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Description

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 ;)

Usage

  1. Install the library via pip.

pip install karpet

In case you don’t use uv you need to install the newerest cloudscraper manually

pip install git+https://github.com/VeNoMouS/cloudscraper.git@3.0.0

  1. Import the library class first.

from karpet import Karpet

fetch_crypto_historical_data()

Retrieves historical data.

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 (based on coingecko.com tickers, first 100 tickers only, so it covers the major exchanges). Accepts a symbol or a coingecko slug.

k = Karpet()
k.fetch_crypto_exchanges("nrg")
['LBank', 'MEXC', 'Indodax', 'CoinEx', 'Uniswap V4 (Ethereum)']

fetch_news()

Retrieves crypto news.

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

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().

k = Karpet()
print(k.get_coin_ids("sta"))
['statera']

get_basic_info()

Fetches coin/token basic data (price, market cap, rank and yearly stats). Accepts a symbol or a coingecko slug.

k = Karpet()
print(k.get_basic_info(slug="ethereum"))
{
    'current_price': 3167.67,
    'market_cap': 371964284548,
    'name': 'Ethereum',
    'rank': 2,
    '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.

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.

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

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.

Project links

Key dates

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  • Released:
Latest release

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for n1 from gravatar.com n1

Credits

Author: n1

Requires

Python >=3.13

Classifiers

Report project as malware

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