PyPika 0.27.0

A SQL query builder API for Python

PyPika - Python Query Builder
=============================

.. _intro_start:

|BuildStatus| |CoverageStatus| |Codacy| |Docs| |PyPi| |License|

Abstract
--------

What is |Brand|?

|Brand| is a Python API for building SQL queries. The motivation behind |Brand| is to provide a simple interface for
building SQL queries without limiting the flexibility of handwritten SQL. Designed with data analysis in mind, |Brand|
leverages the builder design pattern to construct queries to avoid messy string formatting and concatenation. It is also
easily extended to take full advantage of specific features of SQL database vendors.

What are the design goals for |Brand|?
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

|Brand| is a fast, expressive and flexible way to replace handwritten SQL (or even ORM for the courageous souls amongst you).
Validation of SQL correctness is not an explicit goal of |Brand|. With such a large number of
SQL database vendors providing a robust validation of input data is difficult. Instead you are encouraged to check inputs you provide to |Brand| or appropriately handle errors raised from
your SQL database - just as you would have if you were writing SQL yourself.

.. _intro_end:

Read the docs: http://pypika.readthedocs.io/en/latest/

Installation
------------

.. _installation_start:

|Brand| supports python ``2.7`` and ``3.3+``. It may also work on pypy, cython, and jython, but is not being tested for these versions.

To install |Brand| run the following command:

.. code-block:: bash

pip install pypika


.. _installation_end:


Tutorial
--------

.. _tutorial_start:

The main classes in pypika are ``pypika.Query``, ``pypika.Table``, and ``pypika.Field``.

.. code-block:: python

from pypika import Query, Table, Field


Selecting Data
^^^^^^^^^^^^^^

The entry point for building queries is ``pypika.Query``. In order to select columns from a table, the table must
first be added to the query. For simple queries with only one table, tables and columns can be references using
strings. For more sophisticated queries a ``pypika.Table`` must be used.

.. code-block:: python

q = Query.from_('customers').select('id', 'fname', 'lname', 'phone')

To convert the query into raw SQL, it can be cast to a string.

.. code-block:: python

str(q)

Alternatively, you can use the `Query.get_sql()` function:

.. code-block:: python

q.get_sql()

Using ``pypika.Table``

.. code-block:: python

customers = Table('customers')
q = Query.from_(customers).select(customers.id, customers.fname, customers.lname, customers.phone)

Both of the above examples result in the following SQL:

.. code-block:: sql

SELECT id,fname,lname,phone FROM customers

Results can be ordered by using the following syntax:

.. code-block:: python

from pypika import Order
Query.from_('customers').select('id', 'fname', 'lname', 'phone').orderby('id', order=Order.desc)

This results in the following SQL:

.. code-block:: sql

SELECT "id","fname","lname","phone" FROM "customers" ORDER BY "id" DESC

Arithmetic
""""""""""

Arithmetic expressions can also be constructed using pypika. Operators such as `+`, `-`, `*`, and `/` are implemented
by ``pypika.Field`` which can be used simply with a ``pypika.Table`` or directly.

.. code-block:: python

from pypika import Field

q = Query.from_('account').select(
Field('revenue') - Field('cost')
)

.. code-block:: sql

SELECT revenue-cost FROM accounts

Using ``pypika.Table``

.. code-block:: python

accounts = Table('accounts')
q = Query.from_(accounts).select(
accounts.revenue - accounts.cost
)

.. code-block:: sql

SELECT revenue-cost FROM accounts

An alias can also be used for fields and expressions.

.. code-block:: sql

q = Query.from_(accounts).select(
(accounts.revenue - accounts.cost).as_('profit')
)

.. code-block:: sql

SELECT revenue-cost profit FROM accounts

More arithmetic examples

.. code-block:: python

table = Table('table')
q = Query.from_(table).select(
table.foo + table.bar,
table.foo - table.bar,
table.foo * table.bar,
table.foo / table.bar,
(table.foo+table.bar) / table.fiz,
)

.. code-block:: sql

SELECT foo+bar,foo-bar,foo*bar,foo/bar,(foo+bar)/fiz FROM table


Filtering
"""""""""

Queries can be filtered with ``pypika.Criterion`` by using equality or inequality operators

.. code-block:: python

customers = Table('customers')
q = Query.from_(customers).select(
customers.id, customers.fname, customers.lname, customers.phone
).where(
customers.lname == 'Mustermann'
)

.. code-block:: sql

SELECT id,fname,lname,phone FROM customers WHERE lname='Mustermann'

