├── README.md
└── python-webscrape.py


/README.md:
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1 | # python-web-scraping
2 | Learning the web scrapping using python
3 | 


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/python-webscrape.py:
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 1 | import yfinance as yf
 2 | import datetime
 3 | import random
 4 | import math
 5 | import pandas as pd
 6 | from termcolor import colored
 7 | 
 8 | def get_stock_data(ticker):
 9 |     stock = yf.Ticker(ticker)
10 |     hist = stock.history(period="1mo")
11 |     return hist
12 | 
13 | def apply_lambda_functions(data):
14 |     # Lambda to calculate daily percentage change
15 |     percentage_change = data.apply(lambda row: ((row['Close'] - row['Open']) / row['Open']) * 100, axis=1)
16 | 
17 |     # Lambda to find day with the highest volume
18 |     highest_volume_day = data['Volume'].idxmax()
19 | 
20 |     return percentage_change, highest_volume_day
21 | 
22 | def math_operations(data):
23 |     mean_close = data['Close'].mean()
24 |     std_dev_close = data['Close'].std()
25 |     max_close = data['Close'].max()
26 |     min_close = data['Close'].min()
27 |     # Find the dates for max and min closing prices
28 |     date_max_close = data[data['Close'] == max_close].index[0]
29 |     date_min_close = data[data['Close'] == min_close].index[0]
30 | 
31 |     return mean_close, std_dev_close, max_close, min_close, date_max_close, date_min_close
32 | 
33 | def trading_days(data):
34 |     return len(data)
35 | 
36 | def latest_trading_date(data):
37 |     return data.index[-1]
38 | 
39 | def random_number_generator(data):
40 |     random_date = random.choice(data.index)
41 |     random_price = data.loc[random_date, 'Close']
42 |     return random_date, random_price
43 | 
44 | # Example Usage
45 | ticker = 'AAPL'
46 | stock_data = get_stock_data(ticker)
47 | 
48 | percentage_change, highest_volume_day = apply_lambda_functions(stock_data)
49 | mean_close, std_dev_close, max_close, min_close, date_max_close, date_min_close = math_operations(stock_data)
50 | trading_days_count = trading_days(stock_data)
51 | last_trading_date = latest_trading_date(stock_data)
52 | random_date, random_price = random_number_generator(stock_data)
53 | 
54 | 
55 | 
56 | 
57 | 
58 | print(colored(f"Stock Data for {ticker}:", 'blue'))
59 | print(stock_data.head())
60 | 
61 | print(colored("\nDaily Percentage Change:", 'green'))
62 | print(percentage_change.head())
63 | 
64 | print(colored(f"\nDay with Highest Volume: {highest_volume_day}", 'cyan'))
65 | print(colored(f"Mean Closing Price: {mean_close}", 'red'))
66 | print(colored(f"Standard Deviation of Closing Prices: {std_dev_close}", 'yellow'))
67 | print(colored(f"Maximum Closing Price: {max_close} on {date_max_close}", 'magenta'))
68 | print(colored(f"Minimum Closing Price: {min_close} on {date_min_close}", 'magenta'))
69 | print(colored(f"Number of Trading Days: {trading_days_count}", 'cyan'))
70 | print(colored(f"Most Recent Trading Date: {last_trading_date}", 'cyan'))
71 | print(colored(f"Random Date: {random_date}, Random Closing Price: {random_price}", 'green'))
72 | 
73 | 


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