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The following commit(s) were added to refs/heads/main by this push:
new deb1f25 Documentation updates: simplify examples and add section on
data sources (#955)
deb1f25 is described below
commit deb1f255ac314d569ee9299dfd421028763bbbb9
Author: Tim Saucer <[email protected]>
AuthorDate: Fri Nov 29 11:57:42 2024 -0500
Documentation updates: simplify examples and add section on data sources
(#955)
* Add a simple example to the introduction page to demonstrate loading a
dataframe from a csv file and displaying the contents
* Update basics doc to be a little more straight forward
* Move downloading of data files for examples into the build scripts and
just point the users to where these files are located instead of adding url lib
requests to the python examples so we can focus on what is most important to
the user
* Handle a few errors generated by doc site builder
* Switch example so that there is not confusion about the single and double
quotes due to capitalization
* Add section on data sources
* Build pipeline doesn't have polars and it isn't really necessary for the
example, so swith to a code block instead of ipython directive
---
.github/workflows/docs.yaml | 2 +
docs/.gitignore | 2 +
docs/build.sh | 11 +-
docs/source/images/jupyter_lab_df_view.png | Bin 0 -> 150303 bytes
docs/source/index.rst | 25 +--
docs/source/user-guide/basics.rst | 74 ++++----
.../user-guide/common-operations/aggregations.rst | 10 +-
.../user-guide/common-operations/functions.rst | 6 -
docs/source/user-guide/common-operations/index.rst | 2 +
.../common-operations/select-and-filter.rst | 11 +-
.../user-guide/common-operations/windows.rst | 6 -
docs/source/user-guide/data-sources.rst | 187 +++++++++++++++++++++
docs/source/user-guide/introduction.rst | 34 ++++
docs/source/user-guide/io/avro.rst | 2 +
docs/source/user-guide/io/csv.rst | 2 +
docs/source/user-guide/io/json.rst | 2 +
docs/source/user-guide/io/parquet.rst | 3 +-
docs/source/user-guide/io/table_provider.rst | 2 +
python/datafusion/dataframe.py | 2 +-
python/datafusion/plan.py | 4 +-
20 files changed, 300 insertions(+), 87 deletions(-)
diff --git a/.github/workflows/docs.yaml b/.github/workflows/docs.yaml
index e47497b..86288e2 100644
--- a/.github/workflows/docs.yaml
+++ b/.github/workflows/docs.yaml
@@ -75,6 +75,8 @@ jobs:
set -x
source venv/bin/activate
cd docs
+ curl -O
https://gist.githubusercontent.com/ritchie46/cac6b337ea52281aa23c049250a4ff03/raw/89a957ff3919d90e6ef2d34235e6bf22304f3366/pokemon.csv
+ curl -O
https://d37ci6vzurychx.cloudfront.net/trip-data/yellow_tripdata_2021-01.parquet
make html
- name: Copy & push the generated HTML
diff --git a/docs/.gitignore b/docs/.gitignore
index 41e1353..6e8a53b 100644
--- a/docs/.gitignore
+++ b/docs/.gitignore
@@ -1,2 +1,4 @@
pokemon.csv
yellow_trip_data.parquet
+yellow_tripdata_2021-01.parquet
+
diff --git a/docs/build.sh b/docs/build.sh
index 5afe858..31398d1 100755
--- a/docs/build.sh
+++ b/docs/build.sh
@@ -19,8 +19,17 @@
#
set -e
+
+if [ ! -f pokemon.csv ]; then
+ curl -O
https://gist.githubusercontent.com/ritchie46/cac6b337ea52281aa23c049250a4ff03/raw/89a957ff3919d90e6ef2d34235e6bf22304f3366/pokemon.csv
+fi
+
+if [ ! -f yellow_tripdata_2021-01.parquet ]; then
+ curl -O
https://d37ci6vzurychx.cloudfront.net/trip-data/yellow_tripdata_2021-01.parquet
+fi
+
rm -rf build 2> /dev/null
rm -rf temp 2> /dev/null
mkdir temp
cp -rf source/* temp/
-make SOURCEDIR=`pwd`/temp html
\ No newline at end of file
+make SOURCEDIR=`pwd`/temp html
diff --git a/docs/source/images/jupyter_lab_df_view.png
