extra:

pip install 'ibis-framework[sqlite]'

Connect using ibis.sqlite.connect.

Trino

Install with the trino extra:

pip install 'ibis-framework[trino]'

Connect using ibis.trino.connect.

conda

BigQuery

Install the ibis-bigquery package:

conda install -c conda-forge ibis-bigquery

Connect using ibis.bigquery.connect.

ClickHouse

Install the ibis-clickhouse package:

conda install -c conda-forge ibis-clickhouse

Connect using ibis.clickhouse.connect.

Dask

Install the ibis-dask package:

conda install -c conda-forge ibis-dask

Connect using ibis.dask.connect.

DataFusion

Install the ibis-datafusion package:

conda install -c conda-forge ibis-datafusion

Connect using ibis.datafusion.connect.

Druid

Install the ibis-druid package:

conda install -c conda-forge ibis-druid

Connect using ibis.druid.connect.

DuckDB

Install the ibis-duckdb package:

conda install -c conda-forge ibis-duckdb

Connect using ibis.duckdb.connect.

Impala

Install the ibis-impala package:

conda install -c conda-forge ibis-impala

Connect using ibis.impala.connect.

MSSQL

Install the ibis-mssql package:

conda install -c conda-forge ibis-mssql

Connect using ibis.mssql.connect.

MySQL

Install the ibis-mysql package:

conda install -c conda-forge ibis-mysql

Connect using ibis.mysql.connect.

Oracle

Install the ibis-oracle package:

conda install -c conda-forge ibis-oracle

Connect using ibis.oracle.connect.

pandas

Install the ibis-pandas package:

conda install -c conda-forge ibis-pandas

Connect using ibis.pandas.connect.

Polars

Install the ibis-polars package:

conda install -c conda-forge ibis-polars

Connect using ibis.polars.connect.

PostgreSQL

Install the ibis-postgres package:

conda install -c conda-forge ibis-postgres

Connect using ibis.postgres.connect.

PySpark

Install the ibis-pyspark package:

conda install -c conda-forge ibis-pyspark

Connect using ibis.pyspark.connect.

Snowflake

Install the ibis-snowflake package:

conda install -c conda-forge ibis-snowflake

Connect using ibis.snowflake.connect.

SQLite

Install the ibis-sqlite package:

conda install -c conda-forge ibis-sqlite

Connect using ibis.sqlite.connect.

Trino

Install the ibis-trino package:

conda install -c conda-forge ibis-trino

Connect using ibis.trino.connect.

mamba

BigQuery

Install the ibis-bigquery package:

mamba install -c conda-forge ibis-bigquery

Connect using ibis.bigquery.connect.

ClickHouse

Install the ibis-clickhouse package:

mamba install -c conda-forge ibis-clickhouse

Connect using ibis.clickhouse.connect.

Dask

Install the ibis-dask package:

mamba install -c conda-forge ibis-dask

Connect using ibis.dask.connect.

DataFusion

Install the ibis-datafusion package:

mamba install -c conda-forge ibis-datafusion

Connect using ibis.datafusion.connect.

Druid

Install the ibis-druid package:

mamba install -c conda-forge ibis-druid

Connect using ibis.druid.connect.

DuckDB

Install the ibis-duckdb package:

mamba install -c conda-forge ibis-duckdb

Connect using ibis.duckdb.connect.

Impala

Install the ibis-impala package:

mamba install -c conda-forge ibis-impala

Connect using ibis.impala.connect.

MSSQL

Install the ibis-mssql package:

mamba install -c conda-forge ibis-mssql

Connect using ibis.mssql.connect.

MySQL

Install the ibis-mysql package:

mamba install -c conda-forge ibis-mysql

Connect using ibis.mysql.connect.

Oracle

Install the ibis-oracle package:

mamba install -c conda-forge ibis-oracle

Connect using ibis.oracle.connect.

pandas

Install the ibis-pandas package:

mamba install -c conda-forge ibis-pandas

Connect using ibis.pandas.connect.

Polars

Install the ibis-polars package:

mamba install -c conda-forge ibis-polars

Connect using ibis.polars.connect.

PostgreSQL

Install the ibis-postgres package:

mamba install -c conda-forge ibis-postgres

Connect using ibis.postgres.connect.

