This project involves fetching the latest cryptocurrency data from the CoinMarketCap API, processing the data using Pandas, and visualizing various aspects of the market using Matplotlib.

1. Installation
2. Usage
3. Features
4. License

To get started with this project, clone the repository and install the necessary dependencies.

git clone https://github.com/yourusername/crypto-market-analysis.git
cd crypto-market-analysis
pip install -r requirements.txt

1. Fetch Cryptocurrency Data: The script fetches the latest cryptocurrency listings from the CoinMarketCap API.

2. Process Data with Pandas: The data is then processed to create a DataFrame that contains information about various cryptocurrencies, including market capitalization, circulating supply, and price changes.

3. Visualize Data with Matplotlib: The processed data is visualized using bar charts to show the top cryptocurrencies by market capitalization, the most volatile cryptocurrencies, and more.

Here's a simple example of how to run the script:

import pandas as pd
import matplotlib.pyplot as plt
from requests import Session, Request
from requests.exceptions import ConnectionError, Timeout, TooManyRedirects
import json

# API URL and parameters
url = 'https://pro-api.coinmarketcap.com/v1/cryptocurrency/listings/latest'
parameters = {
    'start': '1',
    'limit': '5000',
    'convert': 'USD'
}
headers = {
    'Accepts': 'application/json',
    'X-CMC_PRO_API_KEY': 'your_api_key',
}

# Fetch data
session = Session()
session.headers.update(headers)

try:
    response = session.get(url, params=parameters)
    data = json.loads(response.text)
    current = pd.DataFrame(data['data'])
    print(current.head())
except (ConnectionError, Timeout, TooManyRedirects) as e:
    print(e)

# Load historical data
dec6 = pd.read_csv('datasets/coinmarketcap_06122017.csv')
market_cap_raw = dec6[['id', 'market_cap_usd']]

# Process data
market_cap_raw = market_cap_raw.query('market_cap_usd > 0')
market_cap_raw = market_cap_raw.assign(market_cap_perc=lambda x: (x.market_cap_usd / market_cap_raw.market_cap_usd.sum()) * 100)

# Plot data
cap10 = market_cap_raw.head(10).set_index('id')
cap10.plot.bar(y='market_cap_usd', logy=True, title='Top 10 Cryptocurrencies by Market Cap')
plt.show()

• Fetch Latest Cryptocurrency Data: Get up-to-date information on the top 5000 cryptocurrencies from the CoinMarketCap API.
• Process Data with Pandas: Clean and process the data to make it ready for analysis and visualization.
• Visualize Data with Matplotlib: Create various visualizations to better understand the cryptocurrency market, including:
  • Top 10 cryptocurrencies by market capitalization
  • Most volatile cryptocurrencies over 24 hours and 7 days
  • Distribution of cryptocurrencies based on market cap size

This project is licensed under the MIT License. See the LICENSE file for details.

Feel free to contribute to this project by creating issues or submitting pull requests on GitHub. For any questions or feedback, please contact [your email address].