b/docs/source/images/jupyter_lab_df_view.png
new file mode 100644
index 0000000..9dafb4f
Binary files /dev/null and b/docs/source/images/jupyter_lab_df_view.png differ
diff --git a/docs/source/index.rst b/docs/source/index.rst
index b0103a3..34eb23b 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -43,27 +43,13 @@ Example
.. ipython:: python
- import datafusion
- from datafusion import col
- import pyarrow
+ from datafusion import SessionContext
- # create a context
- ctx = datafusion.SessionContext()
+ ctx = SessionContext()
- # create a RecordBatch and a new DataFrame from it
- batch = pyarrow.RecordBatch.from_arrays(
- [pyarrow.array([1, 2, 3]), pyarrow.array([4, 5, 6])],
- names=["a", "b"],
- )
- df = ctx.create_dataframe([[batch]], name="batch_array")
+ df = ctx.read_csv("pokemon.csv")
- # create a new statement
- df = df.select(
- col("a") + col("b"),
- col("a") - col("b"),
- )
-
- df
+ df.show()
.. _toc.links:
@@ -85,9 +71,10 @@ Example
user-guide/introduction
user-guide/basics
- user-guide/configuration
+ user-guide/data-sources
user-guide/common-operations/index
user-guide/io/index
+ user-guide/configuration
user-guide/sql
diff --git a/docs/source/user-guide/basics.rst
b/docs/source/user-guide/basics.rst
index 3c97d1e..f37378a 100644
--- a/docs/source/user-guide/basics.rst
+++ b/docs/source/user-guide/basics.rst
@@ -20,72 +20,76 @@
Concepts
========
-In this section, we will cover a basic example to introduce a few key concepts.
+In this section, we will cover a basic example to introduce a few key
concepts. We will use the same
+source file as described in the :ref:`Introduction <guide>`, the Pokemon data
set.
-.. code-block:: python
+.. ipython:: python
- import datafusion
- from datafusion import col
- import pyarrow
+ from datafusion import SessionContext, col, lit, functions as f
- # create a context
- ctx = datafusion.SessionContext()
+ ctx = SessionContext()
- # create a RecordBatch and a new DataFrame from it
- batch = pyarrow.RecordBatch.from_arrays(
- [pyarrow.array([1, 2, 3]), pyarrow.array([4, 5, 6])],
- names=["a", "b"],
- )
- df = ctx.create_dataframe([[batch]])
+ df = ctx.read_parquet("yellow_tripdata_2021-01.parquet")
- # create a new statement
df = df.select(
- col("a") + col("b"),
- col("a") - col("b"),
+ "trip_distance",
+ col("total_amount").alias("total"),
+ (f.round(lit(100.0) * col("tip_amount") / col("total_amount"),
lit(1))).alias("tip_percent"),
)
- # execute and collect the first (and only) batch
- result = df.collect()[0]
+ df.show()
-The first statement group:
+Session Context
+---------------
+
+The first statement group creates a
:py:class:`~datafusion.context.SessionContext`.
.. code-block:: python
# create a context
ctx = datafusion.SessionContext()
-creates a :py:class:`~datafusion.context.SessionContext`, that is, the main
interface for executing queries with DataFusion. It maintains the state
-of the connection between a user and an instance of the DataFusion engine.
Additionally it provides the following functionality:
+A Session Context is the main interface for executing queries with DataFusion.
It maintains the state
+of the connection between a user and an instance of the DataFusion engine.
Additionally it provides
+the following functionality:
-- Create a DataFrame from a CSV or Parquet data source.
-- Register a CSV or Parquet data source as a table that can be referenced from
a SQL query.
-- Register a custom data source that can be referenced from a SQL query.
+- Create a DataFrame from a data source.
+- Register a data source as a table that can be referenced from a SQL query.
