docs/source/python/interchange_protocol.rst
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.. _pyarrow-dataframe-interchange-protocol:
The interchange protocol is implemented for pa.Table and
pa.RecordBatch and is used to interchange data between
PyArrow and other dataframe libraries that also have the
protocol implemented. The data structures that are supported
in the protocol are primitive data types plus the dictionary
data type. The protocol also has missing data support and
it supports chunking, meaning accessing the
data in “batches” of rows.
The Python dataframe interchange protocol is designed by the
Consortium for Python Data API Standards <https://data-apis.org/>_
in order to enable data interchange between dataframe
libraries in the Python ecosystem. See more about the
standard in the
protocol documentation <https://data-apis.org/dataframe-protocol/latest/index.html>_.
__dataframe__() methodThe __dataframe__() method creates a new exchange object that
the consumer library can take and construct an object of it's own.
.. code-block:: python
>>> import pyarrow as pa
>>> table = pa.table({"n_attendees": [100, 10, 1]})
>>> table.__dataframe__()
<pyarrow.interchange.dataframe._PyArrowDataFrame object at ...>
This is meant to be used by the consumer library when calling
the from_dataframe() function and is not meant to be used manually
by the user.
from_dataframe()With the from_dataframe() function, we can construct a :class:pyarrow.Table
from any dataframe object that implements the
__dataframe__() method via the dataframe interchange
protocol.
We can for example take a pandas dataframe and construct a PyArrow table with the use of the interchange protocol:
.. code-block:: python
>>> import pyarrow
>>> from pyarrow.interchange import from_dataframe
>>> import pandas as pd
>>> df = pd.DataFrame({
... "n_attendees": [100, 10, 1],
... "country": ["Italy", "Spain", "Slovenia"],
... })
>>> df
n_attendees country
0 100 Italy
1 10 Spain
2 1 Slovenia
>>> from_dataframe(df)
pyarrow.Table
n_attendees: int64
country: large_string
----
n_attendees: [[100,10,1]]
country: [["Italy","Spain","Slovenia"]]
We can do the same with a polars dataframe:
.. code-block:: python
>>> import polars as pl # doctest: +SKIP
>>> from datetime import datetime # doctest: +SKIP
>>> arr = [datetime(2023, 5, 20, 10, 0), # doctest: +SKIP
... datetime(2023, 5, 20, 11, 0),
... datetime(2023, 5, 20, 13, 30)]
>>> df = pl.DataFrame({ # doctest: +SKIP
... 'Talk': ['About Polars','Intro into PyArrow','Coding in Rust'],
... 'Time': arr,
... })
>>> df # doctest: +SKIP
shape: (3, 2)
┌────────────────────┬─────────────────────┐
│ Talk ┆ Time │
│ --- ┆ --- │
│ str ┆ datetime[μs] │
╞════════════════════╪═════════════════════╡
│ About Polars ┆ 2023-05-20 10:00:00 │
│ Intro into PyArrow ┆ 2023-05-20 11:00:00 │
│ Coding in Rust ┆ 2023-05-20 13:30:00 │
└────────────────────┴─────────────────────┘
>>> from_dataframe(df) # doctest: +SKIP
pyarrow.Table
Talk: large_string
Time: timestamp[us]
----
Talk: [["About Polars","Intro into PyArrow","Coding in Rust"]]
Time: [[2023-05-20 10:00:00.000000,2023-05-20 11:00:00.000000,2023-05-20 13:30:00.000000]]