PySpark

Install the ibis-pyspark package:

mamba install -c conda-forge ibis-pyspark

Connect using ibis.pyspark.connect.

Snowflake

Install the ibis-snowflake package:

mamba install -c conda-forge ibis-snowflake

Connect using ibis.snowflake.connect.

SQLite

Install the ibis-sqlite package:

mamba install -c conda-forge ibis-sqlite

Connect using ibis.sqlite.connect.

Trino

Install the ibis-trino package:

mamba install -c conda-forge ibis-trino

Connect using ibis.trino.connect.

pixi

BigQuery

Add the ibis-bigquery package:

pixi add ibis-bigquery

Connect using ibis.bigquery.connect.

ClickHouse

Add the ibis-clickhouse package:

pixi add ibis-clickhouse

Connect using ibis.clickhouse.connect.

Dask

Add the ibis-dask package:

pixi add ibis-dask

Connect using ibis.dask.connect.

DataFusion

Add the ibis-datafusion package:

pixi add ibis-datafusion

Connect using ibis.datafusion.connect.

Druid

Add the ibis-druid package:

pixi add ibis-druid

Connect using ibis.druid.connect.

DuckDB

Add the ibis-duckdb package:

pixi add ibis-duckdb

Connect using ibis.duckdb.connect.

Impala

Add the ibis-impala package:

pixi add ibis-impala

Connect using ibis.impala.connect.

MSSQL

Add the ibis-mssql package:

pixi add ibis-mssql

Connect using ibis.mssql.connect.

MySQL

Add the ibis-mysql package:

pixi add ibis-mysql

Connect using ibis.mysql.connect.

Oracle

Add the ibis-oracle package:

pixi add ibis-oracle

Connect using ibis.oracle.connect.

pandas

Add the ibis-pandas package:

pixi add ibis-pandas

Connect using ibis.pandas.connect.

Polars

Add the ibis-polars package:

pixi add ibis-polars

Connect using ibis.polars.connect.

PostgreSQL

Add the ibis-postgres package:

pixi add ibis-postgres

Connect using ibis.postgres.connect.

PySpark

Add the ibis-pyspark package:

pixi add ibis-pyspark

Connect using ibis.pyspark.connect.

Snowflake

Add the ibis-snowflake package:

pixi add ibis-snowflake

Connect using ibis.snowflake.connect.

SQLite

Add the ibis-sqlite package:

pixi add ibis-sqlite

Connect using ibis.sqlite.connect.

Trino

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!

Show quickstart

Quickstart

See the getting started tutorial for a more in-depth introduction to Ibis. Below is a quick overview.

import ibis
import ibis.selectors as s

ibis.options.interactive = True

t = ibis.examples.penguins.fetch()
t.head(3)

1

Ensure you install Ibis first.

2

Use interactive mode for exploratory data analysis (EDA) or demos.

3

Load a dataset from the built-in examples.

4

Display the table.

┏━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━┓
┃ 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 │
└─────────┴───────────┴────────────────┴───────────────┴───────────────────┴─────────────┴────────┴───────┘

Ibis is a dataframe library with familiar syntax.

t[10:15]

1

Display a slice of the table.

┏━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━┓
┃ 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 │           37.8 │          17.1 │               186 │        3300 │ NULL   │  2007 │
│ Adelie  │ Torgersen │           37.8 │          17.3 │               180 │        3700 │ NULL   │  2007 │
│ Adelie  │ Torgersen │           41.1 │          17.6 │               182 │        3200 │ female │  2007 │
│ Adelie  │ Torgersen │           38.6 │          21.2 │               191 │        3800 │ male   │  2007 │
│ Adelie  │ Torgersen │           34.6 │          21.1 │               198 │        4400 │ male   │  2007 │
└─────────┴───────────┴────────────────┴───────────────┴───────────────────┴─────────────┴────────┴───────┘

Show analytics

Analytics

Ibis is built for easy analytics at scale in Python.

(
    t.filter(ibis._["body_mass_g"] != None)
    .group_by(["species", "island"])
    .aggregate(count=ibis._.count())
    .order_by(ibis.desc("count"))
)

1

Group by species and island, and compute the number of rows in each group.