- Execute a SQL query
+DataFrame
+---------
+
The second statement group creates a :code:`DataFrame`,
.. code-block:: python
- # create a RecordBatch and a new DataFrame from it
- batch = pyarrow.RecordBatch.from_arrays(
- [pyarrow.array([1, 2, 3]), pyarrow.array([4, 5, 6])],
- names=["a", "b"],
- )
- df = ctx.create_dataframe([[batch]])
+ # Create a DataFrame from a file
+ df = ctx.read_parquet("yellow_tripdata_2021-01.parquet")
A DataFrame refers to a (logical) set of rows that share the same column
names, similar to a `Pandas DataFrame
<https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html>`_.
DataFrames are typically created by calling a method on
:py:class:`~datafusion.context.SessionContext`, such as :code:`read_csv`, and
can then be modified by
calling the transformation methods, such as
:py:func:`~datafusion.dataframe.DataFrame.filter`,
:py:func:`~datafusion.dataframe.DataFrame.select`,
:py:func:`~datafusion.dataframe.DataFrame.aggregate`,
and :py:func:`~datafusion.dataframe.DataFrame.limit` to build up a query
definition.
-The third statement uses :code:`Expressions` to build up a query definition.
+Expressions
+-----------
+
+The third statement uses :code:`Expressions` to build up a query definition.
You can find
+explanations for what the functions below do in the user documentation for
+:py:func:`~datafusion.col`, :py:func:`~datafusion.lit`,
:py:func:`~datafusion.functions.round`,
+and :py:func:`~datafusion.expr.Expr.alias`.
.. code-block:: python
df = df.select(
- col("a") + col("b"),
- col("a") - col("b"),
+ "trip_distance",
+ col("total_amount").alias("total"),
+ (f.round(lit(100.0) * col("tip_amount") / col("total_amount"),
lit(1))).alias("tip_percent"),
)
-Finally the :py:func:`~datafusion.dataframe.DataFrame.collect` method converts
the logical plan represented by the DataFrame into a physical plan and execute
it,
-collecting all results into a list of `RecordBatch
<https://arrow.apache.org/docs/python/generated/pyarrow.RecordBatch.html>`_.
+Finally the :py:func:`~datafusion.dataframe.DataFrame.show` method converts
the logical plan
+represented by the DataFrame into a physical plan and execute it, collecting
all results and
+displaying them to the user. It is important to note that DataFusion performs
lazy evaluation
+of the DataFrame. Until you call a method such as
:py:func:`~datafusion.dataframe.DataFrame.show`
+or :py:func:`~datafusion.dataframe.DataFrame.collect`, DataFusion will not
perform the query.
diff --git a/docs/source/user-guide/common-operations/aggregations.rst
b/docs/source/user-guide/common-operations/aggregations.rst
index 8fee26a..e458e5f 100644
--- a/docs/source/user-guide/common-operations/aggregations.rst
+++ b/docs/source/user-guide/common-operations/aggregations.rst
@@ -26,15 +26,7 @@ to form a single summary value. For performing an
aggregation, DataFusion provid
.. ipython:: python
- import urllib.request
- from datafusion import SessionContext
- from datafusion import col, lit
- from datafusion import functions as f
-
- urllib.request.urlretrieve(
-
"https://gist.githubusercontent.com/ritchie46/cac6b337ea52281aa23c049250a4ff03/raw/89a957ff3919d90e6ef2d34235e6bf22304f3366/pokemon.csv";,
- "pokemon.csv",
- )
+ from datafusion import SessionContext, col, lit, functions as f
ctx = SessionContext()
df = ctx.read_csv("pokemon.csv")
diff --git a/docs/source/user-guide/common-operations/functions.rst
b/docs/source/user-guide/common-operations/functions.rst
index a0b95c9..8d6a808 100644
--- a/docs/source/user-guide/common-operations/functions.rst
+++ b/docs/source/user-guide/common-operations/functions.rst
@@ -25,14 +25,8 @@ We'll use the pokemon dataset in the following examples.
