Research
Security News
Malicious npm Package Targets Solana Developers and Hijacks Funds
A malicious npm package targets Solana developers, rerouting funds in 2% of transactions to a hardcoded address.
By Socket Research Team - Dec 02, 2024
Huge News!Announcing our $40M Series B led by Abstract Ventures.Learn More →
- Maintainers
- 1
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 3.6+. 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()
Tables, Columns, Schemas, and Databases ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
In simple queries like the above example, columns in the "from" table can be referenced by passing string names into
the select query builder function. In more complex examples, the pypika.Table class should be used. Columns can be
referenced as attributes on instances of pypika.Table.
.. code-block:: python
from pypika import Table, Query
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
An alias for the table can be given using the .as_ function on pypika.Table
.. code-block:: sql
customers = Table('x_view_customers').as_('customers')
q = Query.from_(customers).select(customers.id, customers.phone)
.. code-block:: sql
SELECT id,phone FROM x_view_customers customers
A schema can also be specified. Tables can be referenced as attributes on the schema.
.. code-block:: sql
from pypika import Table, Query, Schema
views = Schema('views')
q = Query.from_(views.customers).select(customers.id, customers.phone)
.. code-block:: sql
SELECT id,phone FROM views.customers
Also references to databases can be used. Schemas can be referenced as attributes on the database.
.. code-block:: sql
from pypika import Table, Query, Database
my_db = Database('my_db')
q = Query.from_(my_db.analytics.customers).select(customers.id, customers.phone)
.. code-block:: sql
SELECT id,phone FROM my_db.analytics.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"
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).
Join Types
All join types are supported by |Brand|.
.. code-block:: python
Query \
.from_(base_table)
...
.join(join_table, JoinType.left)
...
.. code-block:: python
Query \
.from_(base_table)
...
.left_join(join_table) \
.left_outer_join(join_table) \
.right_join(join_table) \
.right_outer_join(join_table) \
.inner_join(join_table) \
.outer_join(join_table) \
.full_outer_join(join_table) \
.cross_join(join_table) \
.hash_join(join_table) \
...
See the list of join types here ``pypika.enums.JoinTypes``
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) \
.using('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 synonymous with "UNION`` so |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 will throw a ``SetOperationException`` 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"
Intersect
"""""""""
``INTERSECT`` is supported. Intersects require that queries have the same number of ``SELECT`` clauses so
trying to cast a intersected query to string will throw a ``SetOperationException`` if the column sizes are mismatched.
To create a intersect query, use the ``Query.intersect()`` method.
.. 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
)
r = Query.from_(provider_b).select(
provider_b.created_time, provider_b.fiz, provider_b.buz
)
intersected_query = q.intersect(r)
.. code-block:: sql
SELECT "created_time","foo","bar" FROM "provider_a" INTERSECT SELECT "created_time","fiz","buz" FROM "provider_b"
Minus
"""""
``MINUS`` is supported. Minus require that queries have the same number of ``SELECT`` clauses so
trying to cast a minus query to string will throw a ``SetOperationException`` if the column sizes are mismatched.
To create a minus query, use either the ``Query.minus()`` method or `-` operator with two query instances.
.. 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
)
r = Query.from_(provider_b).select(
provider_b.created_time, provider_b.fiz, provider_b.buz
)
minus_query = q.minus(r)
(or)
minus_query = 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" MINUS SELECT "created_time","fiz","buz" FROM "provider_b"
EXCEPT
""""""
``EXCEPT`` is supported. Minus require that queries have the same number of ``SELECT`` clauses so
trying to cast a except query to string will throw a ``SetOperationException`` if the column sizes are mismatched.
To create a except query, use the ``Query.except_of()`` method.
.. 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
)
r = Query.from_(provider_b).select(
provider_b.created_time, provider_b.fiz, provider_b.buz
)
minus_query = q.except_of(r)
.. code-block:: sql
SELECT "created_time","foo","bar" FROM "provider_a" EXCEPT 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
Custom Functions
""""""""""""""""
Custom Functions allows us to use any function on queries, as some functions are not covered by PyPika as default, we can appeal
to Custom functions.
.. code-block:: python
from pypika import CustomFunction
customers = Tables('customers')
DateDiff = CustomFunction('DATE_DIFF', ['interval', 'start_date', 'end_date'])
q = Query.from_(customers).select(
customers.id,
customers.fname,
customers.lname,
DateDiff('day', customers.created_date, customers.updated_date)
)
.. code-block:: sql
SELECT id,fname,lname,DATE_DIFF('day',created_date,updated_date) 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"
With Clause
"""""""""""""""
With clause allows give a sub-query block a name, which can be referenced in several places within the main SQL query.