Query methods such as select, where, groupby, and orderby can be called multiple times. Multiple calls to the where
method will add additional conditions as

.. code-block:: python

customers = Table('customers')
q = Query.from_(customers).select(
customers.id, customers.fname, customers.lname, customers.phone
).where(
customers.fname == 'Max'
).where(
customers.lname == 'Mustermann'
)

.. code-block:: sql

SELECT id,fname,lname,phone FROM customers WHERE fname='Max' AND lname='Mustermann'

Filters such as IN and BETWEEN are also supported

.. code-block:: python

customers = Table('customers')
q = Query.from_(customers).select(
customers.id,customers.fname
).where(
customers.age[18:65] & customers.status.isin(['new', 'active'])
)

.. code-block:: sql

SELECT id,fname FROM customers WHERE age BETWEEN 18 AND 65 AND status IN ('new','active')

Filtering with complex criteria can be created using boolean symbols ``&``, ``|``, and ``^``.

AND

.. code-block:: python

customers = Table('customers')
q = Query.from_(customers).select(
customers.id, customers.fname, customers.lname, customers.phone
).where(
(customers.age >= 18) & (customers.lname == 'Mustermann')
)

.. code-block:: sql

SELECT id,fname,lname,phone FROM customers WHERE age>=18 AND lname='Mustermann'

OR

.. code-block:: python

customers = Table('customers')
q = Query.from_(customers).select(
customers.id, customers.fname, customers.lname, customers.phone
).where(
(customers.age >= 18) | (customers.lname == 'Mustermann')
)

.. code-block:: sql

SELECT id,fname,lname,phone FROM customers WHERE age>=18 OR lname='Mustermann'

XOR

.. code-block:: python

customers = Table('customers')
q = Query.from_(customers).select(
customers.id, customers.fname, customers.lname, customers.phone
).where(
(customers.age >= 18) ^ customers.is_registered
)

.. code-block:: sql

SELECT id,fname,lname,phone FROM customers WHERE age>=18 XOR is_registered


Convenience Methods
"""""""""""""""""""

In the `Criterion` class, there are the static methods `any` and `all` that allow building chains AND and OR expressions with a list of terms.

.. code-block:: python

from pypika import Criterion

customers = Table('customers')
q = Query.from_(customers).select(
customers.id,
customers.fname
).where(
Criterion.all([
customers.is_registered,
customers.age >= 18,
customers.lname == "Jones",
])
)

.. code-block:: sql

SELECT id,fname FROM customers WHERE is_registered AND age>=18 AND lname = "Jones"


Grouping and Aggregating
""""""""""""""""""""""""

Grouping allows for aggregated results and works similar to ``SELECT`` clauses.

.. code-block:: python

from pypika import functions as fn

customers = Table('customers')
q = Query.from_(customers).where(
customers.age >= 18
).groupby(
customers.id
).select(
customers.id, fn.Sum(customers.revenue)
)

.. code-block:: sql

SELECT id,SUM(revenue) FROM customers WHERE age>=18 GROUP BY id ORDER BY id ASC

After adding a ``GROUP BY`` clause to a query, the ``HAVING`` clause becomes available. The method
``Query.having()`` takes a ``Criterion`` parameter similar to the method ``Query.where()``.

.. code-block:: python

from pypika import functions as fn

payments = Table('payments')
q = Query.from_(payments).where(
payments.transacted[date(2015, 1, 1):date(2016, 1, 1)]
).groupby(
payments.customer_id
).having(
fn.Sum(payments.total) >= 1000
).select(
payments.customer_id, fn.Sum(payments.total)
)

.. code-block:: sql

SELECT customer_id,SUM(total) FROM payments
WHERE transacted BETWEEN '2015-01-01' AND '2016-01-01'
GROUP BY customer_id HAVING SUM(total)>=1000


Joining Tables and Subqueries
"""""""""""""""""""""""""""""

Tables and subqueries can be joined to any query using the ``Query.join()`` method. Joins can be performed with either
a ``USING`` or ``ON`` clauses. The ``USING`` clause can be used when both tables/subqueries contain the same field and
the ``ON`` clause can be used with a criterion. To perform a join, ``...join()`` can be chained but then must be
followed immediately by ``...on(<criterion>)`` or ``...using(*field)``.