.. ipython:: python
- import urllib.request
from datafusion import SessionContext
- urllib.request.urlretrieve(
-
"https://gist.githubusercontent.com/ritchie46/cac6b337ea52281aa23c049250a4ff03/raw/89a957ff3919d90e6ef2d34235e6bf22304f3366/pokemon.csv";,
- "pokemon.csv",
- )
-
ctx = SessionContext()
ctx.register_csv("pokemon", "pokemon.csv")
df = ctx.table("pokemon")
diff --git a/docs/source/user-guide/common-operations/index.rst
b/docs/source/user-guide/common-operations/index.rst
index b15b04c..d7c708c 100644
--- a/docs/source/user-guide/common-operations/index.rst
+++ b/docs/source/user-guide/common-operations/index.rst
@@ -18,6 +18,8 @@
Common Operations
=================
+The contents of this section are designed to guide a new user through how to
use DataFusion.
+
.. toctree::
:maxdepth: 2
diff --git a/docs/source/user-guide/common-operations/select-and-filter.rst
b/docs/source/user-guide/common-operations/select-and-filter.rst
index 0759091..083bcbb 100644
--- a/docs/source/user-guide/common-operations/select-and-filter.rst
+++ b/docs/source/user-guide/common-operations/select-and-filter.rst
@@ -21,18 +21,15 @@ Column Selections
Use :py:func:`~datafusion.dataframe.DataFrame.select` for basic column
selection.
DataFusion can work with several file types, to start simple we can use a
subset of the
-`TLC Trip Record Data
<https://www.nyc.gov/site/tlc/about/tlc-trip-record-data.page>`_
+`TLC Trip Record Data
<https://www.nyc.gov/site/tlc/about/tlc-trip-record-data.page>`_,
+which you can download `here
<https://d37ci6vzurychx.cloudfront.net/trip-data/yellow_tripdata_2021-01.parquet>`_.
.. ipython:: python
-
- import urllib.request
- from datafusion import SessionContext
-
urllib.request.urlretrieve("https://d37ci6vzurychx.cloudfront.net/trip-data/yellow_tripdata_2021-01.parquet";,
- "yellow_trip_data.parquet")
+ from datafusion import SessionContext
ctx = SessionContext()
- df = ctx.read_parquet("yellow_trip_data.parquet")
+ df = ctx.read_parquet("yellow_tripdata_2021-01.parquet")
df.select("trip_distance", "passenger_count")
For mathematical or logical operations use :py:func:`~datafusion.col` to
select columns, and give meaningful names to the resulting
diff --git a/docs/source/user-guide/common-operations/windows.rst
b/docs/source/user-guide/common-operations/windows.rst
index 6091768..8225d12 100644
--- a/docs/source/user-guide/common-operations/windows.rst
+++ b/docs/source/user-guide/common-operations/windows.rst
@@ -30,16 +30,10 @@ We'll use the pokemon dataset (from Ritchie Vink) in the
following examples.
.. ipython:: python
- import urllib.request
from datafusion import SessionContext
from datafusion import col
from datafusion import functions as f
- urllib.request.urlretrieve(
-
"https://gist.githubusercontent.com/ritchie46/cac6b337ea52281aa23c049250a4ff03/raw/89a957ff3919d90e6ef2d34235e6bf22304f3366/pokemon.csv";,
- "pokemon.csv",
- )
-
ctx = SessionContext()
df = ctx.read_csv("pokemon.csv")
diff --git a/docs/source/user-guide/data-sources.rst
b/docs/source/user-guide/data-sources.rst
new file mode 100644
index 0000000..ba5967c
--- /dev/null
+++ b/docs/source/user-guide/data-sources.rst
@@ -0,0 +1,187 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+.. or more contributor license agreements. See the NOTICE file
+.. distributed with this work for additional information
+.. regarding copyright ownership. The ASF licenses this file
+.. to you under the Apache License, Version 2.0 (the
+.. "License"); you may not use this file except in compliance
+.. with the License. You may obtain a copy of the License at
+
+.. http://www.apache.org/licenses/LICENSE-2.0
+
+.. Unless required by applicable law or agreed to in writing,
+.. software distributed under the License is distributed on an
+.. "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+.. KIND, either express or implied. See the License for the
+.. specific language governing permissions and limitations
+.. under the License.
