docs/source/user-guide/basics.rst

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.. _user_guide_concepts:

Concepts
========

In this section, we will cover a basic example to introduce a few key concepts. We will use the
2021 Yellow Taxi Trip Records (`download <https://d37ci6vzurychx.cloudfront.net/trip-data/yellow_tripdata_2021-01.parquet>`_),
from the `TLC Trip Record Data <https://www.nyc.gov/site/tlc/about/tlc-trip-record-data.page>`_.

.. ipython:: python

    from datafusion import SessionContext, col, lit, functions as f

    ctx = SessionContext()

    df = ctx.read_parquet("yellow_tripdata_2021-01.parquet")

    df = df.select(
        "trip_distance",
        col("total_amount").alias("total"),
        (f.round(lit(100.0) * col("tip_amount") / col("total_amount"), lit(1))).alias("tip_percent"),
    )

    df.show()

Session Context
---------------

The first statement group creates a :py:class:`~datafusion.context.SessionContext`.

.. code-block:: python

    # create a context
    ctx = datafusion.SessionContext()

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 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 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.

For more details on working with DataFrames, including visualization options and conversion to other formats, see :doc:`dataframe/index`.

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(
        "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.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.