Example of a join using `ON`
~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. code-block:: python

history, customers = Tables('history', 'customers')
q = Query.from_(history).join(
customers
).on(
history.customer_id == customers.id
).select(
history.star
).where(
customers.id == 5
)


.. code-block:: sql

SELECT "history".* FROM "history" JOIN "customers" ON "history"."customer_id"="customers"."id" WHERE "customers"."id"=5

As a shortcut, the ``Query.join().on_field()`` function is provided for joining the (first) table in the ``FROM`` clause
with the joined table when the field name(s) are the same in both tables.

Example of a join using `ON`
~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. code-block:: python

history, customers = Tables('history', 'customers')
q = Query.from_(history).join(
customers
).on_field(
'customer_id', 'group'
).select(
history.star
).where(
customers.group == 'A'
)


.. code-block:: sql

SELECT "history".* FROM "history" JOIN "customers" ON "history"."customer_id"="customers"."customer_id" AND "history"."group"="customers"."group" WHERE "customers"."group"='A'


Example of a join using `USING`
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. code-block:: python

history, customers = Tables('history', 'customers')
q = Query.from_(history).join(
customers
).on(
'customer_id'
).select(
history.star
).where(
customers.id == 5
)


.. code-block:: sql

SELECT "history".* FROM "history" JOIN "customers" USING "customer_id" WHERE "customers"."id"=5


Example of a correlated subquery in the `SELECT`
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. code-block:: python
history, customers = Tables('history', 'customers')
last_purchase_at = Query.from_(history).select(
history.purchase_at
).where(history.customer_id==customers.customer_id).orderby(
history.purchase_at, order=Order.desc
).limit(1)
q = Query.from_(customers).select(
customers.id, last_purchase_at._as('last_purchase_at')
)
.. code-block:: sql

SELECT
"id",
(SELECT "history"."purchase_at"
FROM "history"
WHERE "history"."customer_id" = "customers"."customer_id"
ORDER BY "history"."purchase_at" DESC
LIMIT 1) "last_purchase_at"
FROM "customers"


Unions
""""""

Both ``UNION`` and ``UNION ALL`` are supported. ``UNION DISTINCT`` is synonomous with "UNION`` so and |Brand| does not
provide a separate function for it. Unions require that queries have the same number of ``SELECT`` clauses so
trying to cast a unioned query to string with through a ``UnionException`` if the column sizes are mismatched.

To create a union query, use either the ``Query.union()`` method or `+` operator with two query instances. For a
union all, use ``Query.union_all()`` or the `*` operator.

.. code-block:: python

provider_a, provider_b = Tables('provider_a', 'provider_b')
q = Query.from_(provider_a).select(
provider_a.created_time, provider_a.foo, provider_a.bar
) + Query.from_(provider_b).select(
provider_b.created_time, provider_b.fiz, provider_b.buz
)

.. code-block:: sql

SELECT "created_time","foo","bar" FROM "provider_a" UNION SELECT "created_time","fiz","buz" FROM "provider_b"


Date, Time, and Intervals
"""""""""""""""""""""""""

Using ``pypika.Interval``, queries can be constructed with date arithmetic. Any combination of intervals can be
used except for weeks and quarters, which must be used separately and will ignore any other values if selected.

.. code-block:: python

from pypika import functions as fn

fruits = Tables('fruits')
q = Query.from_(fruits) \
.select(fruits.id, fruits.name) \
.where(fruits.harvest_date + Interval(months=1) < fn.Now())

.. code-block:: sql

SELECT id,name FROM fruits WHERE harvest_date+INTERVAL 1 MONTH<NOW()


Tuples
""""""

Tuples are supported through the class ``pypika.Tuple`` but also through the native python tuple wherever possible.
Tuples can be used with ``pypika.Criterion`` in **WHERE** clauses for pairwise comparisons.

.. code-block:: python

from pypika import Query, Tuple

q = Query.from_(self.table_abc) \
.select(self.table_abc.foo, self.table_abc.bar) \
.where(Tuple(self.table_abc.foo, self.table_abc.bar) == Tuple(1, 2))

.. code-block:: sql

SELECT "foo","bar" FROM "abc" WHERE ("foo","bar")=(1,2)

Using ``pypika.Tuple`` on both sides of the comparison is redundant and |Brand| supports native python tuples.

.. code-block:: python

from pypika import Query, Tuple

q = Query.from_(self.table_abc) \
.select(self.table_abc.foo, self.table_abc.bar) \
.where(Tuple(self.table_abc.foo, self.table_abc.bar) == (1, 2))

.. code-block:: sql

SELECT "foo","bar" FROM "abc" WHERE ("foo","bar")=(1,2)

Tuples can be used in **IN** clauses.

