getSent_AndPrice_twint.py

def sentimentAndPrice(ticker, start, end, numtweets=20):
    """
    Visually compares sentiment with the closing price of a given stock ticker.
    """
    # Creating a DF that contains daily tweets between two dates
    df = tweetByDay(start, end, pd.DataFrame(), search="$"+ticker, limit=numtweets)
        
    # Analyzing the sentiment of each tweet
    sent_df = getSentiment(
        df, 
        measurement='compound'
    )
    
    # Getting stock price history
    stock_df = getStockPrices(
        ticker, 
        start, 
        end
    )
    
    # Merging the two DF
    comb_df = sent_df.merge(stock_df, how='outer', sort=True)
    
    # Shifting the sentiment scores 1 day to compensate for lookahead bias
    comb_df['sentiment'] = comb_df['sentiment'].shift(1)
    
    # Filling the nans with zeroes
    comb_df = comb_df.fillna(0)
    
    # Setting the index
    comb_df.set_index('date', inplace=True)

    return comb_df