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===========
Emulate SQL
===========
agate's command structure is very similar to SQL. The primary difference between agate and SQL is that commands like :code:`SELECT` and :code:`WHERE` explicitly create new tables. You can chain them together as you would with SQL, but be aware each command is actually creating a new table.
.. note::
All examples in this section use the `PostgreSQL <https://www.postgresql.org/>`_ dialect for comparison.
If you want to read and write data from SQL, see :ref:`load_a_table_from_a_sql_database`.
SELECT
======
SQL:
.. code-block:: postgres
SELECT state, total FROM table;
agate:
.. code-block:: python
new_table = table.select(['state', 'total'])
WHERE
=====
SQL:
.. code-block:: postgres
SELECT * FROM table WHERE LOWER(state) = 'california';
agate:
.. code-block:: python
new_table = table.where(lambda row: row['state'].lower() == 'california')
ORDER BY
========
SQL:
.. code-block:: postgres
SELECT * FROM table ORDER BY total DESC;
agate:
.. code-block:: python
new_table = table.order_by(lambda row: row['total'], reverse=True)
DISTINCT
========
SQL:
.. code-block:: postgres
SELECT DISTINCT ON (state) * FROM table;
agate:
.. code-block:: python
new_table = table.distinct('state')
.. note::
Unlike most SQL implementations, agate always returns the full row. Use :meth:`.Table.select` if you want to filter the columns first.
INNER JOIN
==========
SQL (two ways):
.. code-block:: postgres
SELECT * FROM patient, doctor WHERE patient.doctor = doctor.id;
SELECT * FROM patient INNER JOIN doctor ON (patient.doctor = doctor.id);
agate:
.. code-block:: python
joined = patients.join(doctors, 'doctor', 'id', inner=True)
LEFT OUTER JOIN
===============
SQL:
.. code-block:: postgres
SELECT * FROM patient LEFT OUTER JOIN doctor ON (patient.doctor = doctor.id);
agate:
.. code-block:: python
joined = patients.join(doctors, 'doctor', 'id')
FULL OUTER JOIN
===============
SQL:
.. code-block:: postgres
SELECT * FROM patient FULL OUTER JOIN doctor ON (patient.doctor = doctor.id);
agate:
.. code-block:: python
joined = patients.join(doctors, 'doctor', 'id', full_outer=True)
GROUP BY
========
agate's :meth:`.Table.group_by` works slightly different than SQLs. It does not require an aggregate function. Instead it returns :py:class:`.TableSet`. To see how to perform the equivalent of a SQL aggregate, see below.
.. code-block:: python
doctors = patients.group_by('doctor')
You can group by two or more columns by chaining the command.
.. code-block:: python
doctors_by_state = patients.group_by('state').group_by('doctor')
HAVING
======
agate's :meth:`.TableSet.having` works very similar to SQL's keyword of the same name.
.. code-block:: python
doctors = patients.group_by('doctor')
popular_doctors = doctors.having([
('patient_count', Count())
], lambda t: t['patient_count'] > 100)
This filters to only those doctors whose table includes at least 100 results. Can add as many aggregations as you want to the list and each will be available, by name in the test function you pass.
For example, here we filter to popular doctors with more an average review of at least three stars:
.. code-block:: python
doctors = patients.group_by('doctor')
popular_doctors = doctors.having([
('patient_count', Count()),
('average_stars', Average('stars'))
], lambda t: t['patient_count'] > 100 and t['average_stars'] >= 3)
Chain commands together
=======================
SQL:
.. code-block:: postgres
SELECT state, total FROM table WHERE LOWER(state) = 'california' ORDER BY total DESC;
agate:
.. code-block:: python
new_table = table \
.select(['state', 'total']) \
.where(lambda row: row['state'].lower() == 'california') \
.order_by('total', reverse=True)
.. note::
Chaining commands in this way is sometimes not a good idea. Being explicit about each step can lead to clearer code.
Aggregate functions
===================
SQL:
.. code-block:: postgres
SELECT mean(age), median(age) FROM patients GROUP BY doctor;
agate:
.. code-block:: python
doctors = patients.group_by('doctor')
patient_ages = doctors.aggregate([
('patient_count', agate.Count()),
('age_mean', agate.Mean('age')),
('age_median', agate.Median('age'))
])
The resulting table will have four columns: ``doctor``, ``patient_count``, ``age_mean`` and ``age_median``.
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