+
+.. _user_guide_data_sources:
+
+Data Sources
+============
+
+DataFusion provides a wide variety of ways to get data into a DataFrame to
perform operations.
+
+Local file
+----------
+
+DataFusion has the abilty to read from a variety of popular file formats, such
as :ref:`Parquet <io_parquet>`,
+:ref:`CSV <io_csv>`, :ref:`JSON <io_json>`, and :ref:`AVRO <io_avro>`.
+
+.. ipython:: python
+
+ from datafusion import SessionContext
+ ctx = SessionContext()
+ df = ctx.read_csv("pokemon.csv")
+ df.show()
+
+Create in-memory
+----------------
+
+Sometimes it can be convenient to create a small DataFrame from a Python list
or dictionary object.
+To do this in DataFusion, you can use one of the three functions
+:py:func:`~datafusion.context.SessionContext.from_pydict`,
+:py:func:`~datafusion.context.SessionContext.from_pylist`, or
+:py:func:`~datafusion.context.SessionContext.create_dataframe`.
+
+As their names suggest, ``from_pydict`` and ``from_pylist`` will create
DataFrames from Python
+dictionary and list objects, respectively. ``create_dataframe`` assumes you
will pass in a list
+of list of `PyArrow Record Batches
<https://arrow.apache.org/docs/python/generated/pyarrow.RecordBatch.html>`_.
+
+The following three examples all will create identical DataFrames:
+
+.. ipython:: python
+
+ import pyarrow as pa
+
+ ctx.from_pylist([
+ { "a": 1, "b": 10.0, "c": "alpha" },
+ { "a": 2, "b": 20.0, "c": "beta" },
+ { "a": 3, "b": 30.0, "c": "gamma" },
+ ]).show()
+
+ ctx.from_pydict({
+ "a": [1, 2, 3],
+ "b": [10.0, 20.0, 30.0],
+ "c": ["alpha", "beta", "gamma"],
+ }).show()
+
+ batch = pa.RecordBatch.from_arrays(
+ [
+ pa.array([1, 2, 3]),
+ pa.array([10.0, 20.0, 30.0]),
+ pa.array(["alpha", "beta", "gamma"]),
+ ],
+ names=["a", "b", "c"],
+ )
+
+ ctx.create_dataframe([[batch]]).show()
+
+
+Object Store
+------------
+
+DataFusion has support for multiple storage options in addition to local files.
+The example below requires an appropriate S3 account with access credentials.
+
+Supported Object Stores are
+
+- :py:class:`~datafusion.object_store.AmazonS3`
+- :py:class:`~datafusion.object_store.GoogleCloud`
+- :py:class:`~datafusion.object_store.Http`
+- :py:class:`~datafusion.object_store.LocalFileSystem`
+- :py:class:`~datafusion.object_store.MicrosoftAzure`
+
+.. code-block:: python
+
+ from datafusion.object_store import AmazonS3
+
+ region = "us-east-1"
+ bucket_name = "yellow-trips"
+
+ s3 = AmazonS3(
+ bucket_name=bucket_name,
+ region=region,
+ access_key_id=os.getenv("AWS_ACCESS_KEY_ID"),
+ secret_access_key=os.getenv("AWS_SECRET_ACCESS_KEY"),
+ )
+
+ path = f"s3://{bucket_name}/"
+ ctx.register_object_store("s3://", s3, None)
+
+ ctx.register_parquet("trips", path)
+
+ ctx.table("trips").show()
+
+Other DataFrame Libraries
+-------------------------
+
+DataFusion can import DataFrames directly from other libraries, such as
+`Polars <https://pola.rs/>`_ and `Pandas <https://pandas.pydata.org/>`_.
