karpet 0.2.4.1

Library for fetching coin/token metrics data from the internet.

Contents

• Karpet

  • Dependencies

  • Usage

    • get_coin_slug()

    • fetch_historical_data()

    • fetch_exchanges()

    • fetch_tweets()

    • fetch_google_trends()

    • fetch_news()

  • Changelog

    • 0.2.4

    • 0.2.3

    • 0.2.2

    • 0.2.1

    • 0.2

    • 0.1

  • Credits

Karpet

Karpet is a tiny library with just a few dependencies for fetching coins/tokens metrics data 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

• for Twitter scraping - twitter

Usage

1. Install the library via pip.

pip install karpet  # Basics only
pip install karpet[twitter]  # For Twitter scraping
pip install karpet[google]  # For Google trends
pip install karpet[twitter,google]  # All features

2. Import the library class first.

from karpet import Karpet

get_coin_slug()

Symbol (ticker) -> coninmarketcap.com URL slug conversion.

c = Karpet()
c.get_coin_slug("BTC")  # bitcoin

fetch_historical_data()

Retrieves historical data.

c = Karpet(date(2019, 1, 1), date(2019, 5, 1))
df = c.fetch_crypto_historical_data(coin="bitcoin")  # Dataframe with historical data
df.head()

fetch_exchanges()

Retrieves exchange list.

c = Karpet()
c.fetch_exchanges("nrg")
['DigiFinex', 'KuCoin', 'CryptoBridge', 'Bitbns', 'CoinExchange']

fetch_tweets()

Retrieves twitter tweets.

c = Karpet(date(2019, 1, 1), date(2019, 5, 1))
df = c.fetch_tweets(kw_list=["bitcoin"], lang="en")  # Dataframe with tweets.
df.head()

fetch_google_trends()

Retrieves Google Trends - in percents for the given date range.

c = Karpet(date(2019, 1, 1), date(2019, 5, 1))
df = c.fetch_google_trends(kw_list=["bitcoin"])  # Dataframe with trends.
df.head()

And with a few lines of code you can get a chart

df = df.set_index("date")
df.plot()

fetch_news()

Retrieves crypto news.

c = Karpet()
news = c.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, 10, 19, 0, 13),
   'image': 'https://images.cointelegraph.com/....jpg'  # Truncated.
}
news = c.fetch_news("btc", limit=30)  # Gets 30 news.

Changelog

0.2.4

• fetch_news() adds new “description” item and renames “image_url” to “image”.

• All fetch_news() item properties are now presented even if they are None.

0.2.3

• Simplified import from from karpet.karpet import Karpet to from karpet import Karpet.

0.2.2

• Added fetch_news() method for retrieving crypto news.

0.2.1

• Added fetch_exchanges() method for retrieving symbol exchange list.

• Removed obsolete library dependency.

0.2

• Twitter scraping added.

0.1

• Initial release.

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.