.. _quickstart: =============== Getting Started =============== .. module:: recipe This page gives a good introduction in how to get started with Recipe. This assumes you already have Recipe installed. If you do not, head over to :ref:`Installing Recipe `. First, make sure that: * Recipe is :ref:`installed ` * Recipe is :ref:`up-to-date ` Let's gets started with some simple use cases and examples. ------------------ Creating a Shelf ------------------ A :class:`Shelf ` is a place to store SQL fragments. In recipe these are called :class:`Ingredients `. Ingredients can contain columns that should be part of the ``SELECT`` portion of a query, filters that are part of a ``WHERE`` clause of a query, group_bys that contribute to a query's ``GROUP BY`` and havings which add ``HAVING`` limits to a query. You won't have to construct an Ingredient with all these parts directly because Recipe contains convenience classes that help you build the most common SQL fragments. The two most common Ingredient subclasses are :class:`Dimension ` which provides both a column and a grouping on that column and :class:`Metric ` which provides a column aggregation. Shelf acts like a dictionary. The keys are strings and the values are Ingredients. The keys are a shortcut name for the ingredient. Here's an example. .. code:: python from recipe import * # Define a database connection oven = get_oven('sqlite://') Base = declarative_base(bind=oven.engine) # Define a SQLAlchemy mapping class Census(Base): state = Column('state', String(), primary_key=True) sex = Column('sex', String()) age = Column('age', Integer()) pop2000 = Column('pop2000', Integer()) pop2008 = Column('pop2008', Integer()) __tablename__ = 'census' __table_args__ = {'extend_existing': True} # Use that mapping to define a shelf. shelf = Shelf({ 'state': Dimension(Census.state), 'age': WtdAvgMetric(Census.age, Census.pop2000), 'population': Metric(func.sum(Census.pop2000)) }) This is a shelf with two metrics (a weighted average of age, and the sum of population) and a dimension which lets you group on US State names. --------------------------------- Using the Shelf to build a Recipe --------------------------------- Now that you have the shelf, you can build a :class:`Recipe `. .. code:: python r = Recipe(shelf=shelf, session=oven.Session())\ .dimensions('state')\ .metrics('age')\ .order_by('-age') print(r.dataset.csv) This results in .. code:: state,age,state_id Florida,39.08283934000634,Florida West Virginia,38.555058651148165,West Virginia Maine,38.10118393261269,Maine Pennsylvania,38.03856695544053,Pennsylvania Rhode Island,37.20343773873182,Rhode Island Connecticut,37.19867141455273,Connecticut ... Note that a recipe contains data from a single table.` ------------------------------------------------ Defining Shelves and Recipes Using Configuration ------------------------------------------------ Recipes and shelves can be defined using plain ole' python objects. In the following example we'll use YAML. For instance, we can define the shelf using this yaml config. .. code:: YAML state: kind: Dimension field: state age: kind: WtdAvgMetric field: age weight: pop2000 population: kind: Metric field: pop2000 We can load this config by parsing it against any **selectable**, which can be a SQLAlchemy mapping, a SQLAlchemy select, or another Recipe. .. code:: python shelf_yaml = yaml.load('shelf.yaml') s = Shelf.from_config(shelf_yaml, Census) We can also define a Recipe with Configuration .. code:: YAML metrics: - age - population dimensions: - state order_by: - '-age' If we load that we get a Recipe .. code:: python recipe_yaml = yaml.load('shelf.yaml') recipe = Recipe.from_config(s, recipe_yaml, session=oven.Session()) print(recipe.dataset.csv) This results in a list of the oldest US states and their populations: .. code:: state,age,population,state_id Florida,39.08283934000634,15976093,Florida West Virginia,38.555058651148165,1805847,West Virginia Maine,38.10118393261269,1271694,Maine Pennsylvania,38.03856695544053,12276157,Pennsylvania Rhode Island,37.20343773873182,1047200,Rhode Island Connecticut,37.19867141455273,3403620,Connecticut ... ------------------------------- Adding Features with Extensions ------------------------------- Using extensions, you can add features to Recipe. Here are a few interesting thing you can do. This example mixes in two extensions. **AutomaticFilters** defines filters (where clauses) using configuration. In this case were are filtering to states that start with the letter C. **CompareRecipe** mixes in results from another recipe. In this case, we are using this comparison recipe to calculate an average age across all states. .. code:: python recipe_yaml = yaml.load(r) recipe = Recipe.from_config(s, recipe_yaml, session=oven.Session(), extension_classes=(AutomaticFilters, CompareRecipe))\ .automatic_filters({'state__like': 'C%'})\ .compare(Recipe(shelf=s, session=oven.Session()).metrics('age')) print(recipe.to_sql()) print() print(recipe.dataset.csv) The output looks like this .. code:: SQL SELECT census.state AS state, CAST(sum(census.age * census.pop2000) AS FLOAT) / (coalesce(CAST(sum(census.pop2000) AS FLOAT), 0.0) + 1e-09) AS age, sum(census.pop2000) AS population, avg(anon_1.age) AS age_compare FROM census LEFT OUTER JOIN (SELECT CAST(sum(census.age * census.pop2000) AS FLOAT) / (coalesce(CAST(sum(census.pop2000) AS FLOAT), 0.0) + 1e-09) AS age FROM census) AS anon_1 ON 1=1 WHERE census.state LIKE 'C%' GROUP BY census.state ORDER BY CAST(sum(census.age * census.pop2000) AS FLOAT) / (coalesce(CAST(sum(census.pop2000) AS FLOAT), 0.0) + 1e-09) DESC state,age,population,age_compare,state_id Connecticut,37.19867141455273,3403620,35.789568740450036,Connecticut Colorado,34.5386073584527,4300877,35.789568740450036,Colorado California,34.17872597484759,33829442,35.789568740450036,California Now, go check out the :ref:`API Documentation ` or look at an :ref:`concepts_overview`.