import requests import pandas as pd import matplotlib.pyplot as plt API_KEY = 'DH1HKPF1RYUDJICP' top_10_companies = ['AAPL', 'MSFT', 'GOOGL', 'AMZN', 'BRK-B', 'JPM', 'JNJ', 'XOM', 'CVX', 'UNH'] def safe_float(value): try: return float(value) except (TypeError, ValueError): return 0.0 def get_company_data(symbol, api_key): url = f"https://www.alphavantage.co/query?function=OVERVIEW&symbol={symbol}&apikey={api_key}" response = requests.get(url) return response.json() data_list = [] for company in top_10_companies: data = get_company_data(company, API_KEY) data_list.append({ 'Symbol': company, 'MarketCapitalization': safe_float(data.get('MarketCapitalization')), 'QuarterlyEarningsGrowthYOY': safe_float(data.get('QuarterlyEarningsGrowthYOY')), 'QuarterlyRevenueGrowthYOY': safe_float(data.get('QuarterlyRevenueGrowthYOY')), 'EBITDA': safe_float(data.get('EBITDA')), 'PERatio': safe_float(data.get('PERatio')), 'EPS': safe_float(data.get('EPS')), 'RevenuePerShareTTM': safe_float(data.get('RevenuePerShareTTM')), 'ProfitMargin': safe_float(data.get('ProfitMargin')), 'OperatingMarginTTM': safe_float(data.get('OperatingMarginTTM')), 'ReturnOnAssetsTTM': safe_float(data.get('ReturnOnAssetsTTM')), 'ReturnOnEquityTTM': safe_float(data.get('ReturnOnEquityTTM')), 'RevenueTTM': safe_float(data.get('RevenueTTM')), 'GrossProfitTTM': safe_float(data.get('GrossProfitTTM')) }) df = pd.DataFrame(data_list) metrics = ['MarketCapitalization', 'QuarterlyEarningsGrowthYOY', 'QuarterlyRevenueGrowthYOY', 'EBITDA', 'PERatio', 'EPS', 'RevenuePerShareTTM', 'ProfitMargin', 'OperatingMarginTTM', 'ReturnOnAssetsTTM', 'ReturnOnEquityTTM', 'RevenueTTM', 'GrossProfitTTM'] means = df[metrics].mean() stds = df[metrics].std() plt.figure(figsize=(10,6)) plt.bar(df['Symbol'], df['MarketCapitalization'], color='blue') plt.title('Market Capitalization of Top 10 Companies') plt.xlabel('Company') plt.ylabel('Market Capitalization (in trillions)') plt.xticks(rotation=45) plt.show() plt.figure(figsize=(10,6)) plt.bar(df['Symbol'], df['ProfitMargin'], color='green') plt.title('Profit Margin of Top 10 Companies') plt.xlabel('Company') plt.ylabel('Profit Margin (%)') plt.xticks(rotation=45) plt.show() plt.figure(figsize=(10,6)) plt.bar(df['Symbol'], df['QuarterlyEarningsGrowthYOY'], color='orange') plt.title('Quarterly Earnings Growth YOY of Top 10 Companies') plt.xlabel('Company') plt.ylabel('Quarterly Earnings Growth YOY (%)') plt.xticks(rotation=45) plt.show() summary_df = pd.DataFrame({ 'Mean': means, 'Standard Deviation': stds }) print("\nSummary Statistics:") print(summary_df) summary_metrics = ['PERatio', 'ReturnOnEquityTTM'] for metric in summary_metrics: plt.figure(figsize=(10,6)) plt.bar(df['Symbol'], df[metric], color='purple') plt.title(f'{metric} of Top 10 Companies') plt.xlabel('Company') plt.ylabel(metric) plt.xticks(rotation=45) plt.show()