The SQL WITH clause is basically a drop-in replacement to the normal sub-query.
.. code-block:: python
from pypika import Table, AliasedQuery, Query
customers = Table('customers')
sub_query = (Query
.from_(customers)
.select('*'))
test_query = (Query
.with_(sub_query, "an_alias")
.from_(AliasedQuery("an_alias"))
.select('*'))
You can use as much as `.with_()` as you want.
.. code-block:: sql
WITH an_alias AS (SELECT * FROM "customers") SELECT * FROM an_alias
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', 'jane@example.com')
.. code-block:: sql
INSERT INTO customers VALUES (1,'Jane','Doe','jane@example.com')
.. code-block:: python
customers = Table('customers')
q = customers.insert(1, 'Jane', 'Doe', 'jane@example.com')
.. code-block:: sql
INSERT INTO customers VALUES (1,'Jane','Doe','jane@example.com')
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', 'jane@example.com').insert(2, 'John', 'Doe', 'john@example.com')
.. code-block:: python
customers = Table('customers')
q = Query.into(customers).insert((1, 'Jane', 'Doe', 'jane@example.com'),
(2, 'John', 'Doe', 'john@example.com'))
Insert with constraint violation handling
"""""""""""""""""""""""""""""""""""""""""
MySQL
~~~~~
.. code-block:: python
customers = Table('customers')
q = MySQLQuery.into(customers) \
.insert(1, 'Jane', 'Doe', 'jane@example.com') \
.on_duplicate_key_ignore())
.. code-block:: sql
INSERT INTO `customers` VALUES (1,'Jane','Doe','jane@example.com') ON DUPLICATE KEY IGNORE
.. code-block:: python
customers = Table('customers')
q = MySQLQuery.into(customers) \
.insert(1, 'Jane', 'Doe', 'jane@example.com') \
.on_duplicate_key_update(customers.email, Values(customers.email))
.. code-block:: sql
INSERT INTO `customers` VALUES (1,'Jane','Doe','jane@example.com') 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.
PostgreSQL
~~~~~~~~~~
.. code-block:: python
customers = Table('customers')
q = PostgreSQLQuery.into(customers) \
.insert(1, 'Jane', 'Doe', 'jane@example.com') \
.on_conflict(customers.email) \
.do_nothing()
.. code-block:: sql
INSERT INTO "customers" VALUES (1,'Jane','Doe','jane@example.com') ON CONFLICT ("email") DO NOTHING
.. code-block:: python
customers = Table('customers')
q = PostgreSQLQuery.into(customers) \
.insert(1, 'Jane', 'Doe', 'jane@example.com') \
.on_conflict(customers.email) \
.do_update(customers.email, 'bob@example.com')
.. code-block:: sql
INSERT INTO "customers" VALUES (1,'Jane','Doe','jane@example.com') ON CONFLICT ("email") DO UPDATE SET "email"='bob@example.com'
Insert from a SELECT Sub-query
""""""""""""""""""""""""""""""
.. code-block:: sql
INSERT INTO "customers" VALUES (1,'Jane','Doe','jane@example.com'),(2,'John','Doe','john@example.com')
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','jane@example.com')
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"
Using ``pypika.Table`` alias to perform the update
.. code-block:: python
customers = Table('customers')
customers.update()
.set(customers.lname, 'smith')
.where(customers.id == 10)
.. code-block:: sql
UPDATE "customers" SET "lname"='smith' WHERE "id"=10
Using ``limit`` for performing update
.. code-block:: python
customers = Table('customers')
customers.update()
.set(customers.lname, 'smith')
.limit(2)
.. code-block:: sql
UPDATE "customers" SET "lname"='smith' LIMIT 2
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 or use one of the specialized Parameter types per [PEP-0249](https://www.python.org/dev/peps/pep-0249/#paramstyle):
``QmarkParameter()``, ``NumericParameter(int)``, ``NamedParameter(str)``, ``FormatParameter()``, ``PyformatParameter(str)``
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.
Temporal support
^^^^^^^^^^^^^^^^
Temporal criteria can be added to the tables.
Select
""""""
Here is a select using system time.
.. code-block:: python
t = Table("abc")
q = Query.from_(t.for_(SYSTEM_TIME.as_of('2020-01-01'))).select("*")
This produces:
.. code-block:: sql
SELECT * FROM "abc" FOR SYSTEM_TIME AS OF '2020-01-01'
You can also use between.