+Since DataFusion version 42.0.0, any DataFrame library that supports the Arrow
FFI PyCapsule
+interface can be imported to DataFusion using the
+:py:func:`~datafusion.context.SessionContext.from_arrow` function. Older
verions of Polars may
+not support the arrow interface. In those cases, you can still import via the
+:py:func:`~datafusion.context.SessionContext.from_polars` function.
+
+.. code-block:: python
+
+ import pandas as pd
+
+ data = { "a": [1, 2, 3], "b": [10.0, 20.0, 30.0], "c": ["alpha", "beta",
"gamma"] }
+ pandas_df = pd.DataFrame(data)
+
+ datafusion_df = ctx.from_arrow(pandas_df)
+ datafusion_df.show()
+
+.. code-block:: python
+
+ import polars as pl
+ polars_df = pl.DataFrame(data)
+
+ datafusion_df = ctx.from_arrow(polars_df)
+ datafusion_df.show()
+
+Delta Lake
+----------
+
+DataFusion 43.0.0 and later support the ability to register table providers
from sources such
+as Delta Lake. This will require a recent version of
+`deltalake <https://delta-io.github.io/delta-rs/>`_ to provide the required
interfaces.
+
+.. code-block:: python
+
+ from deltalake import DeltaTable
+
+ delta_table = DeltaTable("path_to_table")
+ ctx.register_table_provider("my_delta_table", delta_table)
+ df = ctx.table("my_delta_table")
+ df.show()
+
+On older versions of ``deltalake`` (prior to 0.22) you can use the
+`Arrow DataSet
<https://arrow.apache.org/docs/python/generated/pyarrow.dataset.Dataset.html>`_
+interface to import to DataFusion, but this does not support features such as
filter push down
+which can lead to a significant performance difference.
+
+.. code-block:: python
+
+ from deltalake import DeltaTable
+
+ delta_table = DeltaTable("path_to_table")
+ ctx.register_dataset("my_delta_table", delta_table.to_pyarrow_dataset())
+ df = ctx.table("my_delta_table")
+ df.show()
+
+Iceberg
+-------
+
+Coming soon!
+
+Custom Table Provider
+---------------------
+
+You can implement a custom Data Provider in Rust and expose it to DataFusion
through the
+the interface as describe in the :ref:`Custom Table Provider
<io_custom_table_provider>`
+section. This is an advanced topic, but a
+`user example
<https://github.com/apache/datafusion-python/tree/main/examples/ffi-table-provider>`_
+is provided in the DataFusion repository.
diff --git a/docs/source/user-guide/introduction.rst
b/docs/source/user-guide/introduction.rst
index 8abb911..7b30ef2 100644
--- a/docs/source/user-guide/introduction.rst
+++ b/docs/source/user-guide/introduction.rst
@@ -39,5 +39,39 @@ You can verify the installation by running:
import datafusion
datafusion.__version__
+In this documentation we will also show some examples for how DataFusion
integrates
+with Jupyter notebooks. To install and start a Jupyter labs session use
+.. code-block:: shell
+
+ pip install jupyterlab
+ jupyter lab
+
+To demonstrate working with DataFusion, we need a data source. Later in the
tutorial we will show
+options for data sources. For our first example, we demonstrate using a
Pokemon dataset that you
+can download
+`here
<https://gist.githubusercontent.com/ritchie46/cac6b337ea52281aa23c049250a4ff03/raw/89a957ff3919d90e6ef2d34235e6bf22304f3366/pokemon.csv>`_.
+
+With that file in place you can use the following python example to view the
DataFrame in
+DataFusion.
+
+.. ipython:: python
+
+ from datafusion import SessionContext
+
+ ctx = SessionContext()
+
+ df = ctx.read_csv("pokemon.csv")
+
+ df.show()
+
+If you are working in a Jupyter notebook, you can also use the following to
give you a table
+display that may be easier to read.
