Following dataframe consists of two empty columns (Sales and Income), that should be updated with scraped data corresponding to the ticker column:

df = pd.DataFrame({
    'No.': {0: '1', 1: '2', 2: '3', 3: '4', 4: '5'}, 
    'Ticker': {0: 'AAPL', 1: 'MSFT', 2: 'TSLA', 3: 'FB', 4: 'BRK-B'}, 
    'Sales': {0: '', 1: '', 2: '', 3: '', 4: ''}, 
    'Income': {0: '', 1: '', 2: '', 3: '', 4: ''}, 
    'Company': {0: 'Apple Inc.', 1: 'Microsoft Corporation', 2: 'Tesla, Inc.', 3: 'Meta Platforms, Inc.', 4: 'Berkshire Hathaway Inc.'}, 
    'Sector': {0: 'Technology', 1: 'Technology', 2: 'Consumer Cyclical', 3: 'Communication Services', 4: 'Financial'}
    })

With additional code below I am able to pull in the Sales and Income information from the snapshot table, but only for one ticker and I do not know how to update the dataframe:

Additional Code

    ticker = (df.iloc[0,1])
    #---------- Pulling the data from chosen stock ticker ----------#
    
    url = ('https://finviz.com/quote.ashx?t='   ticker.upper())
    
    req = requests.get(url,headers=headers)
    table = pd.read_html(req.text, attrs = {"class":"snapshot-table2"} )
    df = table[0]
    
    print(f'{df[0][3]}: {df[1][3]}: {df[0][2]}: {df[1][2]}')

Expected result:

CodePudding user response：

How to achieve?

Put your additional code into a def and use zip() in combination with map() to update your columns:

df['Sales'],df['Income'] = zip(*df['Ticker'].map(lambda x: get_ticker(x)))

Example

def get_ticker(ticker):
    #---------- Pulling the data from chosen stock ticker ----------#
    
    url = ('https://finviz.com/quote.ashx?t='   ticker.upper())
    
    req = requests.get(url,headers=headers)
    table = pd.read_html(req.text, attrs = {"class":"snapshot-table2"} )
    df = table[0]
    return(df[1][3],df[1][2])


df['Sales'],df['Income'] = zip(*df['Ticker'].map(lambda x: get_ticker(x)))

Output