┏━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━┓
┃ species   ┃ island    ┃ count ┃
┡━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━┩
│ string    │ string    │ int64 │
├───────────┼───────────┼───────┤
│ Gentoo    │ Biscoe    │   123 │
│ Chinstrap │ Dream     │    68 │
│ Adelie    │ Dream     │    56 │
│ Adelie    │ Torgersen │    51 │
│ Adelie    │ Biscoe    │    44 │
└───────────┴───────────┴───────┘

Show EDA + visualization

Exploratory data analysis (EDA) and visualization

Exploratory data analysis

Ibis has built-in methods for exploration and visualization.

num_species = int(t.select("species").nunique().to_pandas())
t["species"].topk(num_species)

1

Compute the number of species in the dataset.

2

Display the top species by count.

┏━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┓
┃ species   ┃ Count(species) ┃
┡━━━━━━━━━━━╇━━━━━━━━━━━━━━━━┩
│ string    │ int64          │
├───────────┼────────────────┤
│ Adelie    │            152 │
│ Gentoo    │            124 │
│ Chinstrap │             68 │
└───────────┴────────────────┘

Visualization

Ibis works with any Python plotting library that supports the dataframe interchange protocol.

Code

width = 640
height = 480

1

Set the width and height of the plots.

grouped = (
    t.group_by("species")
    .aggregate(count=ibis._.count())
    .order_by(ibis.desc("count"))
)
grouped

1

Setup data to plot.

2

Display the table.

┏━━━━━━━━━━━┳━━━━━━━┓
┃ species   ┃ count ┃
┡━━━━━━━━━━━╇━━━━━━━┩
│ string    │ int64 │
├───────────┼───────┤
│ Adelie    │   152 │
│ Gentoo    │   124 │
│ Chinstrap │    68 │
└───────────┴───────┘

Altair

pip install altair

import altair as alt

chart = (
    alt.Chart(grouped.to_pandas())
    .mark_bar()
    .encode(
        x="species",
        y="count",
        tooltip=["species", "count"],
    )
    .properties(width=width, height=height)
    .interactive()
)
chart

matplotlib

pip install matplotlib

import matplotlib.pyplot as plt

chart = grouped.to_pandas().plot.bar(
    x="species",
    y="count",
    figsize=(width / 100, height / 100),
)
plt.show()

Plotly

pip install plotly

import plotly.express as px

chart = px.bar(
    grouped.to_pandas(),
    x="species",
    y="count",
    width=width,
    height=height,
)
chart

plotnine

pip install plotnine

from plotnine import ggplot, aes, geom_bar, theme

chart = (
    ggplot(
        grouped,
        aes(x="species", y="count"),
    )
    + geom_bar(stat="identity")
    + theme(figure_size=(width / 100, height / 100))
)
chart

<Figure Size: (640 x 480)>

seaborn

pip install seaborn

import seaborn as sns

chart = sns.barplot(
    data=grouped.to_pandas(),
    x="species",
    y="count",
)
chart.figure.set_size_inches(width / 100, height / 100)

Show data science

Data science

Use Ibis with your favorite data science libraries for concise and efficient workflows.

import ibis.selectors as s


def transform(t):
    t = t.mutate(
        s.across(s.numeric(), {"zscore": lambda x: (x - x.mean()) / x.std()})
    ).dropna()
    return t


f = transform(t.drop("year"))
f.select("species", "island", s.contains("zscore"))

1

Import the selectors module.

2

Define a function to transform the table for code reuse (compute z-scores on numeric columns).

3

Apply the function to the table and assign it to a new variable.

4

Display the transformed table.

