.. _topics-dynamic-content:

====================================
Selecting dynamically-loaded content
====================================

Some webpages show the desired data when you load them in a web browser.
However, when you download them using Scrapy, you cannot reach the desired data
using :ref:`selectors <topics-selectors>`.

When this happens, the recommended approach is to
:ref:`find the data source <topics-finding-data-source>` and extract the data
from it.

If you fail to do that, and you can nonetheless access the desired data through
the :ref:`DOM <topics-livedom>` from your web browser, see
:ref:`topics-headless-browsing`.

.. _topics-finding-data-source:

Finding the data source
=======================

To extract the desired data, you must first find its source location.

If the data is in a non-text-based format, such as an image or a PDF document,
use the :ref:`network tool <topics-network-tool>` of your web browser to find
the corresponding request, and :ref:`reproduce it
<topics-reproducing-requests>`.

If your web browser lets you select the desired data as text, the data may be
defined in embedded JavaScript code, or loaded from an external resource in a
text-based format.

In that case, you can use a tool like wgrep_ to find the URL of that resource.

If the data turns out to come from the original URL itself, you must
:ref:`inspect the source code of the webpage <topics-inspecting-source>` to
determine where the data is located.

If the data comes from a different URL, you will need to :ref:`reproduce the
corresponding request <topics-reproducing-requests>`.

.. _topics-inspecting-source:

Inspecting the source code of a webpage
=======================================

Sometimes you need to inspect the source code of a webpage (not the
:ref:`DOM <topics-livedom>`) to determine where some desired data is located.

Use Scrapy’s :command:`fetch` command to download the webpage contents as seen
by Scrapy::

    scrapy fetch --nolog https://example.com > response.html

If the desired data is in embedded JavaScript code within a ``<script/>``
element, see :ref:`topics-parsing-javascript`.

If you cannot find the desired data, first make sure it’s not just Scrapy:
download the webpage with an HTTP client like curl_ or wget_ and see if the
information can be found in the response they get.

If they get a response with the desired data, modify your Scrapy
:class:`~scrapy.Request` to match that of the other HTTP client. For
example, try using the same user-agent string (:setting:`USER_AGENT`) or the
same :attr:`~scrapy.Request.headers`.

If they also get a response without the desired data, you’ll need to take
steps to make your request more similar to that of the web browser. See
:ref:`topics-reproducing-requests`.

.. _topics-reproducing-requests:

Reproducing requests
====================

Sometimes we need to reproduce a request the way our web browser performs it.

Use the :ref:`network tool <topics-network-tool>` of your web browser to see
how your web browser performs the desired request, and try to reproduce that
request with Scrapy.

It might be enough to yield a :class:`~scrapy.Request` with the same HTTP
method and URL. However, you may also need to reproduce the body, headers and
form parameters (see :ref:`form`) of that request.

As all major browsers allow to export the requests in curl_ format, Scrapy
incorporates the method :meth:`~scrapy.Request.from_curl` to generate an equivalent
:class:`~scrapy.Request` from a cURL command. To get more information
visit :ref:`request from curl <requests-from-curl>` inside the network
tool section.

Once you get the expected response, you can :ref:`extract the desired data from
it <topics-handling-response-formats>`.

You can reproduce any request with Scrapy. However, some times reproducing all
necessary requests may not seem efficient in developer time. If that is your
case, and crawling speed is not a major concern for you, you can alternatively
consider :ref:`using a headless browser <topics-headless-browsing>`.

If you get the expected response `sometimes`, but not always, the issue is
probably not your request, but the target server. The target server might be
buggy, overloaded, or :ref:`banning <bans>` some of your requests.

Note that to translate a cURL command into a Scrapy request,
you may use `curl2scrapy <https://michael-shub.github.io/curl2scrapy/>`_.

.. _topics-handling-response-formats:

Handling different response formats
===================================

.. skip: start

Once you have a response with the desired data, how you extract the desired
data from it depends on the type of response:

-   If the response is HTML, XML or JSON, use :ref:`selectors
    <topics-selectors>` as usual.

-   If the response is JSON, use :func:`response.json()
    <scrapy.http.TextResponse.json>` to load the desired data:

    .. code-block:: python

        data = response.json()

    If the desired data is inside HTML or XML code embedded within JSON data,
    you can load that HTML or XML code into a
    :class:`~scrapy.Selector` and then
    :ref:`use it <topics-selectors>` as usual:

    .. code-block:: python

        selector = Selector(text=data["html"])

-   If the response is JavaScript, or HTML with a ``<script/>`` element
    containing the desired data, see :ref:`topics-parsing-javascript`.

-   If the response is CSS, use a :doc:`regular expression <library/re>` to
    extract the desired data from
    :attr:`response.text <scrapy.http.TextResponse.text>`.

.. _topics-parsing-images:

-   If the response is an image or another format based on images (e.g. PDF),
    read the response as bytes from
    :attr:`response.body <scrapy.http.Response.body>` and use an OCR
    solution to extract the desired data as text.

