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مشاهده بیشترتوضیحات
Productivity and analysis tools for online marketing
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | advertools-0.9.1 |
| نام | advertools |
| نسخه کتابخانه | 0.9.1 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Elias Dabbas |
| ایمیل نویسنده | eliasdabbas@gmail.com |
| آدرس صفحه اصلی | https://github.com/eliasdabbas/advertools |
| آدرس اینترنتی | https://pypi.org/project/advertools/ |
| مجوز | MIT license |
.. image:: https://img.shields.io/pypi/v/advertools.svg
:target: https://pypi.python.org/pypi/advertools
.. image:: https://readthedocs.org/projects/advertools/badge/?version=latest
:target: https://advertools.readthedocs.io/en/latest/?badge=latest
:alt: Documentation Status
.. image:: http://pepy.tech/badge/advertools
:target: http://pepy.tech/project/advertools
.. image:: https://img.shields.io/lgtm/grade/python/g/eliasdabbas/advertools.svg
:target: https://lgtm.com/projects/g/eliasdabbas/advertools/context:python
.. image:: https://badges.gitter.im/advertools/community.svg
:target: https://bit.ly/3hEWZCe
| **Webinar:** `Creating SEM campaigns on a large scale - Wednesday April 20, 2022 <https://bit.ly/3KqAtuO>`_
|
| 🎉 **New:** ``crawl_headers`` Function for `crawling a known list of URLs with the HEAD method only <https://advertools.readthedocs.io/en/master/advertools.header_spider.html>`_
| 🎊 **New:** `SEO crawler <https://advertools.readthedocs.io/en/master/advertools.spider.html>`_
has new options for following links, include/exclude URL params and/or URL regex.
| 🎉 **New:** ``reverse_dns_lookup`` Function for `getting host information on a list of IP addresses <https://advertools.readthedocs.io/en/master/advertools.reverse_dns_lookup.html>`_
``advertools``: productivity & analysis tools to scale your online marketing
============================================================================
| A digital marketer is a data scientist.
| Your job is to manage, manipulate, visualize, communicate, understand,
and make decisions based on data.
You might be doing basic stuff, like copying and pasting text on spread
sheets, you might be running large scale automated platforms with
sophisticated algorithms, or somewhere in between. In any case your job
is all about working with data.
As a data scientist you don't spend most of your time producing cool
visualizations or finding great insights. The majority of your time is spent
wrangling with URLs, figuring out how to stitch together two tables, hoping
that the dates, won't break, without you knowing, or trying to generate the
next 124,538 keywords for an upcoming campaign, by the end of the week!
``advertools`` is a Python package that can hopefully make that part of your job a little easier.
Installation
------------
.. code:: bash
pip install advertools
# OR:
pip3 install advertools
SEM Campaigns
-------------
The most important thing to achieve in SEM is a proper mapping between the
three main elements of a search campaign
**Keywords** (the intention) -> **Ads** (your promise) -> **Landing Pages** (your delivery of the promise)
Once you have this done, you can focus on management and analysis. More importantly,
once you know that you can set this up in an easy way, you know you can focus
on more strategic issues. In practical terms you need two main tables to get started:
* Keywords: You can `generate keywords <https://advertools.readthedocs.io/en/master/advertools.kw_generate.html>`_ (note I didn't say research) with the
`kw_generate` function.
* Ads: There are two approaches that you can use:
* Bottom-up: You can create text ads for a large number of products by simple
replacement of product names, and providing a placeholder in case your text
is too long. Check out the `ad_create <https://advertools.readthedocs.io/en/master/advertools.ad_create.html>`_ function for more details.
* Top-down: Sometimes you have a long description text that you want to split
into headlines, descriptions and whatever slots you want to split them into.
`ad_from_string <https://advertools.readthedocs.io/en/master/advertools.ad_from_string.html>`_
helps you accomplish that.
* Tutorials and additional resources
* Get started with `Data Science for Digital Marketing and SEO/SEM <https://www.oncrawl.com/technical-seo/data-science-seo-digital-marketing-guide-beginners/>`_
* `Setting a full SEM campaign <https://www.datacamp.com/community/tutorials/sem-data-science>`_ for DataCamp's website tutorial
* Project to practice `generating SEM keywords with Python <https://www.datacamp.com/projects/400>`_ on DataCamp
* `Setting up SEM campaigns on a large scale <https://www.semrush.com/blog/setting-up-search-engine-marketing-campaigns-on-large-scale/>`_ tutorial on SEMrush
* Visual `tool to generate keywords <https://www.dashboardom.com/advertools>`_ online based on the `kw_generate` function
SEO
---
Probably the most comprehensive online marketing area that is both technical
(crawling, indexing, rendering, redirects, etc.) and non-technical (content
creation, link building, outreach, etc.). Here are some tools that can help
with your SEO
* `SEO crawler: <https://advertools.readthedocs.io/en/master/advertools.spider.html>`_
A generic SEO crawler that can be customized, built with Scrapy, & with several
features:
* Standard SEO elements extracted by default (title, header tags, body text,
status code, reponse and request headers, etc.)
* CSS and XPath selectors: You probably have more specific needs in mind, so
you can easily pass any selectors to be extracted in addition to the
standard elements being extracted
* Custom settings: full access to Scrapy's settings, allowing you to better
control the crawling behavior (set custom headers, user agent, stop spider
after x pages, seconds, megabytes, save crawl logs, run jobs at intervals
where you can stop and resume your crawls, which is ideal for large crawls
or for continuous monitoring, and many more options)
* Following links: option to only crawl a set of specified pages or to follow
and discover all pages through links
* `robots.txt downloader <https://advertools.readthedocs.io/en/master/advertools.sitemaps.html#advertools.sitemaps.robotstxt_to_df>`_
A simple downloader of robots.txt files in a DataFrame format, so you can
keep track of changes across crawls if any, and check the rules, sitemaps,
etc.
* `XML Sitemaps downloader / parser <https://advertools.readthedocs.io/en/master/advertools.sitemaps.html>`_
An essential part of any SEO analysis is to check XML sitemaps. This is a
simple function with which you can download one or more sitemaps (by
providing the URL for a robots.txt file, a sitemap file, or a sitemap index
* `SERP importer and parser for Google & YouTube <https://advertools.readthedocs.io/en/master/advertools.serp.html>`_
Connect to Google's API and get the search data you want. Multiple search
parameters supported, all in one function call, and all results returned in a
DataFrame
* Tutorials and additional resources
* A visual tool built with the ``serp_goog`` function to get `SERP rankings on Google <https://www.dashboardom.com/google-serp>`_
* A tutorial on `analyzing SERPs on a large scale with Python <https://www.semrush.com/blog/analyzing-search-engine-results-pages/>`_ on SEMrush
* `SERP datasets on Kaggle <https://www.kaggle.com/eliasdabbas/datasets?search=engine>`_ for practicing on different industries and use cases
* `SERP notebooks on Kaggle <https://www.kaggle.com/eliasdabbas/notebooks?sortBy=voteCount&group=everyone&pageSize=20&userId=484496&tagIds=1220>`_
some examples on how you might tackle such data
* `Content Analysis with XML Sitemaps and Python <https://www.semrush.com/blog/content-analysis-xml-sitemaps-python/>`_
* XML dataset examples: `news sites <https://www.kaggle.com/eliasdabbas/news-sitemaps>`_, `Turkish news sites <https://www.kaggle.com/eliasdabbas/turk-haber-sitelerinin-site-haritalari>`_,
`Bloomberg news <https://www.kaggle.com/eliasdabbas/bloomberg-business-articles-urls>`_
Text & Content Analysis (for SEO & Social Media)
------------------------------------------------
URLs, page titles, tweets, video descriptions, comments, hashtags are some
exmaples of the types of text we deal with. ``advertools`` provides a few
options for text analysis
* `Word frequency <https://advertools.readthedocs.io/en/master/advertools.word_frequency.html>`_
Counting words in a text list is one of the most basic and important tasks in
text mining. What is also important is counting those words by taking in
consideration their relative weights in the dataset. ``word_frequency`` does
just that.
* `URL Analysis <https://advertools.readthedocs.io/en/master/advertools.urlytics.html>`_
We all have to handle many thousands of URLs in reports, crawls, social media
extracts, XML sitemaps and so on. ``url_to_df`` converts your URLs into
easily readable DataFrames.
* `Emoji <https://advertools.readthedocs.io/en/master/advertools.emoji.html>`_
Produced with one click, extremely expressive, highly diverse (3k+ emoji),
and very popular, it's important to capture what people are trying to communicate
with emoji. Extracting emoji, get their names, groups, and sub-groups is
possible. The full emoji database is also available for convenience, as well
as an ``emoji_search`` function in case you want some ideas for your next
social media or any kind of communication
* `extract_ functions <https://advertools.readthedocs.io/en/master/advertools.extract.html>`_
The text that we deal with contains many elements and entities that have
their own special meaning and usage. There is a group of convenience
functions to help in extracting and getting basic statistics about structured
entities in text; emoji, hashtags, mentions, currency, numbers, URLs, questions
and more. You can also provide a special regex for your own needs.
* `Stopwords <https://advertools.readthedocs.io/en/master/advertools.stopwords.html>`_
A list of stopwords in forty different languages to help in text analysis.
* Tutorial on DataCamp for creating the ``word_frequency`` function and
explaining the importance of the difference between `absolute and weighted word frequency <https://www.datacamp.com/community/tutorials/absolute-weighted-word-frequency>`_
* `Text Analysis for Online Marketers <https://www.semrush.com/blog/text-analysis-for-online-marketers/>`_
An introductory article on SEMrush
Social Media
------------
In addition to the text analysis techniques provided, you can also connect to
the Twitter and YouTube data APIs. The main benefits of using ``advertools``
for this:
* Handles pagination and request limits: typically every API has a limited
number of results that it returns. You have to handle pagination when you
need more than the limit per request, which you typically do. This is handled
by default
* DataFrame results: APIs send you back data in a formats that need to be
parsed and cleaned so you can more easily start your analysis. This is also
handled automatically
* Multiple requests: in YouTube's case you might want to request data for the
same query across several countries, languages, channels, etc. You can
specify them all in one request and get the product of all the requests in
one response
* Tutorials and additional resources
* A visual tool to `check what is trending on Twitter <https://www.dashboardom.com/trending-twitter>`_ for all available locations
* A `Twitter data analysis dashboard <https://www.dashboardom.com/twitterdash>`_ with many options
* How to use the `Twitter data API with Python <https://www.kaggle.com/eliasdabbas/twitter-in-a-dataframe>`_
* `Extracting entities from social media posts <https://www.kaggle.com/eliasdabbas/extract-entities-from-social-media-posts>`_ tutorial on Kaggle
* `Analyzing 131k tweets <https://www.kaggle.com/eliasdabbas/extract-entities-from-social-media-posts>`_ by European Football clubs tutorial on Kaggle
* An overview of the `YouTube data API with Python <https://www.kaggle.com/eliasdabbas/youtube-data-api>`_
Conventions
-----------
Function names mostly start with the object you are working on, so you can use
autocomplete to discover other options:
| ``kw_``: for keywords-related functions
| ``ad_``: for ad-related functions
| ``url_``: URL tracking and generation
| ``extract_``: for extracting entities from social media posts (mentions, hashtags, emoji, etc.)
| ``emoji_``: emoji related functions and objects
| ``twitter``: a module for querying the Twitter API and getting results in a DataFrame
| ``youtube``: a module for querying the YouTube Data API and getting results in a DataFrame
| ``serp_``: get search engine results pages in a DataFrame, currently available: Google and YouTube
| ``crawl``: a function you will probably use a lot if you do SEO
| ``*_to_df``: a set of convenience functions for converting to DataFrames
(log files, XML sitemaps, robots.txt files, and lists of URLs)
=======================
Change Log - advertools
=======================
0.13.2 (2022-09-30)
-------------------
* Added
- Crawling recipe for how to use the ``DEFAULT_REQUEST_HEADERS`` to change
the default headers.
* Changed
- Split long lists of URL while crawling regardless of the ``follow_links``
parameter
* Fixed
- Clarify that while authenticating for Twitter only ``app_key`` and
``app_secret`` are required, with the option to provide ``oauth_token``
and ``oauth_token_secret`` if/when needed.
0.13.1 (2022-05-11)
-------------------
* Added
- Command line interface with most functions
- Make documentation interactive for most pages using ``thebe-sphinx``
* Changed
- Use `np.nan` wherever there are missing values in ``url_to_df``
* Fixed
- Don't remove double quotes from etags when downloading XML sitemaps
- Replace instances of ``pd.DataFrame.append`` with ``pd.concat``, which is
depracated.
- Replace empty values with np.nan for the size column in ``logs_to_df``
0.13.0 (2022-02-10)
-------------------
* Added
- New function ``crawl_headers``: A crawler that only makes `HEAD` requests
to a known list of URLs.
- New function ``reverse_dns_lookup``: A way to get host information for a
large list of IP addresses concurrently.
- New options for crawling: `exclude_url_params`, `include_url_params`,
`exclude_url_regex`, and `include_url_regex` for controlling which links to
follow while crawling.
* Fixed
- Any ``custom_settings`` options given to the ``crawl`` function that were
defined using a dictionary can now be set without issues. There was an
issue if those options were not strings.
* Changed
- The `skip_url_params` option was removed and replaced with the more
versatile ``exclude_url_params``, which accepts either ``True`` or a list
of URL parameters to exclude while following links.
0.12.3 (2021-11-27)
-------------------
* Fixed
- Crawler stops when provided with bad URLs in list mode.
0.12.0,1,2 (2021-11-27)
-----------------------
* Added
- New function ``logs_to_df``: Convert a log file of any non-JSON format
into a pandas DataFrame and save it to a `parquet` file. This also
compresses the file to a much smaller size.
- Crawler extracts all available ``img`` attributes: 'alt', 'crossorigin',
'height', 'ismap', 'loading', 'longdesc', 'referrerpolicy', 'sizes',
'src', 'srcset', 'usemap', and 'width' (excluding global HTML attributes
like ``style`` and ``draggable``).
- New parameter for the ``crawl`` function ``skip_url_params``: Defaults to
False, consistent with previous behavior, with the ability to not
follow/crawl links containing any URL parameters.
- New column for ``url_to_df`` "last_dir": Extract the value in the last
directory for each of the URLs.
* Changed
- Query parameter columns in ``url_to_df`` DataFrame are now sorted by how
full the columns are (the percentage of values that are not `NA`)
0.11.1 (2021-04-09)
-------------------
* Added
- The `nofollow` attribute for nav, header, and footer links.
* Fixed
- Timeout error while downloading robots.txt files.
- Make extracting nav, header, and footer links consistent with all links.
0.11.0 (2021-03-31)
-------------------
* Added
- New parameter `recursive` for ``sitemap_to_df`` to control whether or not
to get all sub sitemaps (default), or to only get the current
(sitemapindex) one.
- New columns for ``sitemap_to_df``: ``sitemap_size_mb``
(1 MB = 1,024x1,024 bytes), and ``sitemap_last_modified`` and ``etag``
(if available).
- Option to request multiple robots.txt files with ``robotstxt_to_df``.
- Option to save downloaded robots DataFrame(s) to a file with
``robotstxt_to_df`` using the new parameter ``output_file``.
- Two new columns for ``robotstxt_to_df``: ``robotstxt_last_modified`` and
``etag`` (if available).
- Raise `ValueError` in ``crawl`` if ``css_selectors`` or
``xpath_selectors`` contain any of the default crawl column headers
- New XPath code recipes for custom extraction.
- New function ``crawllogs_to_df`` which converts crawl logs to a DataFrame
provided they were saved while using the ``crawl`` function.
- New columns in ``crawl``: `viewport`, `charset`, all `h` headings
(whichever is available), nav, header and footer links and text, if
available.
- Crawl errors don't stop crawling anymore, and the error message is
included in the output file under a new `errors` and/or `jsonld_errors`
column(s).
- In case of having JSON-LD errors, errors are reported in their respective
column, and the remainder of the page is scraped.
* Changed
- Removed column prefix `resp_meta_` from columns containing it
- Redirect URLs and reasons are separated by '@@' for consistency with
other multiple-value columns
- Links extracted while crawling are not unique any more (all links are
extracted).
- Emoji data updated with v13.1.
- Heading tags are scraped even if they are empty, e.g. <h2></h2>.
- Default user agent for crawling is now advertools/VERSION.
* Fixed
- Handle sitemap index files that contain links to themselves, with an
error message included in the final DataFrame
- Error in robots.txt files caused by comments preceded by whitespace
- Zipped robots.txt files causing a parsing issue
- Crawl issues on some Linux systems when providing a long list of URLs
* Removed
- Columns from the ``crawl`` output: `url_redirected_to`, `links_fragment`
0.10.7 (2020-09-18)
-------------------
* Added
- New function ``knowledge_graph`` for querying Google's API
- Faster ``sitemap_to_df`` with threads
- New parameter `max_workers` for ``sitemap_to_df`` to determine how fast
it could go
- New parameter `capitalize_adgroups` for ``kw_generate`` to determine
whether or not to keep ad groups as is, or set them to title case (the
default)
* Fixed
- Remove restrictions on the number of URLs provided to ``crawl``,
assuming `follow_links` is set to `False` (list mode)
- JSON-LD issue breaking crawls when it's invalid (now skipped)
* Removed
- Deprecate the ``youtube.guide_categories_list`` (no longer supported by
the API)
0.10.6 (2020-06-30)
-------------------
* Added
- JSON-LD support in crawling. If available on a page, JSON-LD items will
have special columns, and multiple JSON-LD snippets will be numbered for
easy filtering
* Changed
- Stricter parsing for rel attributes, making sure they are in link
elements as well
- Date column names for ``robotstxt_to_df`` and ``sitemap_to_df`` unified
as "download_date"
- Numbering OG, Twitter, and JSON-LD where multiple elements are present in
the same page, follows a unified approach: no numbering for the first
element, and numbers start with "1" from the second element on. "element",
"element_1", "element_2" etc.
0.10.5 (2020-06-14)
-------------------
* Added
- New features for the ``crawl`` function:
* Extract canonical tags if available
* Extract alternate `href` and `hreflang` tags if available
* Open Graph data "og:title", "og:type", "og:image", etc.
* Twitter cards data "twitter:site", "twitter:title", etc.
* Fixed
- Minor fixes to ``robotstxt_to_df``:
* Allow whitespace in fields
* Allow case-insensitive fields
* Changed
- ``crawl`` now only supports `output_file` with the extension ".jl"
- ``word_frequency`` drops `wtd_freq` and `rel_value` columns if `num_list`
is not provided
0.10.4 (2020-06-07)
-------------------
* Added
- New function ``url_to_df``, splitting URLs into their components and to a
DataFrame
- Slight speed up for ``robotstxt_test``
0.10.3 (2020-06-03)
-------------------
* Added
- New function ``robotstxt_test``, testing URLs and whether they can be
fetched by certain user-agents
* Changed
- Documentation main page relayout, grouping of topics, & sidebar captions
- Various documentation clarifications and new tests
0.10.2 (2020-05-25)
-------------------
* Added
- User-Agent info to requests getting sitemaps and robotstxt files
- CSS/XPath selectors support for the crawl function
- Support for custom spider settings with a new parameter ``custom_settings``
* Fixed
- Update changed supported search operators and values for CSE
0.10.1 (2020-05-23)
-------------------
* Changed
- Links are better handled, and new output columns are available:
``links_url``, ``links_text``, ``links_fragment``, ``links_nofollow``
- ``body_text`` extraction is improved by containing <p>, <li>, and <span>
elements
0.10.0 (2020-05-21)
-------------------
* Added
- New function ``crawl`` for crawling and parsing websites
- New function ``robotstxt_to_df`` downloading robots.txt files into
DataFrames
0.9.1 (2020-05-19)
------------------
* Added
- Ability to specify robots.txt file for ``sitemap_to_df``
- Ability to retreive any kind of sitemap (news, video, or images)
- Errors column to the returnd DataFrame if any errors occur
- A new ``sitemap_downloaded`` column showing datetime of getting the
sitemap
* Fixed
- Logging issue causing ``sitemap_to_df`` to log the same action twice
- Issue preventing URLs not ending with xml or gz from being retreived
- Correct sitemap URL showing in the ``sitemap`` column
0.9.0 (2020-04-03)
------------------
* Added
- New function ``sitemap_to_df`` imports an XML sitemap into a
``DataFrame``
0.8.1 (2020-02-08)
------------------
* Changed
- Column `query_time` is now named `queryTime` in the `youtube` functions
- Handle json_normalize import from pandas based on pandas version
0.8.0 (2020-02-02)
------------------
* Added
- New module `youtube` connecting to all GET requests in API
- `extract_numbers` new function
- `emoji_search` new function
- `emoji_df` new variable containing all emoji as a DataFrame
* Changed
- Emoji database updated to v13.0
- `serp_goog` with expanded `pagemap` and metadata
* Fixed
- `serp_goog` errors, some parameters not appearing in result
df
- `extract_numbers` issue when providing dash as a separator
in the middle
0.7.3 (2019-04-17)
------------------
* Added
- New function `extract_exclamations` very similar to
`extract_questions`
- New function `extract_urls`, also counts top domains and
top TLDs
- New keys to `extract_emoji`; `top_emoji_categories`
& `top_emoji_sub_categories`
- Groups and sub-groups to `emoji db`
0.7.2 (2019-03-29)
------------------
* Changed
- Emoji regex updated
- Simpler extraction of Spanish `questions`
0.7.1 (2019-03-26)
------------------
* Fixed
- Missing __init__ imports.
0.7.0 (2019-03-26)
------------------
* Added
- New `extract_` functions:
* Generic `extract` used by all others, and takes
arbitrary regex to extract text.
* `extract_questions` to get question mark statistics, as
well as the text of questions asked.
* `extract_currency` shows text that has currency symbols in it, as
well as surrounding text.
* `extract_intense_words` gets statistics about, and extract words with
any character repeated three or more times, indicating an intense
feeling (+ve or -ve).
- New function `word_tokenize`:
* Used by `word_frequency` to get tokens of
1,2,3-word phrases (or more).
* Split a list of text into tokens of a specified number of words each.
- New stop-words from the ``spaCy`` package:
**current:** Arabic, Azerbaijani, Danish, Dutch, English, Finnish,
French, German, Greek, Hungarian, Italian, Kazakh, Nepali, Norwegian,
Portuguese, Romanian, Russian, Spanish, Swedish, Turkish.
**new:** Bengali, Catalan, Chinese, Croatian, Hebrew, Hindi, Indonesian,
Irish, Japanese, Persian, Polish, Sinhala, Tagalog, Tamil, Tatar, Telugu,
Thai, Ukrainian, Urdu, Vietnamese
* Changed
- `word_frequency` takes new parameters:
* `regex` defaults to words, but can be changed to anything '\S+'
to split words and keep punctuation for example.
* `sep` not longer used as an option, the above `regex` can
be used instead
* `num_list` now optional, and defaults to counts of 1 each if not
provided. Useful for counting `abs_freq` only if data not
available.
* `phrase_len` the number of words in each split token. Defaults
to 1 and can be set to 2 or higher. This helps in analyzing phrases
as opposed to words.
- Parameters supplied to `serp_goog` appear at the beginning
of the result df
- `serp_youtube` now contains `nextPageToken` to make
paginating requests easier
0.6.0 (2019-02-11)
------------------
* New function
- `extract_words` to extract an arbitrary set of words
* Minor updates
- `ad_from_string` slots argument reflects new text
ad lenghts
- `hashtag` regex improved
0.5.3 (2019-01-31)
------------------
* Fix minor bugs
- Handle Twitter search queries with 0 results in final request
0.5.2 (2018-12-01)
------------------
* Fix minor bugs
- Properly handle requests for >50 items (`serp_youtube`)
- Rewrite test for _dict_product
- Fix issue with string printing error msg
0.5.1 (2018-11-06)
------------------
* Fix minor bugs
- _dict_product implemented with lists
- Missing keys in some YouTube responses
0.5.0 (2018-11-04)
------------------
* New function `serp_youtube`
- Query YouTube API for videos, channels, or playlists
- Multiple queries (product of parameters) in one function call
- Reponse looping and merging handled, one DataFrame
* `serp_goog` return Google's original error messages
* twitter responses with entities, get the entities extracted, each in a
separate column
0.4.1 (2018-10-13)
------------------
* New function `serp_goog` (based on Google CSE)
- Query Google search and get the result in a DataFrame
- Make multiple queries / requests in one function call
- All responses merged in one DataFrame
* twitter.get_place_trends results are ranked by town and country
0.4.0 (2018-10-08)
------------------
* New Twitter module based on twython
- Wraps 20+ functions for getting Twitter API data
- Gets data in a pands DataFrame
- Handles looping over requests higher than the defaults
* Tested on Python 3.7
0.3.0 (2018-08-14)
------------------
* Search engine marketing cheat sheet.
* New set of extract\_ functions with summary stats for each:
* extract_hashtags
* extract_mentions
* extract_emoji
* Tests and bug fixes
0.2.0 (2018-07-06)
------------------
* New set of kw_<match-type> functions.
* Full testing and coverage.
0.1.0 (2018-07-02)
------------------
* First release on PyPI.
* Functions available:
- ad_create: create a text ad place words in placeholders
- ad_from_string: split a long string to shorter string that fit into
given slots
- kw_generate: generate keywords from lists of products and words
- url_utm_ga: generate a UTM-tagged URL for Google Analytics tracking
- word_frequency: measure the absolute and weighted frequency of words in
collection of documents
نیازمندی
| مقدار | نام |
|---|---|
| >=1.1.0 | pandas |
| >=0.4 | pyasn1 |
| >=2.5.0 | scrapy |
| >=3.8.0 | twython |
| >=5.0.0 | pyarrow |
نحوه نصب
نصب پکیج whl advertools-0.9.1:
pip install advertools-0.9.1.whl
نصب پکیج tar.gz advertools-0.9.1:
pip install advertools-0.9.1.tar.gz