.. _topics-loaders:

============
Item Loaders
============

.. module:: scrapy.loader
   :synopsis: Item Loader class

Item Loaders provide a convenient mechanism for populating scraped :ref:`items
<topics-items>`. Even though items can be populated directly, Item Loaders provide a
much more convenient API for populating them from a scraping process, by automating
some common tasks like parsing the raw extracted data before assigning it.

In other words, :ref:`items <topics-items>` provide the *container* of
scraped data, while Item Loaders provide the mechanism for *populating* that
container.

Item Loaders are designed to provide a flexible, efficient and easy mechanism
for extending and overriding different field parsing rules, either by spider,
or by source format (HTML, XML, etc) without becoming a nightmare to maintain.

.. note:: Item Loaders are an extension of the itemloaders_ library that make it
    easier to work with Scrapy by adding support for
    :ref:`responses <topics-request-response>`.

Using Item Loaders to populate items
====================================

To use an Item Loader, you must first instantiate it. You can either
instantiate it with an :ref:`item object <topics-items>` or without one, in which
case an :ref:`item object <topics-items>` is automatically created in the
Item Loader ``__init__`` method using the :ref:`item <topics-items>` class
specified in the :attr:`ItemLoader.default_item_class` attribute.

Then, you start collecting values into the Item Loader, typically using
:ref:`Selectors <topics-selectors>`. You can add more than one value to
the same item field; the Item Loader will know how to "join" those values later
using a proper processing function.

.. note:: Collected data is internally stored as lists,
   allowing to add several values to the same field.
   If an ``item`` argument is passed when creating a loader,
   each of the item's values will be stored as-is if it's already
   an iterable, or wrapped with a list if it's a single value.

Here is a typical Item Loader usage in a :ref:`Spider <topics-spiders>`, using
the :ref:`Product item <topics-items-declaring>` declared in the :ref:`Items
chapter <topics-items>`:

.. skip: next
.. code-block:: python

    from scrapy.loader import ItemLoader
    from myproject.items import Product


    def parse(self, response):
        l = ItemLoader(item=Product(), response=response)
        l.add_xpath("name", '//div[@class="product_name"]')
        l.add_xpath("name", '//div[@class="product_title"]')
        l.add_xpath("price", '//p[@id="price"]')
        l.add_css("stock", "p#stock")
        l.add_value("last_updated", "today")  # you can also use literal values
        return l.load_item()

By quickly looking at that code, we can see the ``name`` field is being
extracted from two different XPath locations in the page:

1. ``//div[@class="product_name"]``
2. ``//div[@class="product_title"]``

In other words, data is being collected by extracting it from two XPath
locations, using the :meth:`~ItemLoader.add_xpath` method. This is the
data that will be assigned to the ``name`` field later.

Afterwards, similar calls are used for ``price`` and ``stock`` fields
(the latter using a CSS selector with the :meth:`~ItemLoader.add_css` method),
and finally the ``last_update`` field is populated directly with a literal value
(``today``) using a different method: :meth:`~ItemLoader.add_value`.

Finally, when all data is collected, the :meth:`ItemLoader.load_item` method is
called which actually returns the item populated with the data
previously extracted and collected with the :meth:`~ItemLoader.add_xpath`,
:meth:`~ItemLoader.add_css`, and :meth:`~ItemLoader.add_value` calls.


.. _topics-loaders-dataclass:

Working with dataclass items
============================

By default, :ref:`dataclass items <dataclass-items>` require all fields to be
passed when created. This could be an issue when using dataclass items with
item loaders: unless a pre-populated item is passed to the loader, fields
will be populated incrementally using the loader's :meth:`~ItemLoader.add_xpath`,
:meth:`~ItemLoader.add_css` and :meth:`~ItemLoader.add_value` methods.

One approach to overcome this is to define items using the
:func:`~dataclasses.field` function, with a ``default`` argument:

.. code-block:: python

    from dataclasses import dataclass, field
    from typing import Optional


    @dataclass
    class InventoryItem:
        name: Optional[str] = field(default=None)
        price: Optional[float] = field(default=None)
        stock: Optional[int] = field(default=None)


.. _topics-loaders-processors:

Input and Output processors
===========================

An Item Loader contains one input processor and one output processor for each
(item) field. The input processor processes the extracted data as soon as it's
received (through the :meth:`~ItemLoader.add_xpath`, :meth:`~ItemLoader.add_css` or
:meth:`~ItemLoader.add_value` methods) and the result of the input processor is
collected and kept inside the ItemLoader. After collecting all data, the
:meth:`ItemLoader.load_item` method is called to populate and get the populated
:ref:`item object <topics-items>`.  That's when the output processor is
called with the data previously collected (and processed using the input
processor). The result of the output processor is the final value that gets
assigned to the item.