.. code-block:: python
t = Table("abc")
q = Query.from_(
t.for_(SYSTEM_TIME.between('2020-01-01', '2020-02-01'))
).select("*")
This produces:
.. code-block:: sql
SELECT * FROM "abc" FOR SYSTEM_TIME BETWEEN '2020-01-01' AND '2020-02-01'
You can also use a period range.
.. code-block:: python
t = Table("abc")
q = Query.from_(
t.for_(SYSTEM_TIME.from_to('2020-01-01', '2020-02-01'))
).select("*")
This produces:
.. code-block:: sql
SELECT * FROM "abc" FOR SYSTEM_TIME FROM '2020-01-01' TO '2020-02-01'
Finally you can select for all times:
.. code-block:: python
t = Table("abc")
q = Query.from_(t.for_(SYSTEM_TIME.all_())).select("*")
This produces:
.. code-block:: sql
SELECT * FROM "abc" FOR SYSTEM_TIME ALL
A user defined period can also be used in the following manner.
.. code-block:: python
t = Table("abc")
q = Query.from_(
t.for_(t.valid_period.between('2020-01-01', '2020-02-01'))
).select("*")
This produces:
.. code-block:: sql
SELECT * FROM "abc" FOR "valid_period" BETWEEN '2020-01-01' AND '2020-02-01'
Joins
"""""
With joins, when the table object is used when specifying columns, it is
important to use the table from which the temporal constraint was generated.
This is because `Table("abc")` is not the same table as `Table("abc").for_(...)`.
The following example demonstrates this.
.. code-block:: python
t0 = Table("abc").for_(SYSTEM_TIME.as_of('2020-01-01'))
t1 = Table("efg").for_(SYSTEM_TIME.as_of('2020-01-01'))
query = (
Query.from_(t0)
.join(t1)
.on(t0.foo == t1.bar)
.select("*")
)
This produces:
.. code-block:: sql
SELECT * FROM "abc" FOR SYSTEM_TIME AS OF '2020-01-01'
JOIN "efg" FOR SYSTEM_TIME AS OF '2020-01-01'
ON "abc"."foo"="efg"."bar"
Update & Deletes
""""""""""""""""
An update can be written as follows:
.. code-block:: python
t = Table("abc")
q = Query.update(
t.for_portion(
SYSTEM_TIME.from_to('2020-01-01', '2020-02-01')
)
).set("foo", "bar")
This produces:
.. code-block:: sql
UPDATE "abc"
FOR PORTION OF SYSTEM_TIME FROM '2020-01-01' TO '2020-02-01'
SET "foo"='bar'
Here is a delete:
.. code-block:: python
t = Table("abc")
q = Query.from_(
t.for_portion(t.valid_period.from_to('2020-01-01', '2020-02-01'))
).delete()
This produces:
.. code-block:: sql
DELETE FROM "abc"
FOR PORTION OF "valid_period" FROM '2020-01-01' TO '2020-02-01'
Creating Tables
^^^^^^^^^^^^^^^
The entry point for creating tables is ``pypika.Query.create_table``, which is used with the class ``pypika.Column``.
As with selecting data, first the table should be specified. This can be either a
string or a `pypika.Table`. Then the columns, and constraints. Here's an example
that demonstrates much of the functionality.
.. code-block:: python
stmt = Query \
.create_table("person") \
.columns(
Column("id", "INT", nullable=False),
Column("first_name", "VARCHAR(100)", nullable=False),
Column("last_name", "VARCHAR(100)", nullable=False),
Column("phone_number", "VARCHAR(20)", nullable=True),
Column("status", "VARCHAR(20)", nullable=False, default=ValueWrapper("NEW")),
Column("date_of_birth", "DATETIME")) \
.unique("last_name", "first_name") \
.primary_key("id")
This produces:
.. code-block:: sql
CREATE TABLE "person" (
"id" INT NOT NULL,
"first_name" VARCHAR(100) NOT NULL,
"last_name" VARCHAR(100) NOT NULL,
"phone_number" VARCHAR(20) NULL,
"status" VARCHAR(20) NOT NULL DEFAULT 'NEW',
"date_of_birth" DATETIME,
UNIQUE ("last_name","first_name"),
PRIMARY KEY ("id")
)
There is also support for creating a table from a query.
.. code-block:: python
stmt = Query.create_table("names").as_select(
Query.from_("person").select("last_name", "first_name")
)
This produces:
.. code-block:: sql
CREATE TABLE "names" AS (SELECT "last_name","first_name" FROM "person")
Managing Table Indices
^^^^^^^^^^^^^^^^^^^^^^
Create Indices
""""""""""""""""
The entry point for creating indices is ``pypika.Query.create_index``.
