Fast, modern, pure python meta tags parser and snippet creator with full support of type annotations, py.typed in basic package and structured output. No jelly dicts, only typed structures!
If you want to see what exactly is social media snippets, look at the example:
- Python 3.8+
- Httpx
pip install meta-tags-parser
- Parse meta tags from source:
from meta_tags_parser import parse_meta_tags_from_source, structs desired_result: structs.TagsGroup = parse_meta_tags_from_source("""... html source ...""") # desired_result — is what you want
- Parse meta tags from url:
from meta_tags_parser import parse_tags_from_url, parse_tags_from_url_async, structs desired_result: structs.TagsGroup = parse_tags_from_url("https://xfenix.ru") # and async variant desired_result: structs.TagsGroup = await parse_tags_from_url_async("https://xfenix.ru") # desired_result — is what you want for both cases
- Parse social media snippet from source:
from meta_tags_parser import parse_snippets_from_source, structs snippet_obj: structs.SnippetGroup = parse_snippets_from_source("""... html source ...""") # snippet_obj — is what you want # access like snippet_obj.open_graph.title, ...
- Parse social media snippet from url:
from meta_tags_parser import parse_snippets_from_url, parse_snippets_from_url_async, structs snippet_obj: structs.SnippetGroup = parse_snippets_from_url("https://xfenix.ru") # and async variant snippet_obj: structs.SnippetGroup = await parse_snippets_from_url_async("https://xfenix.ru") # snippet_obj — is what you want # access like snippet_obj.open_graph.title, ...
Huge note: functions *_from_url written only for convenience and very error-prone, so any reconnections/error handling — completely on your side.
Also, I don't want to add some bloated requirements to achieve robust connections for any users, because they may simply not await any of this from the library. But if you really need this — write me.
Lets say you want extract snippet for twitter from html page:
from meta_tags_parser import parse_snippets_from_source, structs
my_result: structs.TagsGroup = parse_snippets_from_source("""
<meta property="og:card" content="summary_large_image">
<meta property="og:url" content="https://github.com/">
<meta property="og:title" content="Hello, my friend">
<meta property="og:description" content="Content here, yehehe">
<meta property="twitter:card" content="summary_large_image">
<meta property="twitter:url" content="https://github.com/">
<meta property="twitter:title" content="Hello, my friend">
<meta property="twitter:description" content="Content here, yehehe">
""")
print(my_result)
# What will be printed:
"""
SnippetGroup(
open_graph=SocialMediaSnippet(
title='Hello, my friend',
description='Content here, yehehe',
image='',
url='https://github.com/'
),
twitter=SocialMediaSnippet(
title='Hello, my friend',
description='Content here, yehehe',
image='',
url='https://github.com/'
)
)
"""
# You can access attributes as this
my_result.open_graph.title
my_result.twitter.image
# All fields are necessary and will be always available, even if they have not contain data
# So no need to worry about attributes exsitence (but you may need to check values)Main function is parse_meta_tags_from_source. It can be used like this:
from meta_tags_parser import parse_meta_tags_from_source, structs
my_result: structs.TagsGroup = parse_meta_tags_from_source("""... html source ...""")
print(my_result)
# What will be printed:
"""
structs.TagsGroup(
title="...",
twitter=[
structs.OneMetaTag(
name="title", value="Hello",
...
)
],
open_graph=[
structs.OneMetaTag(
name="title", value="Hello",
...
)
],
basic=[
structs.OneMetaTag(
name="title", value="Hello",
...
)
],
other=[
structs.OneMetaTag(
name="article:name", value="Hello",
...
)
]
)
"""As you can see from this example, we are not using any jelly dicts, only structured dataclasses. Lets see another example:
from meta_tags_parser import parse_meta_tags_from_source, structs
my_result: structs.TagsGroup = parse_meta_tags_from_source("""
<meta property="twitter:card" content="summary_large_image">
<meta property="twitter:url" content="https://github.com/">
<meta property="twitter:title" content="Hello, my friend">
<meta property="twitter:description" content="Content here, yehehe">
""")
print(my_result)
# What will be printed:
"""
TagsGroup(
title='',
basic=[],
open_graph=[],
twitter=[
OneMetaTag(name='card', value='summary_large_image'),
OneMetaTag(name='url', value='https://github.com/'),
OneMetaTag(name='title', value='Hello, my friend'),
OneMetaTag(name='description', value='Content here, yehehe')
],
other=[]
)
"""
for one_tag in my_result.twitter:
if one_tag.name == "title":
print(one_tag.value)
# What will be printed:
"""
Hello, my friend
"""You can specify what you want to parse:
from meta_tags_parser import parse_meta_tags_from_source, structs
result: structs.TagsGroup = parse_meta_tags_from_source("""... source ...""",
what_to_parse=(WhatToParse.TITLE, WhatToParse.BASIC, WhatToParse.OPEN_GRAPH, WhatToParse.TWITTER, WhatToParse.OTHER)
)If you reduce this tuple of parsing requirements it may increase overall parsing speed.
- Any name in meta tag (name or property attribute) will be lowercased
- I decided to strip
og:andtwitter:from original attributes, and let dataclass structures carry this information. If parser met meta tag with propertyog:name, it will be available inmy_resultvariable as one element of listmy_result.open_graph - Title of page (e.g.
<title>Something</title>) will be available as stringmy_result.title(of course, you recieveSomething) - «Standart» tags like title, description (check full list here ./meta_tags_parser/structs.py in constant
BASIC_META_TAGS) will be available as list inmy_result.basic - Other tags will be available as list in
my_result.otherattribute, name of tags will be preserved, unlikeog:/twitter:behaviour - If you want structured snippets, use
parse_snippets_from_sourcefunction
You can check https://github.com/xfenix/meta-tags-parser/releases/ release page.