Documentation updates: simplify examples and add section on data sour…

…ces (#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

@@ -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

docs/.gitignore

@@ -1,2 +1,4 @@
 pokemon.csv
 yellow_trip_data.parquet
+yellow_tripdata_2021-01.parquet
+

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
+make SOURCEDIR=`pwd`/temp html

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
 
 

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.

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")

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")

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
 

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

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")