┏━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━┓
┃ species ┃ island    ┃ bill_length_mm_zscore ┃ bill_depth_mm_zscore ┃ flipper_length_mm_zscore ┃ body_mass_g_zscore ┃
┡━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━┩
│ string  │ string    │ float64               │ float64              │ float64                  │ float64            │
├─────────┼───────────┼───────────────────────┼──────────────────────┼──────────────────────────┼────────────────────┤
│ Adelie  │ Torgersen │             -0.883205 │             0.784300 │                -1.416272 │          -0.563317 │
│ Adelie  │ Torgersen │             -0.809939 │             0.126003 │                -1.060696 │          -0.500969 │
│ Adelie  │ Torgersen │             -0.663408 │             0.429833 │                -0.420660 │          -1.186793 │
│ Adelie  │ Torgersen │             -1.322799 │             1.088129 │                -0.562890 │          -0.937403 │
│ Adelie  │ Torgersen │             -0.846572 │             1.746426 │                -0.776236 │          -0.688012 │
│ Adelie  │ Torgersen │             -0.919837 │             0.328556 │                -1.416272 │          -0.719186 │
│ Adelie  │ Torgersen │             -0.864888 │             1.240044 │                -0.420660 │           0.590115 │
│ Adelie  │ Torgersen │             -0.516876 │             0.227280 │                -1.345156 │          -1.249141 │
│ Adelie  │ Torgersen │             -0.974787 │             2.050255 │                -0.705121 │          -0.500969 │
│ Adelie  │ Torgersen │             -1.707443 │             1.999617 │                -0.207315 │           0.247203 │
│ …       │ …         │                     … │                    … │                        … │                  … │
└─────────┴───────────┴───────────────────────┴──────────────────────┴──────────────────────────┴────────────────────┘

pip install scikit-learn

import plotly.express as px
from sklearn.decomposition import PCA

X = f.select(s.contains("zscore"))

n_components = 3
pca = PCA(n_components=n_components).fit(X)

t_pca = ibis.memtable(pca.transform(X)).relabel(
    {"col0": "pc1", "col1": "pc2", "col2": "pc3"}
)

f = f.mutate(row_number=ibis.row_number().over()).join(
    t_pca.mutate(row_number=ibis.row_number().over()), "row_number"
)

px.scatter_3d(
    f.to_pandas(),
    x="pc1",
    y="pc2",
    z="pc3",
    color="species",
    symbol="island",
)

1

Import data science libraries

2

Select “features” (numeric columns) as X

3

Compute PCA

4

Create a table from the PCA results

5

Join the PCA results to the original table

6

Plot the results

Show input and output

Input and output

Ibis supports a variety of input and output options.

Data platforms

You can connect Ibis to any supported backend to read and write data in backend-native tables.

Code

con = ibis.duckdb.connect("penguins.ddb")
t = con.create_table("penguins", t.to_pyarrow(), overwrite=True)

con = ibis.duckdb.connect("penguins.ddb")
t = con.table("penguins")
t.head(3)

1

Connect to a backend.

2

Load a table.

3

Display the table.

┏━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━┓
┃ 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 │
└─────────┴───────────┴────────────────┴───────────────┴───────────────────┴─────────────┴────────┴───────┘

grouped = (
    t.group_by(["species", "island"])
    .aggregate(count=ibis._.count())
    .order_by(ibis.desc("count"))
)
con.create_table("penguins_grouped", grouped.to_pyarrow(), overwrite=True)

1

Create a lazily evaluated Ibis expression.

2

Write to a table.

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

File formats

Depending on the backend, you can read and write data in several file formats.

CSV

pip install 'ibis-framework[duckdb]'

t.to_csv("penguins.csv")
ibis.read_csv("penguins.csv").head(3)

1

Write the table to a CSV file. Dependent on backend.

2

Read the CSV file into a table. Dependent on backend.

┏━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━┓
┃ 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 │
└─────────┴───────────┴────────────────┴───────────────┴───────────────────┴─────────────┴────────┴───────┘

Delta Lake

pip install 'ibis-framework[duckdb,deltalake]'

t.to_delta("penguins.delta", mode="overwrite")
ibis.read_delta("penguins.delta").head(3)

1

Write the table to a Delta Lake table. Dependent on backend.

2

Read the Delta Lake table into a table. Dependent on backend.

┏━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━┓
┃ 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 │
└─────────┴───────────┴────────────────┴───────────────┴───────────────────┴─────────────┴────────┴───────┘

Parquet

pip install 'ibis-framework[duckdb]'

t.to_parquet("penguins.parquet")
ibis.read_parquet("penguins.parquet").head(3)

1

Write the table to a Parquet file. Dependent on backend.

2

Read the Parquet file into a table. Dependent on backend.