.. code-block:: python

Query.from_(self.table_abc) \
.select(self.table_abc.foo, self.table_abc.bar) \
.where(Tuple(self.table_abc.foo, self.table_abc.bar).isin([(1, 1), (2, 2), (3, 3)]))

.. code-block:: sql

SELECT "foo","bar" FROM "abc" WHERE ("foo","bar") IN ((1,1),(2,2),(3,3))


Strings Functions
"""""""""""""""""

There are several string operations and function wrappers included in |Brand|. Function wrappers can be found in the
``pypika.functions`` package. In addition, `LIKE` and `REGEX` queries are supported as well.

.. code-block:: python

from pypika import functions as fn

customers = Tables('customers')
q = Query.from_(customers).select(
customers.id,
customers.fname,
customers.lname,
).where(
customers.lname.like('Mc%')
)

.. code-block:: sql

SELECT id,fname,lname FROM customers WHERE lname LIKE 'Mc%'

.. code-block:: python

from pypika import functions as fn

customers = Tables('customers')
q = Query.from_(customers).select(
customers.id,
customers.fname,
customers.lname,
).where(
customers.lname.regex(r'^[abc][a-zA-Z]+&')
)

.. code-block:: sql

SELECT id,fname,lname FROM customers WHERE lname REGEX '^[abc][a-zA-Z]+&';


.. code-block:: python

from pypika import functions as fn

customers = Tables('customers')
q = Query.from_(customers).select(
customers.id,
fn.Concat(customers.fname, ' ', customers.lname).as_('full_name'),
)

.. code-block:: sql

SELECT id,CONCAT(fname, ' ', lname) full_name FROM customers


Case Statements
"""""""""""""""

Case statements allow fow a number of conditions to be checked sequentially and return a value for the first condition
met or otherwise a default value. The Case object can be used to chain conditions together along with their output
using the ``when`` method and to set the default value using ``else_``.


.. code-block:: python

from pypika import Case, functions as fn

customers = Tables('customers')
q = Query.from_(customers).select(
customers.id,
Case()
.when(customers.fname == "Tom", "It was Tom")
.when(customers.fname == "John", "It was John")
.else_("It was someone else.").as_('who_was_it')
)


.. code-block:: sql

SELECT "id",CASE WHEN "fname"='Tom' THEN 'It was Tom' WHEN "fname"='John' THEN 'It was John' ELSE 'It was someone else.' END "who_was_it" FROM "customers"


Inserting Data
^^^^^^^^^^^^^^

Data can be inserted into tables either by providing the values in the query or by selecting them through another query.

By default, data can be inserted by providing values for all columns in the order that they are defined in the table.

Insert with values
""""""""""""""""""

.. code-block:: python

customers = Table('customers')

q = Query.into(customers).insert(1, 'Jane', 'Doe', '[e-mail]')

.. code-block:: sql

INSERT INTO customers VALUES (1,'Jane','Doe','[e-mail]')

Multiple rows of data can be inserted either by chaining the ``insert`` function or passing multiple tuples as args.

.. code-block:: python

customers = Table('customers')

q = Query.into(customers).insert(1, 'Jane', 'Doe', '[e-mail]').insert(2, 'John', 'Doe', '[e-mail]')

.. code-block:: python

customers = Table('customers')

q = Query.into(customers).insert((1, 'Jane', 'Doe', '[e-mail]'),
(2, 'John', 'Doe', '[e-mail]'))

Insert with on Duplicate Key Update
"""""""""""""""""""""""""""""""""""

.. code-block:: python

customers = Table('customers')

q = Query.into(customers)\
.insert(1, 'Jane', 'Doe', '[e-mail]')\
.on_duplicate_key_update(customers.email, Values(customers.email))

.. code-block:: sql

INSERT INTO customers VALUES (1,'Jane','Doe','[e-mail]') ON DUPLICATE KEY UPDATE `email`=VALUES(`email`)

``.on_duplicate_key_update`` works similar to ``.set`` for updating rows, additionally it provides the ``Values``
wrapper to update to the value specified in the ``INSERT`` clause.


Insert from a SELECT Sub-query
""""""""""""""""""""""""""""""

.. code-block:: sql

INSERT INTO customers VALUES (1,'Jane','Doe','[e-mail]'),(2,'John','Doe','[e-mail]')


To specify the columns and the order, use the ``columns`` function.

