pandas_-project

import requests import pandas as pd from datetime import datetime

Step 1: Specify the CoinGecko API endpoint for historical prices

url = 'https://api.coingecko.com/api/v3/coins/bitcoin/market_chart'

Step 2: Make an HTTP request to the CoinGecko API with the specified parameters

params = { 'vs_currency': 'usd', 'days': '7', 'interval': 'daily',

Project Name

This project is a Python script that retrieves historical prices of Bitcoin from the CoinGecko API and performs data analysis using pandas.

Getting Started

To get started with this project, you'll need to have Python installed on your machine. You can download Python from the official website: https://www.python.org/downloads/

Installation

1. Clone the repository: git clone https://github.com/your-username/project.git
2. Navigate to the project directory: cd project
3. Install the required dependencies: pip install -r requirements.txt

Usage

1. Open the project.py file in your preferred code editor.
2. Run the script to retrieve Bitcoin historical prices and perform data analysis.
3. The script will generate two CSV files: bitcoin_prices_week.csv and weekly_statistics.csv.
4. You can find the generated CSV files in the project directory.

Contributing

Contributions are welcome! If you have any suggestions or improvements, feel free to open an issue or submit a pull request.

License

This project is licensed under the MIT License. response = requests.get(url, params=params) # Make an HTTP GET request data = response.json() # Convert the response to JSON format

Step 3: Extract relevant information from the API response

timestamps = [timestamp[0] // 1000 for timestamp in data['prices']] # Convert milliseconds to seconds prices = [price[1] for price in data['prices']] # Extract the price from each timestamp market_caps = [cap[1] for cap in data['market_caps']] # Extract the market cap from each timestamp total_volumes = [volume[1] for volume in data['total_volumes']] # Extract the total volume from each timestamp

Step 4: Create a Pandas DataFrame

Ensure all arrays have the same length

min_length = min(len(timestamps), len(prices), len(market_caps), len(total_volumes)) # Get the minimum length timestamps = timestamps[:min_length] prices = prices[:min_length] market_caps = market_caps[:min_length] total_volumes = total_volumes[:min_length]

bitcoin_prices_week = pd.DataFrame({ 'Timestamp': timestamps, 'Date': [datetime.utcfromtimestamp(timestamp).strftime('%Y-%m-%d') for timestamp in timestamps], 'Price (USD)': prices, 'Market Cap (USD)': market_caps, 'Total Volume (USD)': total_volumes, })

Convert 'Timestamp' column to datetime and set it as the index

bitcoin_prices_week['Timestamp'] = pd.to_datetime(bitcoin_prices_week['Timestamp'], unit='s') bitcoin_prices_week.set_index('Timestamp', inplace=True)

Step 5: Save the DataFrame to a CSV file

bitcoin_prices_week.to_csv('bitcoin_prices_week.csv', index=False)

print('CSV file created successfully.')

Step 6: Display the DataFrame

print(bitcoin_prices_week)

weekly_statistics = bitcoin_prices_week.resample('w').agg({'Price (USD)': ['min', 'max', 'mean'], 'Market Cap (USD)': 'mean', 'Total Volume (USD)': 'mean'})

Step 7: Save the DataFrame to a CSV file

weekly_statistics.to_csv('weekly_statistics.csv')

print('-------------------') print(weekly_statistics)