Let's see an example to illustrate how the input and output processors are
called for a particular field (the same applies for any other field):

.. skip: next
.. code-block:: python

    l = ItemLoader(Product(), some_selector)
    l.add_xpath("name", xpath1)  # (1)
    l.add_xpath("name", xpath2)  # (2)
    l.add_css("name", css)  # (3)
    l.add_value("name", "test")  # (4)
    return l.load_item()  # (5)

So what happens is:

1. Data from ``xpath1`` is extracted, and passed through the *input processor* of
   the ``name`` field. The result of the input processor is collected and kept in
   the Item Loader (but not yet assigned to the item).

2. Data from ``xpath2`` is extracted, and passed through the same *input
   processor* used in (1). The result of the input processor is appended to the
   data collected in (1) (if any).

3. This case is similar to the previous ones, except that the data is extracted
   from the ``css`` CSS selector, and passed through the same *input
   processor* used in (1) and (2). The result of the input processor is appended to the
   data collected in (1) and (2) (if any).

4. This case is also similar to the previous ones, except that the value to be
   collected is assigned directly, instead of being extracted from a XPath
   expression or a CSS selector.
   However, the value is still passed through the input processors. In this
   case, since the value is not iterable it is converted to an iterable of a
   single element before passing it to the input processor, because input
   processor always receive iterables.

5. The data collected in steps (1), (2), (3) and (4) is passed through
   the *output processor* of the ``name`` field.
   The result of the output processor is the value assigned to the ``name``
   field in the item.

It's worth noticing that processors are just callable objects, which are called
with the data to be parsed, and return a parsed value. So you can use any
function as input or output processor. The only requirement is that they must
accept one (and only one) positional argument, which will be an iterable.

.. note:: Both input and output processors must receive an iterable as their
   first argument. The output of those functions can be anything. The result of
   input processors will be appended to an internal list (in the Loader)
   containing the collected values (for that field). The result of the output
   processors is the value that will be finally assigned to the item.

The other thing you need to keep in mind is that the values returned by input
processors are collected internally (in lists) and then passed to output
processors to populate the fields.

Last, but not least, itemloaders_ comes with some :ref:`commonly used
processors <itemloaders:built-in-processors>` built-in for convenience.


Declaring Item Loaders
======================

Item Loaders are declared using a class definition syntax. Here is an example:

.. code-block:: python

    from itemloaders.processors import TakeFirst, MapCompose, Join
    from scrapy.loader import ItemLoader


    class ProductLoader(ItemLoader):
        default_output_processor = TakeFirst()

        name_in = MapCompose(str.title)
        name_out = Join()

        price_in = MapCompose(str.strip)

        # ...

As you can see, input processors are declared using the ``_in`` suffix while
output processors are declared using the ``_out`` suffix. And you can also
declare a default input/output processors using the
:attr:`ItemLoader.default_input_processor` and
:attr:`ItemLoader.default_output_processor` attributes.

.. _topics-loaders-processors-declaring:

Declaring Input and Output Processors
=====================================

As seen in the previous section, input and output processors can be declared in
the Item Loader definition, and it's very common to declare input processors
this way. However, there is one more place where you can specify the input and
output processors to use: in the :ref:`Item Field <topics-items-fields>`
metadata. Here is an example:

.. code-block:: python

    import scrapy
    from itemloaders.processors import Join, MapCompose, TakeFirst
    from w3lib.html import remove_tags


    def filter_price(value):
        if value.isdigit():
            return value


    class Product(scrapy.Item):
        name = scrapy.Field(
            input_processor=MapCompose(remove_tags),
            output_processor=Join(),
        )
        price = scrapy.Field(
            input_processor=MapCompose(remove_tags, filter_price),
            output_processor=TakeFirst(),
        )


.. skip: start
.. code-block:: pycon

    >>> from scrapy.loader import ItemLoader
    >>> il = ItemLoader(item=Product())
    >>> il.add_value("name", ["Welcome to my", "<strong>website</strong>"])
    >>> il.add_value("price", ["&euro;", "<span>1000</span>"])
    >>> il.load_item()
    {'name': 'Welcome to my website', 'price': '1000'}

.. skip: end

The precedence order, for both input and output processors, is as follows:

1. Item Loader field-specific attributes: ``field_in`` and ``field_out`` (most
   precedence)
2. Field metadata (``input_processor`` and ``output_processor`` key)
3. Item Loader defaults: :meth:`ItemLoader.default_input_processor` and
   :meth:`ItemLoader.default_output_processor` (least precedence)

See also: :ref:`topics-loaders-extending`.

.. _topics-loaders-context:

Item Loader Context
===================

The Item Loader Context is a dict of arbitrary key/values which is shared among
all input and output processors in the Item Loader. It can be passed when
declaring, instantiating or using Item Loader. They are used to modify the
behaviour of the input/output processors.

For example, suppose you have a function ``parse_length`` which receives a text
value and extracts a length from it:

.. code-block:: python

    def parse_length(text, loader_context):
        unit = loader_context.get("unit", "m")
        # ... length parsing code goes here ...
        return parsed_length

By accepting a ``loader_context`` argument the function is explicitly telling
the Item Loader that it's able to receive an Item Loader context, so the Item
Loader passes the currently active context when calling it, and the processor
function (``parse_length`` in this case) can thus use them.

