#!/usr/bin/python3
'''
# Lab: Selector Golf

This is the second web-scraping lab. It drills the Beautiful Soup half of the
unit: turning the CSS selectors you learned in weeks 1-2 into a scraper that
pulls names and prices out of a listings page.

Fill in the body of every function below so that its doctests pass. Run the
tests from your terminal until the command falls silent:

    python3 -m doctest lab_selectors.py

Every function takes an HTML string and works on it through Beautiful Soup:

    soup = BeautifulSoup(html, 'html.parser')
    soup.select('some-css-selector')   # a list of every matching tag

`.select` takes the exact same selectors you used to *color* pages in the CSS
unit, and hands back a list of tags. From a tag, `tag.text` is the text between
its opening and closing tags, and `tag['href']` reads an attribute. That is the
whole toolkit, and it is the first half of Project 2 (scraping eBay into JSON).
'''
from bs4 import BeautifulSoup


def count_matches(html, selector):
    '''
    Return how many tags in `html` match the CSS `selector`.

    >>> count_matches('<div><p>a</p><p>b</p><span><p>c</p></span></div>', 'p')
    3
    >>> count_matches('<div><p>a</p><p>b</p><span><p>c</p></span></div>', 'div > p')
    2
    '''
    # TODO: make a soup and return len(soup.select(selector))


def select_texts(html, selector):
    '''
    Return the `.text` of every tag in `html` that matches `selector`.

    >>> select_texts('<span class="price">$9.99</span><span class="price">$19.99</span>', '.price')
    ['$9.99', '$19.99']
    >>> select_texts('<div><p>a</p><p>b</p></div>', 'p')
    ['a', 'b']
    '''
    # TODO: select the tags, then collect each tag's .text into a list


def select_attr(html, selector, attr):
    '''
    Return the value of `attr` on every tag in `html` matching `selector`.

    >>> select_attr('<a href="/a">One</a><a href="/b">Two</a>', 'a', 'href')
    ['/a', '/b']
    >>> select_attr('<img src="x.png"><img src="y.png">', 'img', 'src')
    ['x.png', 'y.png']
    '''
    # TODO: select the tags, then collect each tag[attr] into a list


def extract_titles(html):
    '''
    Return the text of every listing title (the `a.title` links).

    >>> extract_titles('<li class="item"><a class="title" href="/1">Mouse</a></li><li class="item"><a class="title" href="/2">Keyboard</a></li>')
    ['Mouse', 'Keyboard']
    '''
    # TODO: select '.title' and return each one's .text (strip() is a good habit)


def extract_prices_cents(html):
    '''
    Return every `.price` as a whole number of cents (an int, never a float).

    >>> extract_prices_cents('<span class="price">$49.99</span><span class="price">$5.00</span>')
    [4999, 500]
    >>> extract_prices_cents('<span class="price">$8.99</span>')
    [899]
    '''
    # TODO: for each '.price', strip the '$', convert dollars to an int of cents
    # Hint: int(round(float(dollars) * 100)) turns '49.99' into 4999


def count_free_shipping(html):
    '''
    Return how many listings carry a free-shipping badge (`.free-shipping`).

    >>> count_free_shipping('<li class="item"><span class="free-shipping">Free shipping</span></li><li class="item"></li>')
    1
    >>> count_free_shipping('<li class="item"></li><li class="item"></li>')
    0
    '''
    # TODO: count the '.free-shipping' tags


def first_link(html):
    '''
    Return the `href` of the first link in `html`, or None if there are none.

    >>> first_link('<div><a href="/first">One</a><a href="/second">Two</a></div>')
    '/first'
    >>> first_link('<p>no links here</p>')  # returns None, which prints nothing
    '''
    # TODO: select 'a[href]'; return the first one's href, or None if the list is empty


def extract_listings(html):
    '''
    Turn a listings page into a list of dicts, one per `.item`, each with the
    item's `name`, `price_cents`, and `url` -- the exact shape Project 2 saves
    as JSON.

    >>> html = '<li class="item"><a class="title" href="/itm/1">Mouse</a><span class="price">$9.99</span></li><li class="item"><a class="title" href="/itm/2">Keyboard</a><span class="price">$19.99</span></li>'
    >>> extract_listings(html)
    [{'name': 'Mouse', 'price_cents': 999, 'url': '/itm/1'}, {'name': 'Keyboard', 'price_cents': 1999, 'url': '/itm/2'}]
    '''
    # TODO: loop over each '.item'; inside each, find its '.title' and '.price'
    # and build a dict with keys 'name', 'price_cents', and 'url'


if __name__ == '__main__':
    import doctest
    doctest.testmod()