docs/source/user-guide/basics.rst

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Concepts
========

In this section, we will cover a basic example to introduce a few key concepts.

.. code-block:: python

    import datafusion
    from datafusion import col
    import pyarrow

    # create a context
    ctx = datafusion.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]])

    # create a new statement
    df = df.select(
        col("a") + col("b"),
        col("a") - col("b"),
    )

    # execute and collect the first (and only) batch
    result = df.collect()[0]

The first statement group:

.. code-block:: python

    # create a context
    ctx = datafusion.SessionContext()

creates a :code:`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:

- 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.
- Execute a SQL query

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]])

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 :code:`SessionContext`, such as :code:`read_csv`, and can then be modified by
calling the transformation methods, such as :meth:`.DataFrame.filter`, :meth:`.DataFrame.select`, :meth:`.DataFrame.aggregate`,
and :meth:`.DataFrame.limit` to build up a query definition.

The third statement uses :code:`Expressions` to build up a query definition.

.. code-block:: python

    df = df.select(
        col("a") + col("b"),
        col("a") - col("b"),
    )

Finally the :code:`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>`_.