An index name (as ``str``) or a ``pypika.terms.Index`` a table (as ``str`` or ``pypika.Table``) and
columns (as ``pypika.Column``) must be specified.
.. code-block:: python
my_index = Index("my_index")
person = Table("person")
stmt = Query \
.create_index(my_index) \
.on(person) \
.columns(person.first_name, person.last_name)
This produces:
.. code-block:: sql
CREATE INDEX my_index
ON person (first_name, last_name)
It is also possible to create a unique index
.. code-block:: python
my_index = Index("my_index")
person = Table("person")
stmt = Query \
.create_index(my_index) \
.on(person) \
.columns(person.first_name, person.last_name) \
.unique()
This produces:
.. code-block:: sql
CREATE UNIQUE INDEX my_index
ON person (first_name, last_name)
It is also possible to create an index if it does not exist
.. code-block:: python
my_index = Index("my_index")
person = Table("person")
stmt = Query \
.create_index(my_index) \
.on(person) \
.columns(person.first_name, person.last_name) \
.if_not_exists()
This produces:
.. code-block:: sql
CREATE INDEX IF NOT EXISTS my_index
ON person (first_name, last_name)
Drop Indices
""""""""""""""""
Then entry point for dropping indices is ``pypika.Query.drop_index``.
It takes either ``str`` or ``pypika.terms.Index`` as an argument.
.. code-block:: python
my_index = Index("my_index")
stmt = Query.drop_index(my_index)
This produces:
.. code-block:: sql
DROP INDEX my_index
It is also possible to drop an index if it exists
.. code-block:: python
my_index = Index("my_index")
stmt = Query.drop_index(my_index).if_exists()
This produces:
.. code-block:: sql
DROP INDEX IF EXISTS my_index
Chaining Functions
^^^^^^^^^^^^^^^^^^
The ``QueryBuilder.pipe`` method gives a more readable alternative while chaining functions.
.. code-block:: python
# This
(
query
.pipe(func1, *args)
.pipe(func2, **kwargs)
.pipe(func3)
)
# Is equivalent to this
func3(func2(func1(query, *args), **kwargs))
Or for a more concrete example:
.. code-block:: python
from pypika import Field, Query, functions as fn
from pypika.queries import QueryBuilder
def filter_days(query: QueryBuilder, col, num_days: int) -> QueryBuilder:
if isinstance(col, str):
col = Field(col)
return query.where(col > fn.Now() - num_days)
def count_groups(query: QueryBuilder, *groups) -> QueryBuilder:
return query.groupby(*groups).select(*groups, fn.Count("*").as_("n_rows"))
base_query = Query.from_("table")
query = (
base_query
.pipe(filter_days, "date", num_days=7)
.pipe(count_groups, "col1", "col2")
)
This produces:
.. code-block:: sql
SELECT "col1","col2",COUNT(*) n_rows
FROM "table"
WHERE "date">NOW()-7
GROUP BY "col1","col2"
.. _tutorial_end:
.. _contributing_start:
Contributing
------------
We welcome community contributions to |Brand|. Please see the `contributing guide <6_contributing.html>`_ to more info.
.. _contributing_end:
.. _license_start:
License
-------
Copyright 2020 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://github.com/kayak/pypika/workflows/Unit%20Tests/badge.svg
:target: https://github.com/kayak/pypika/actions
.. |CoverageStatus| image:: https://coveralls.io/repos/kayak/pypika/badge.svg?branch=master
: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/
.. |PyPi| image:: https://img.shields.io/pypi/v/pypika.svg?style=flat
:target: https://pypi.python.org/pypi/pypika
.. |License| image:: https://img.shields.io/hexpm/l/plug.svg?maxAge=2592000
:target: http://www.apache.org/licenses/LICENSE-2.0
.. _available_badges_end:
FAQs
A SQL query builder API for Python
We found that PyPika-git demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
Related posts
Security News
Research
Typosquatting Cryptographic Libraries: Malicious npm Packages Threaten Crypto Developers with Keylogging and Wallet Theft
Socket researchers have discovered malicious npm packages targeting crypto developers, stealing credentials and wallet data using spyware delivered through typosquats of popular cryptographic libraries.
By Kirill Boychenko - Nov 27, 2024
Security News
Weekly Downloads Now Available in npm Package Search Results
Socket's package search now displays weekly downloads for npm packages, helping developers quickly assess popularity and make more informed decisions.
By Sarah Gooding - Nov 26, 2024