┏━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━┓
┃ 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 │
└─────────┴───────────┴────────────────┴───────────────┴───────────────────┴─────────────┴────────┴───────┘

With other Python libraries

Ibis uses Apache Arrow for efficient data transfer to and from other libraries. Ibis tables implement the __dataframe__ and __array__ protocols, so you can pass them to any library that supports these protocols.

pandas

You can convert Ibis tables to pandas dataframes.

pip install pandas

df = t.to_pandas()
df.head(3)

1

Returns a pandas dataframe.

	species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex	year
0	Adelie	Torgersen	39.1	18.7	181.0	3750.0	male	2007
1	Adelie	Torgersen	39.5	17.4	186.0	3800.0	female	2007
2	Adelie	Torgersen	40.3	18.0	195.0	3250.0	female	2007

Or you can convert pandas dataframes to Ibis tables.

t = ibis.memtable(df)
t.head(3)

1

Returns an Ibis table.

┏━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━┓
┃ species ┃ island    ┃ bill_length_mm ┃ bill_depth_mm ┃ flipper_length_mm ┃ body_mass_g ┃ sex    ┃ year  ┃
┡━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━┩
│ string  │ string    │ float64        │ float64       │ float64           │ float64     │ string │ int64 │
├─────────┼───────────┼────────────────┼───────────────┼───────────────────┼─────────────┼────────┼───────┤
│ Adelie  │ Torgersen │           39.1 │          18.7 │             181.0 │      3750.0 │ male   │  2007 │
│ Adelie  │ Torgersen │           39.5 │          17.4 │             186.0 │      3800.0 │ female │  2007 │
│ Adelie  │ Torgersen │           40.3 │          18.0 │             195.0 │      3250.0 │ female │  2007 │
└─────────┴───────────┴────────────────┴───────────────┴───────────────────┴─────────────┴────────┴───────┘

polars

You can convert Ibis tables to Polars dataframes.

pip install polars

import polars as pl

df = pl.from_arrow(t.to_pyarrow())
df.head(3)

shape: (3, 8)

species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex	year
str	str	f64	f64	f64	f64	str	i64
"Adelie"	"Torgersen"	39.1	18.7	181.0	3750.0	"male"	2007
"Adelie"	"Torgersen"	39.5	17.4	186.0	3800.0	"female"	2007
"Adelie"	"Torgersen"	40.3	18.0	195.0	3250.0	"female"	2007

Or Polars dataframes to Ibis tables.

t = ibis.memtable(df)
t.head(3)

┏━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━┳━━━━━━━┓
┃ col0   ┃ col1      ┃ col2    ┃ col3    ┃ col4    ┃ col5    ┃ col6   ┃ col7  ┃
┡━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━╇━━━━━━━┩
│ string │ string    │ float64 │ float64 │ float64 │ float64 │ string │ int64 │
├────────┼───────────┼─────────┼─────────┼─────────┼─────────┼────────┼───────┤
│ Adelie │ Torgersen │    39.1 │    18.7 │   181.0 │  3750.0 │ male   │  2007 │
│ Adelie │ Torgersen │    39.5 │    17.4 │   186.0 │  3800.0 │ female │  2007 │
│ Adelie │ Torgersen │    40.3 │    18.0 │   195.0 │  3250.0 │ female │  2007 │
└────────┴───────────┴─────────┴─────────┴─────────┴─────────┴────────┴───────┘

pyarrow

You can convert Ibis tables to PyArrow tables.

pip install pyarrow

t.to_pyarrow()

pyarrow.Table
col0: string
col1: string
col2: double
col3: double
col4: double
col5: double
col6: string
col7: int64
----
col0: [["Adelie","Adelie","Adelie","Adelie","Adelie",...,"Chinstrap","Chinstrap","Chinstrap","Chinstrap","Chinstrap"]]
col1: [["Torgersen","Torgersen","Torgersen","Torgersen","Torgersen",...,"Dream","Dream","Dream","Dream","Dream"]]
col2: [[39.1,39.5,40.3,null,36.7,...,55.8,43.5,49.6,50.8,50.2]]
col3: [[18.7,17.4,18,null,19.3,...,19.8,18.1,18.2,19,18.7]]
col4: [[181,186,195,null,193,...,207,202,193,210,198]]
col5: [[3750,3800,3250,null,3450,...,4000,3400,3775,4100,3775]]
col6: [["male","female","female",null,"female",...,"male","female","male","male","female"]]
col7: [[2007,2007,2007,2007,2007,...,2009,2009,2009,2009,2009]]

