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Activity badge for triestpa/Cryptocurrency-Analysis-Python repository

Why triestpa/Cryptocurrency-Analysis-Python is active?

The result is based on ratio of number of commits and code additions from initial and final time ranges.

Initial time range – from 5 Jul, 2023 to 5 Oct, 2023

Final time range – from 5 Apr, 2024 to 5 Jul, 2024

Additions and deletions stats are not available for this repository due to GitHub API limitations.

Data calculated on 5 Jul, 2024

Bus factor

What is Bus factor?

It is basically a number of most active contributors responsible for 80% of contributions.

Bus factor tries to assess "What happens if a key member of the team is hit by a bus?". The more there are key members, the lower the risk.

The triestpa/Cryptocurrency-Analysis-Python repository has a bus factor of 1.

High risk, a lot of knowledge concentrated in a few people

Bus factor was measured on 14 Aug 2024

1

Summary of triestpa/Cryptocurrency-Analysis-Python

The triestpa/Cryptocurrency-Analysis-Python repository on GitHub is a Python-based project that uses data analysis tools to profile cryptocurrencies. It includes Jupyter notebooks that demonstrate how to pull data on cryptocurrencies from the coinmarketcap.com API.

The datasets pulled from the API consist of data such as the Market Capitalization of each coin, the Price per coin, the available Supply, and other metrics which it uses to analyze and profile the cryptocurrencies.

The repository also uses data visualization tools such as Pie Charts to represent the Market Capitalization of different coins, and Area Charts to represent the market cap by volume of different coins.

Dependencies for running this project include pandas, numpy, and plotly.

Please note, that this repository was last updated in 2018 and the Coinmarketcap's API that this project is using has been deprecated and might not work properly. It's advised to check the new API or use other services for the cryptocurrency data.

## Repository Info: - **Title**: Cryptocurrency Analysis Python - **Author**: Paul Trieste - **Language**: Python - **Dependencies**: Pandas, Plotly, Numpy - **Last Update**: 2018

Remember to always fit the cryptocurrency analysis techniques to the latest market conditions, as they are highly volatile and rapidly changing.

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