    For example, you can use pytesseract_. To read a table from a PDF,
    `tabula-py`_ may be a better choice.

-   If the response is SVG, or HTML with embedded SVG containing the desired
    data, you may be able to extract the desired data using
    :ref:`selectors <topics-selectors>`, since SVG is based on XML.

    Otherwise, you might need to convert the SVG code into a raster image, and
    :ref:`handle that raster image <topics-parsing-images>`.

.. skip: end

.. _topics-parsing-javascript:

Parsing JavaScript code
=======================

.. skip: start

If the desired data is hardcoded in JavaScript, you first need to get the
JavaScript code:

-   If the JavaScript code is in a JavaScript file, simply read
    :attr:`response.text <scrapy.http.TextResponse.text>`.

-   If the JavaScript code is within a ``<script/>`` element of an HTML page,
    use :ref:`selectors <topics-selectors>` to extract the text within that
    ``<script/>`` element.

Once you have a string with the JavaScript code, you can extract the desired
data from it:

-   You might be able to use a :doc:`regular expression <library/re>` to
    extract the desired data in JSON format, which you can then parse with
    :func:`json.loads`.

    For example, if the JavaScript code contains a separate line like
    ``var data = {"field": "value"};`` you can extract that data as follows:

    .. code-block:: pycon

        >>> pattern = r"\bvar\s+data\s*=\s*(\{.*?\})\s*;\s*\n"
        >>> json_data = response.css("script::text").re_first(pattern)
        >>> json.loads(json_data)
        {'field': 'value'}

-   chompjs_ provides an API to parse JavaScript objects into a :class:`dict`.

    For example, if the JavaScript code contains
    ``var data = {field: "value", secondField: "second value"};``
    you can extract that data as follows:

    .. code-block:: pycon

        >>> import chompjs
        >>> javascript = response.css("script::text").get()
        >>> data = chompjs.parse_js_object(javascript)
        >>> data
        {'field': 'value', 'secondField': 'second value'}

-   Otherwise, use js2xml_ to convert the JavaScript code into an XML document
    that you can parse using :ref:`selectors <topics-selectors>`.

    For example, if the JavaScript code contains
    ``var data = {field: "value"};`` you can extract that data as follows:

    .. code-block:: pycon

        >>> import js2xml
        >>> import lxml.etree
        >>> from parsel import Selector
        >>> javascript = response.css("script::text").get()
        >>> xml = lxml.etree.tostring(js2xml.parse(javascript), encoding="unicode")
        >>> selector = Selector(text=xml)
        >>> selector.css('var[name="data"]').get()
        '<var name="data"><object><property name="field"><string>value</string></property></object></var>'

.. skip: end

.. _topics-headless-browsing:

Using a headless browser
========================

On webpages that fetch data from additional requests, reproducing those
requests that contain the desired data is the preferred approach. The effort is
often worth the result: structured, complete data with minimum parsing time and
network transfer.

However, sometimes it can be really hard to reproduce certain requests. Or you
may need something that no request can give you, such as a screenshot of a
webpage as seen in a web browser. In this case using a `headless browser`_ will
help.

A headless browser is a special web browser that provides an API for
automation. By installing the :ref:`asyncio reactor <install-asyncio>`,
it is possible to integrate ``asyncio``-based libraries which handle headless browsers.

One such library is `playwright-python`_ (an official Python port of `playwright`_).
The following is a simple snippet to illustrate its usage within a Scrapy spider:

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

    import scrapy
    from playwright.async_api import async_playwright


    class PlaywrightSpider(scrapy.Spider):
        name = "playwright"
        start_urls = ["data:,"]  # avoid using the default Scrapy downloader

        async def parse(self, response):
            async with async_playwright() as pw:
                browser = await pw.chromium.launch()
                page = await browser.new_page()
                await page.goto("https://example.org")
                title = await page.title()
                return {"title": title}


However, using `playwright-python`_ directly as in the above example
circumvents most of the Scrapy components (middlewares, dupefilter, etc).
We recommend using `scrapy-playwright`_ for a better integration.

.. _CSS: https://en.wikipedia.org/wiki/Cascading_Style_Sheets
.. _chompjs: https://github.com/Nykakin/chompjs
.. _curl: https://curl.se/
.. _headless browser: https://en.wikipedia.org/wiki/Headless_browser
.. _js2xml: https://github.com/scrapinghub/js2xml
.. _playwright-python: https://github.com/microsoft/playwright-python
.. _playwright: https://github.com/microsoft/playwright
.. _pytesseract: https://github.com/madmaze/pytesseract
.. _scrapy-playwright: https://github.com/scrapy-plugins/scrapy-playwright
.. _tabula-py: https://github.com/chezou/tabula-py
.. _wget: https://www.gnu.org/software/wget/
.. _wgrep: https://github.com/stav/wgrep
