the value will be converted in any case.
   </para>
  </sect2>

  <sect2 id="plpython-data-null">
   <title>Null, None</title>
  <para>
   If an SQL null value<indexterm><primary>null value</primary><secondary
   sortas="PL/Python">in PL/Python</secondary></indexterm> is passed to a
   function, the argument value will appear as <symbol>None</symbol> in
   Python. For example, the function definition of <function>pymax</function>
   shown in <xref linkend="plpython-funcs"/> will return the wrong answer for null
   inputs. We could add <literal>STRICT</literal> to the function definition
   to make <productname>PostgreSQL</productname> do something more reasonable:
   if a null value is passed, the function will not be called at all,
   but will just return a null result automatically. Alternatively,
   we could check for null inputs in the function body:

<programlisting>
CREATE FUNCTION pymax (a integer, b integer)
  RETURNS integer
AS $$
  if (a is None) or (b is None):
    return None
  if a &gt; b:
    return a
  return b
$$ LANGUAGE plpython3u;
</programlisting>

   As shown above, to return an SQL null value from a PL/Python
   function, return the value <symbol>None</symbol>. This can be done whether the
   function is strict or not.
  </para>
  </sect2>

  <sect2 id="plpython-arrays">
   <title>Arrays, Lists</title>
  <para>
   SQL array values are passed into PL/Python as a Python list.  To
   return an SQL array value out of a PL/Python function, return a
   Python list:

<programlisting>
CREATE FUNCTION return_arr()
  RETURNS int[]
AS $$
return [1, 2, 3, 4, 5]
$$ LANGUAGE plpython3u;

SELECT return_arr();
 return_arr
-------------
 {1,2,3,4,5}
(1 row)
</programlisting>

   Multidimensional arrays are passed into PL/Python as nested Python lists.
   A 2-dimensional array is a list of lists, for example. When returning
   a multi-dimensional SQL array out of a PL/Python function, the inner
   lists at each level must all be of the same size. For example:

<programlisting>
CREATE FUNCTION test_type_conversion_array_int4(x int4[]) RETURNS int4[] AS $$
plpy.info(x, type(x))
return x
$$ LANGUAGE plpython3u;

SELECT * FROM test_type_conversion_array_int4(ARRAY[[1,2,3],[4,5,6]]);
INFO:  ([[1, 2, 3], [4, 5, 6]], &lt;type 'list'&gt;)
 test_type_conversion_array_int4
---------------------------------
 {{1,2,3},{4,5,6}}
(1 row)
</programlisting>

   Other Python sequences, like tuples, are also accepted for
   backwards-compatibility with PostgreSQL versions 9.6 and below, when
   multi-dimensional arrays were not supported. However, they are always
   treated as one-dimensional arrays, because they are ambiguous with
   composite types. For the same reason, when a composite type is used in a
   multi-dimensional array, it must be represented by a tuple, rather than a
   list.
  </para>
  <para>
   Note that in Python, strings are sequences, which can have
   undesirable effects that might be familiar to Python programmers:

<programlisting>
CREATE FUNCTION return_str_arr()
  RETURNS varchar[]
AS $$
return "hello"
$$ LANGUAGE plpython3u;

SELECT return_str_arr();
 return_str_arr
----------------
 {h,e,l,l,o}
(1 row)
</programlisting>
  </para>
  </sect2>

  <sect2 id="plpython-data-composite-types">
   <title>Composite Types</title>
  <para>
   Composite-type arguments are passed to the function as Python mappings. The
   element names of the mapping are the attribute names of the composite type.
   If an attribute in the passed row has the null value, it has the value
   <symbol>None</symbol> in the mapping. Here is an example:

<programlisting>
CREATE TABLE employee (
  name text,
  salary integer,
  age integer
);

CREATE FUNCTION overpaid (e employee)
  RETURNS boolean
AS $$
  if e["salary"] &gt; 200000:
    return True
  if (e["age"] &lt; 30) and (e["salary"] &gt; 100000):
    return True
  return False
$$ LANGUAGE plpython3u;
</programlisting>
  </para>

  <para>
   There are multiple ways to return row or composite types