.. skip: start

There are several ways to modify Item Loader context values:

1. By modifying the currently active Item Loader context
   (:attr:`~ItemLoader.context` attribute):

   .. code-block:: python

      loader = ItemLoader(product)
      loader.context["unit"] = "cm"

2. On Item Loader instantiation (the keyword arguments of Item Loader
   ``__init__`` method are stored in the Item Loader context):

   .. code-block:: python

      loader = ItemLoader(product, unit="cm")

3. On Item Loader declaration, for those input/output processors that support
   instantiating them with an Item Loader context. :class:`~processor.MapCompose` is one of
   them:

   .. code-block:: python

       class ProductLoader(ItemLoader):
           length_out = MapCompose(parse_length, unit="cm")

.. skip: end


ItemLoader objects
==================

.. autoclass:: scrapy.loader.ItemLoader
    :members:
    :inherited-members:

.. _topics-loaders-nested:

Nested Loaders
==============

When parsing related values from a subsection of a document, it can be
useful to create nested loaders.  Imagine you're extracting details from
a footer of a page that looks something like:

Example::

    <footer>
        <a class="social" href="https://facebook.com/whatever">Like Us</a>
        <a class="social" href="https://twitter.com/whatever">Follow Us</a>
        <a class="email" href="mailto:whatever@example.com">Email Us</a>
    </footer>

Without nested loaders, you need to specify the full xpath (or css) for each value
that you wish to extract.

Example:

.. skip: next
.. code-block:: python

    loader = ItemLoader(item=Item())
    # load stuff not in the footer
    loader.add_xpath("social", '//footer/a[@class = "social"]/@href')
    loader.add_xpath("email", '//footer/a[@class = "email"]/@href')
    loader.load_item()

Instead, you can create a nested loader with the footer selector and add values
relative to the footer.  The functionality is the same but you avoid repeating
the footer selector.

Example:

.. skip: next
.. code-block:: python

    loader = ItemLoader(item=Item())
    # load stuff not in the footer
    footer_loader = loader.nested_xpath("//footer")
    footer_loader.add_xpath("social", 'a[@class = "social"]/@href')
    footer_loader.add_xpath("email", 'a[@class = "email"]/@href')
    # no need to call footer_loader.load_item()
    loader.load_item()

You can nest loaders arbitrarily and they work with either xpath or css selectors.
As a general guideline, use nested loaders when they make your code simpler but do
not go overboard with nesting or your parser can become difficult to read.

.. _topics-loaders-extending:

Reusing and extending Item Loaders
==================================

As your project grows bigger and acquires more and more spiders, maintenance
becomes a fundamental problem, especially when you have to deal with many
different parsing rules for each spider, having a lot of exceptions, but also
wanting to reuse the common processors.

Item Loaders are designed to ease the maintenance burden of parsing rules,
without losing flexibility and, at the same time, providing a convenient
mechanism for extending and overriding them. For this reason Item Loaders
support traditional Python class inheritance for dealing with differences of
specific spiders (or groups of spiders).

Suppose, for example, that some particular site encloses their product names in
three dashes (e.g. ``---Plasma TV---``) and you don't want to end up scraping
those dashes in the final product names.

Here's how you can remove those dashes by reusing and extending the default
Product Item Loader (``ProductLoader``):

.. skip: next
.. code-block:: python

    from itemloaders.processors import MapCompose
    from myproject.ItemLoaders import ProductLoader


    def strip_dashes(x):
        return x.strip("-")


    class SiteSpecificLoader(ProductLoader):
        name_in = MapCompose(strip_dashes, ProductLoader.name_in)

Another case where extending Item Loaders can be very helpful is when you have
multiple source formats, for example XML and HTML. In the XML version you may
want to remove ``CDATA`` occurrences. Here's an example of how to do it:

.. skip: next
.. code-block:: python

    from itemloaders.processors import MapCompose
    from myproject.ItemLoaders import ProductLoader
    from myproject.utils.xml import remove_cdata


    class XmlProductLoader(ProductLoader):
        name_in = MapCompose(remove_cdata, ProductLoader.name_in)

And that's how you typically extend input processors.

As for output processors, it is more common to declare them in the field metadata,
as they usually depend only on the field and not on each specific site parsing
rule (as input processors do). See also:
:ref:`topics-loaders-processors-declaring`.

There are many other possible ways to extend, inherit and override your Item
Loaders, and different Item Loaders hierarchies may fit better for different
projects. Scrapy only provides the mechanism; it doesn't impose any specific
organization of your Loaders collection - that's up to you and your project's
needs.

.. _itemloaders: https://itemloaders.readthedocs.io/en/latest/
.. _processors: https://itemloaders.readthedocs.io/en/latest/built-in-processors.html