.. code-block:: python

customers = Table('customers')

q = Query.into(customers).columns('id', 'fname', 'lname').insert(1, 'Jane', 'Doe')

.. code-block:: sql

INSERT INTO customers (id,fname,lname) VALUES (1,'Jane','Doe','[e-mail]')


Inserting data with a query works the same as querying data with the additional call to the ``into`` method in the
builder chain.

.. code-block:: python

customers, customers_backup = Tables('customers', 'customers_backup')

q = Query.into(customers_backup).from_(customers).select('*')

.. code-block:: sql

INSERT INTO customers_backup SELECT * FROM customers

.. code-block:: python

customers, customers_backup = Tables('customers', 'customers_backup')

q = Query.into(customers_backup).columns('id', 'fname', 'lname')
.from_(customers).select(customers.id, customers.fname, customers.lname)

.. code-block:: sql

INSERT INTO customers_backup SELECT "id", "fname", "lname" FROM customers

The syntax for joining tables is the same as when selecting data

.. code-block:: python

customers, orders, orders_backup = Tables('customers', 'orders', 'orders_backup')

q = Query.into(orders_backup).columns('id', 'address', 'customer_fname', 'customer_lname')
.from_(customers)
.join(orders).on(orders.customer_id == customers.id)
.select(orders.id, customers.fname, customers.lname)

.. code-block:: sql

INSERT INTO "orders_backup" ("id","address","customer_fname","customer_lname")
SELECT "orders"."id","customers"."fname","customers"."lname" FROM "customers"
JOIN "orders" ON "orders"."customer_id"="customers"."id"

Updating Data
^^^^^^^^^^^^^^
PyPika allows update queries to be constructed with or without where clauses.

.. code-block:: python

customers = Table('customers')

Query.update(customers).set(customers.last_login, '2017-01-01 10:00:00')

Query.update(customers).set(customers.lname, smith).where(customers.id == 10)

.. code-block:: sql

UPDATE "customers" SET "last_login"='2017-01-01 10:00:00'

UPDATE "customers" SET "lname"='smith' WHERE "id"=10

The syntax for joining tables is the same as when selecting data

.. code-block:: python

customers, profiles = Tables('customers', 'profiles')

Query.update(customers)
.join(profiles).on(profiles.customer_id == customers.id)
.set(customers.lname, profiles.lname)

.. code-block:: sql

UPDATE "customers"
JOIN "profiles" ON "profiles"."customer_id"="customers"."id"
SET "customers"."lname"="profiles"."lname"

Parametrized Queries
^^^^^^^^^^^^^^^^^^^^

PyPika allows you to use ``Parameter(str)`` term as a placeholder for parametrized queries.

.. code-block:: python

customers = Table('customers')

q = Query.into(customers).columns('id', 'fname', 'lname')
.insert(Parameter(':1'), Parameter(':2'), Parameter(':3'))

.. code-block:: sql

INSERT INTO customers (id,fname,lname) VALUES (:1,:2,:3)

This allows you to build prepared statements, and/or avoid SQL-injection related risks.

Due to the mix of syntax for parameters, depending on connector/driver, it is required that you specify the parameter token explicitly.

An example of some common SQL parameter styles used in Python drivers are:

PostgreSQL:
``$number`` OR ``%s`` + ``:name`` (depending on driver)
MySQL:
``%s``
SQLite:
``?``
Vertica:
``:name``
Oracle:
``:number`` + ``:name``
MSSQL:
``%(name)s`` OR ``:name`` + ``:number`` (depending on driver)

You can find out what parameter style is needed for DBAPI compliant drivers here: https://www.python.org/dev/peps/pep-0249/#paramstyle or in the DB driver documentation.


.. _tutorial_end:


.. _license_start:


License
-------

Copyright 2016 KAYAK Germany, GmbH

Licensed 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.


Crafted with ♥ in Berlin.

.. _license_end:


.. _appendix_start:

.. |Brand| replace:: *PyPika*

.. _appendix_end:

.. _available_badges_start:

.. |BuildStatus| image:: https://travis-ci.org/kayak/pypika.svg?branch=master
:target: https://travis-ci.org/kayak/pypika
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:target: https://coveralls.io/github/kayak/pypika?branch=master
.. |Codacy| image:: https://api.codacy.com/project/badge/Grade/6d7e44e5628b4839a23da0bd82eaafcf
:target: https://www.codacy.com/app/twheys/pypika
.. |Docs| image:: https://readthedocs.org/projects/pypika/badge/?version=latest
:target: http://pypika.readthedocs.io/en/latest/
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.. _available_badges_end: