karpet
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im-n1
Library for fetching coin/token historical data, trends and more.
WARNING
Thanks to GitHub's hostile policy the repository has migrated to Gitlab <https://gitlab.com/imn1/karpet>_.
This repository won't be updated anymore.
.. image:: https://raw.githubusercontent.com/im-n1/karpet/master/assets/logo.png :align: center
.. image:: https://img.shields.io/pypi/v/karpet.svg?color=0c7dbe :alt: PyPI
.. image:: https://img.shields.io/pypi/l/karpet.svg?color=0c7dbe :alt: PyPI - License
.. image:: https://img.shields.io/pypi/dm/karpet.svg?color=0c7dbe :alt: PyPI - Downloads
.. contents::
Karpet
Karpet is a tiny library with just a few dependencies for fetching coins/tokens metrics data from the internet.
It can provide following data:
- coin/token historical price data (no limits)
- google trends for the given list of keywords (longer period than official API)
- twitter scraping for the given keywords (no limits)
- much more info about crypto coins/tokens (no rate limits)
What is upcoming?
- Reddit metrics
- Have a request? Open an issue ;)
Dependencies
Library uses a few nifty dependencies and is Python 3.6+ only. There is no need to install dependencies you don't need. Therefore this library utilizes extras which install optional dependencies:
- for Google trends - google
Usage
- Install the library via pip.
.. code-block:: bash
pip install karpet # Basics only pip install karpet[google] # With Google trends
- Import the library class first.
.. code-block:: python
from karpet import Karpet
fetch_crypto_historical_data()
Retrieves historical data.
.. code-block:: python
k = Karpet(date(2019, 1, 1), date(2019, 5, 1))
df = k.fetch_crypto_historical_data(id="ethereum") # Dataframe with historical data.
df.head()
price market_cap total_volume
2019-01-01 131.458725 1.36773e+10 1.36773e+10
2019-01-02 138.144802 1.43923e+10 1.43923e+10
2019-01-03 152.860453 1.59222e+10 1.59222e+10
2019-01-04 146.730599 1.52777e+10 1.52777e+10
2019-01-05 153.056567 1.59408e+10 1.59408e+10
fetch_crypto_exchanges()
~~~~~~~~~~~~~~~~~~~~~~~~
Retrieves exchange list.
.. code-block:: python
k = Karpet()
k.fetch_crypto_exchanges("nrg")
['DigiFinex', 'KuCoin', 'CryptoBridge', 'Bitbns', 'CoinExchange']
fetch_google_trends()
~~~~~~~~~~~~~~~~~~~~~
Retrieves Google Trends - in percents for the given date range.
.. code-block:: python
k = Karpet(date(2019, 1, 1), date(2019, 5, 1))
df = k.fetch_google_trends(kw_list=["bitcoin"]) # Dataframe with trends.
df.head()
.. image:: https://raw.githubusercontent.com/im-n1/karpet/master/assets/google_trends.png
And with a few lines of code you can get a chart
.. code-block:: python
df = df.set_index("date")
df.plot()
.. image:: https://raw.githubusercontent.com/im-n1/karpet/master/assets/trends_chart.png
fetch_news()
~~~~~~~~~~~~
Retrieves crypto news.
.. code-block:: python
k = Karpet()
news = k.fetch_news("btc") # Gets 10 news.
print(news[0])
{
'url': 'https://cointelegraph.com/ ....', # Truncated.
'title': 'Shell Invests in Blockchain-Based Energy Startup',
'description': 'The world’s fifth top oil and gas firm, Shell, has...', # Truncated.
'date': datetime.datetime(2019, 7, 28, 9, 24, tzinfo=datetime.timezone(datetime.timedelta(seconds=3600)))
'image': 'https://images.cointelegraph.com/....jpg' # Truncated.
}
news = k.fetch_news("btc", limit=30) # Gets 30 news.
fetch_top_news()
~~~~~~~~~~~~~~~~
Retrieves top crypto news in 2 categories:
* Editor's choices - articles picked by editors
* Hot stories - articles with most views
.. code-block:: python
k = Karpet()
editors_choices, top_stories = k.fetch_top_news()
print(len(editors_choices))
5
print(len(top_stories))
5
print(editors_choices[0])
{
'url': 'https://cointelegraph.com/...', # Truncated.
'title': 'Bank of China’s New Infographic Shows Why Bitcoin Price Is Going Up',
'date': datetime.datetime(2019, 7, 27, 10, 7, tzinfo=datetime.timezone(datetime.timedelta(seconds=3600))),
'image': 'https://images.cointelegraph.com/images/740_aHR...', # Truncated.
'description': 'The Chinese central bank released on its website an ...' # Truncated.
}
print(top_stories[0])
{
'url': 'https://cointelegraph.com/...', # Truncated.
'title': 'Bitcoin Price Shuns Volatility as Analysts Warn of Potential Drop to $7,000',
'date': datetime.datetime(2019, 7, 27, 10, 7, tzinfo=datetime.timezone(datetime.timedelta(seconds=3600))),
'image': 'https://images.cointelegraph.com/images/740_aHR0c...' # Truncated.
'description': 'Stability around $10,600 for Bitcoin price is ...' # Truncated.
}
get_coin_ids()
~~~~~~~~~~~~~~
Resolves coin ID's based on the given symbol (there are coins out there with identical symbol).
Use this to get distinctive coin ID which can be used as ``id`` param for
method ``fetch_crypto_historical_data()``.
.. code-block:: python
k = Karpet()
print(k.get_coin_ids("sta"))
['statera']
get_basic_data()
~~~~~~~~~~~~~~~~
Fetches coin/token basic data like:
``open_issues`` is only provided if ``total_issues`` and ``closed_issues`` are
available.
.. code-block:: python
k = Karpet()
print(k.get_basic_data(id="ethereum"))
{
'closed_issues': 5530,
'commit_count_4_weeks': 40,
'current_price': 3167.67,
'forks': 11635,
'market_cap': 371964284548,
'name': 'Ethereum',
'open_issues': 230,
'pull_request_contributors': 552,
'rank': 2,
'reddit_accounts_active_48h': 2881.0,
'reddit_average_comments_48h': 417.083,
'reddit_average_posts_48h': 417.083,
'reddit_subscribers': 1057875,
'stars': 31680,
'total_issues': 5760,
'year_high': 4182.790285752286,
'year_low': 321.0774351739628,
'yoy_change': 695.9225871929757, # growth/drop in percents
'price_change_24': 120.1,
'price_change_24_percents': 1.23
}
get_quick_search_data()
~~~~~~~~~~~~~~~~~~~~~~~
Lists all coins/tokes with some basic info.
.. code-block:: python
k = Karpet()
print(k.get_quick_search_data()[0])
{
"name": "Bitcoin",
"symbol": "BTC",
"rank": 1,
"slug": "bitcoin",
"tokens": [
"Bitcoin",
"bitcoin",
"BTC"
],
"id": 1,
}
fetch_crypto_live_data()
~~~~~~~~~~~~~~~~~~~~~~~~
Retrieves live market data.
.. code-block:: python
k = Karpet()
df = k.fetch_crypto_live_data(id="ethereum") # Dataframe with live data.
df.head()
open high low close
2023-01-16 20:00:00 1593.01 1595.05 1593.01 1594.28
2023-01-16 20:30:00 1593.37 1593.37 1589.03 1589.35
2023-01-16 21:00:00 1592.68 1593.66 1584.71 1587.87
2023-01-16 21:30:00 1587.28 1587.28 1583.13 1583.13
2023-01-16 22:00:00 1573.99 1580.11 1573.99 1579.97
Changelog
---------
[here](./CHANGELOG.md)
Credits
-------
This is my personal library I use in my long-term project. I can pretty much guarantee it will
live for a long time then. I will add new features over time and I more than welcome any
help or bug reports. Feel free to open an issue or merge request.
The code is is licensed under MIT license.
im-n1