Or PyArrow batches:

t.to_pyarrow_batches()

<pyarrow.lib.RecordBatchReader at 0x7fff8d58ff60>

And you can convert PyArrow tables to Ibis tables.

ibis.memtable(t.to_pyarrow()).head(3)

┏━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━┳━━━━━━━┓
┃ col0   ┃ col1      ┃ col2    ┃ col3    ┃ col4    ┃ col5    ┃ col6   ┃ col7  ┃
┡━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━╇━━━━━━━┩
│ string │ string    │ float64 │ float64 │ float64 │ float64 │ string │ int64 │
├────────┼───────────┼─────────┼─────────┼─────────┼─────────┼────────┼───────┤
│ Adelie │ Torgersen │    39.1 │    18.7 │   181.0 │  3750.0 │ male   │  2007 │
│ Adelie │ Torgersen │    39.5 │    17.4 │   186.0 │  3800.0 │ female │  2007 │
│ Adelie │ Torgersen │    40.3 │    18.0 │   195.0 │  3250.0 │ female │  2007 │
└────────┴───────────┴─────────┴─────────┴─────────┴─────────┴────────┴───────┘

torch

You can convert Ibis tables to torch tensors.

pip install torch

t.select(s.numeric()).limit(3).to_torch()

{'col2': tensor([39.1000, 39.5000, 40.3000], dtype=torch.float64),
 'col3': tensor([18.7000, 17.4000, 18.0000], dtype=torch.float64),
 'col4': tensor([181., 186., 195.], dtype=torch.float64),
 'col5': tensor([3750., 3800., 3250.], dtype=torch.float64),
 'col7': tensor([2007, 2007, 2007], dtype=torch.int16)}

__dataframe__

You can directly call the __dataframe__ protocol on Ibis tables, though this is typically handled by the library you’re using.

t.__dataframe__()

<ibis.expr.types.dataframe_interchange.IbisDataFrame at 0x7fff8c1ab6d0>

__array__

You can directly call the __array__ protocol on Ibis tables, though this is typically handled by the library you’re using.

t.__array__()

array([['Adelie', 'Torgersen', 39.1, ..., 3750.0, 'male', 2007],
       ['Adelie', 'Torgersen', 39.5, ..., 3800.0, 'female', 2007],
       ['Adelie', 'Torgersen', 40.3, ..., 3250.0, 'female', 2007],
       ...,
       ['Chinstrap', 'Dream', 49.6, ..., 3775.0, 'male', 2009],
       ['Chinstrap', 'Dream', 50.8, ..., 4100.0, 'male', 2009],
       ['Chinstrap', 'Dream', 50.2, ..., 3775.0, 'female', 2009]],
      dtype=object)

Show SQL + Python

SQL + Python

Ibis has the ibis.to_sql to generate SQL strings and ibis.show_sql display them. Ibis uses SQLGlot under the hood to allow passing a dialect parameter to SQL methods.

BigQuery

dialect = "bigquery"
sql = ibis.to_sql(
    grouped,
    dialect=dialect,
)
sql

1

Set the dialect.

2

Convert the table to a SQL string.

3

Display the SQL string.

SELECT
  t0.*
FROM (
  SELECT
    t1.`species`,
    t1.`island`,
    count(1) AS `count`
  FROM penguins AS t1
  GROUP BY
    1,
    2
) AS t0
ORDER BY
  t0.`count` DESC

You can chain Ibis expressions and .sql together.

con.sql(sql, dialect=dialect).filter(ibis._["species"] == "Adelie")

1

Chain .sql calls and Ibis expressions together.

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

Snowflake

dialect = "snowflake"
sql = ibis.to_sql(
    grouped,
    dialect=dialect,
)
sql

1

Set the dialect.

2

Convert the table to a SQL string.

3

Display the SQL string.

SELECT
  t0.species,
  t0.island,
  t0."count"
FROM (
  SELECT
    t1.species AS species,
    t1.island AS island,
    COUNT(*) AS "count"
  FROM main.penguins AS t1
  GROUP BY
    1,
    2
) AS t0
ORDER BY
  t0."count" DESC

You can chain Ibis expressions and .sql together.

con.sql(sql, dialect=dialect).filter(ibis._["species"] == "Adelie")

1

Chain .sql calls and Ibis expressions together.