+
+.. code-block:: shell
+
+ display(df)
+.. image:: ../images/jupyter_lab_df_view.png
+ :width: 800
+ :alt: Rendered table showing Pokemon DataFrame
diff --git a/docs/source/user-guide/io/avro.rst
b/docs/source/user-guide/io/avro.rst
index 5f1ff72..66398ac 100644
--- a/docs/source/user-guide/io/avro.rst
+++ b/docs/source/user-guide/io/avro.rst
@@ -15,6 +15,8 @@
.. specific language governing permissions and limitations
.. under the License.
+.. _io_avro:
+
Avro
====
diff --git a/docs/source/user-guide/io/csv.rst
b/docs/source/user-guide/io/csv.rst
index d2a62bf..144b661 100644
--- a/docs/source/user-guide/io/csv.rst
+++ b/docs/source/user-guide/io/csv.rst
@@ -15,6 +15,8 @@
.. specific language governing permissions and limitations
.. under the License.
+.. _io_csv:
+
CSV
===
diff --git a/docs/source/user-guide/io/json.rst
b/docs/source/user-guide/io/json.rst
index f9da375..39030db 100644
--- a/docs/source/user-guide/io/json.rst
+++ b/docs/source/user-guide/io/json.rst
@@ -15,6 +15,8 @@
.. specific language governing permissions and limitations
.. under the License.
+.. _io_json:
+
JSON
====
`JSON <https://www.json.org/json-en.html>`_ (JavaScript Object Notation) is a
lightweight data-interchange format.
diff --git a/docs/source/user-guide/io/parquet.rst
b/docs/source/user-guide/io/parquet.rst
index 75bc981..c5b9ca3 100644
--- a/docs/source/user-guide/io/parquet.rst
+++ b/docs/source/user-guide/io/parquet.rst
@@ -15,6 +15,8 @@
.. specific language governing permissions and limitations
.. under the License.
+.. _io_parquet:
+
Parquet
=======
@@ -22,7 +24,6 @@ It is quite simple to read a parquet file using the
:py:func:`~datafusion.contex
.. code-block:: python
-
from datafusion import SessionContext
ctx = SessionContext()
diff --git a/docs/source/user-guide/io/table_provider.rst
b/docs/source/user-guide/io/table_provider.rst
index 2ff9ae4..bd1d6b8 100644
--- a/docs/source/user-guide/io/table_provider.rst
+++ b/docs/source/user-guide/io/table_provider.rst
@@ -15,6 +15,8 @@
.. specific language governing permissions and limitations
.. under the License.
+.. _io_custom_table_provider:
+
Custom Table Provider
=====================
diff --git a/python/datafusion/dataframe.py b/python/datafusion/dataframe.py
index efd4038..e283f59 100644
--- a/python/datafusion/dataframe.py
+++ b/python/datafusion/dataframe.py
@@ -473,7 +473,7 @@ class DataFrame:
*on_exprs: Expr,
how: Literal["inner", "left", "right", "full", "semi", "anti"] =
"inner",
) -> DataFrame:
- """Join two :py:class:`DataFrame`using the specified expressions.
+ """Join two :py:class:`DataFrame` using the specified expressions.
On expressions are used to support in-equality predicates. Equality
predicates are correctly optimized
diff --git a/python/datafusion/plan.py b/python/datafusion/plan.py
index 3836ede..a71965f 100644
--- a/python/datafusion/plan.py
+++ b/python/datafusion/plan.py
@@ -42,7 +42,7 @@ class LogicalPlan:
(table) with a potentially different schema. Plans form a dataflow tree
where data flows from leaves up to the root to produce the query result.
- `LogicalPlan`s can be created by the SQL query planner, the DataFrame API,
+ A `LogicalPlan` can be created by the SQL query planner, the DataFrame API,
or programmatically (for example custom query languages).
"""
@@ -107,7 +107,7 @@ class ExecutionPlan:
self._raw_plan = plan
def children(self) -> List[ExecutionPlan]:
- """Get a list of children `ExecutionPlan`s that act as inputs to this
plan.
+ """Get a list of children `ExecutionPlan` that act as inputs to this
plan.
The returned list will be empty for leaf nodes such as scans, will
contain a
single value for unary nodes, or two values for binary nodes (such as
joins).
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