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

Oracle

dialect = "oracle"
sql = ibis.to_sql(
    grouped,
    dialect=dialect,
)
sql

1

Set the dialect.

2

Convert the table to a SQL string.

3

Display the SQL string.

SELECT
  t0.species,
  t0.island,
  t0."count"
FROM (
  SELECT
    t1.species AS species,
    t1.island AS island,
    COUNT(*) AS "count"
  FROM main.penguins t1
  GROUP BY
    t1.species,
    t1.island
) t0
ORDER BY
  t0."count" DESC

You can chain Ibis expressions and .sql together.

con.sql(sql, dialect=dialect).filter(ibis._["species"] == "Adelie")

1

Chain .sql calls and Ibis expressions together.

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

MySQL

dialect = "mysql"
sql = ibis.to_sql(
    grouped,
    dialect=dialect,
)
sql

1

Set the dialect.

2

Convert the table to a SQL string.

3

Display the SQL string.

SELECT
  t0.species,
  t0.island,
  t0.count
FROM (
  SELECT
    t1.species AS species,
    t1.island AS island,
    COUNT(*) AS count
  FROM main.penguins AS t1
  GROUP BY
    1,
    2
) AS t0
ORDER BY
  t0.count DESC

You can chain Ibis expressions and .sql together.

con.sql(sql, dialect=dialect).filter(ibis._["species"] == "Adelie")

1

Chain .sql calls and Ibis expressions together.

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

MSSQL

dialect = "mssql"
sql = ibis.to_sql(
    grouped,
    dialect=dialect,
)
sql

1

Set the dialect.

2

Convert the table to a SQL string.

3

Display the SQL string.

SELECT
  t0.species,
  t0.island,
  t0.count
FROM (
  SELECT
    t1.species AS species,
    t1.island AS island,
    COUNT(*) AS count
  FROM main.penguins AS t1
  GROUP BY
    t1.species,
    t1.island
) AS t0
ORDER BY
  t0.count DESC

You can chain Ibis expressions and .sql together.

con.sql(sql, dialect=dialect).filter(ibis._["species"] == "Adelie")

1

Chain .sql calls and Ibis expressions together.

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

PostgreSQL

dialect = "postgres"
sql = ibis.to_sql(
    grouped,
    dialect=dialect,
)
sql

1

Set the dialect.

2

Convert the table to a SQL string.

3

Display the SQL string.

SELECT
  t0.species,
  t0.island,
  t0.count
FROM (
  SELECT
    t1.species AS species,
    t1.island AS island,
    COUNT(*) AS count
  FROM main.penguins AS t1
  GROUP BY
    1,
    2
) AS t0
ORDER BY
  t0.count DESC

You can chain Ibis expressions and .sql together.

con.sql(sql, dialect=dialect).filter(ibis._["species"] == "Adelie")

1

Chain .sql calls and Ibis expressions together.

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

SQLite

dialect = "sqlite"
sql = ibis.to_sql(
    grouped,
    dialect=dialect,
)
sql

1

Set the dialect.

2

Convert the table to a SQL string.

3

Display the SQL string.

SELECT
  t0.species,
  t0.island,
  t0.count
FROM (
  SELECT
    t1.species AS species,
    t1.island AS island,
    COUNT(*) AS count
  FROM main.penguins AS t1
  GROUP BY
    1,
    2
) AS t0
ORDER BY
  t0.count DESC

You can chain Ibis expressions and .sql together.

con.sql(sql, dialect=dialect).filter(ibis._["species"] == "Adelie")

1

Chain .sql calls and Ibis expressions together.

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

Trino

dialect = "trino"
sql = ibis.to_sql(
    grouped,
    dialect=dialect,
)
sql

1

Set the dialect.

2

Convert the table to a SQL string.

3

Display the SQL string.

SELECT
  t0.species,
  t0.island,
  t0.count
FROM (
  SELECT
    t1.species AS species,
    t1.island AS island,
    COUNT(*) AS count
  FROM main.penguins AS t1
  GROUP BY
    1,
    2
) AS t0
ORDER BY
  t0.count DESC

You can chain Ibis expressions and .sql together.

con.sql(sql, dialect=dialect).filter(ibis._["species"] == "Adelie")

1

Chain .sql calls and Ibis expressions together.

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