Python Machine Learning Blueprints - Second Edition

Adding features to influence the performance of an IPO

One measure of demand that could be informative is the opening gap. This is the difference between the offer price and the opening price of the issue. Let's add that to our DataFrame:

df['Opening Gap % Chg'] = (df['Opening Price'] - df['Offer Price'])/df['Offer Price'] 

Next, let's get a count of the number of underwriters on the offering. Perhaps having more banks involved leads to better marketing of the issue? This is demonstrated in the following code block:

def get_mgr_count(x): 
    return len(x.split('/')) 
 
df['Mgr Count'] = df['Managers'].apply(get_mgr_count) 

Let's quickly see whether there might be anything to this hypothesis by means of a visualization:

df.groupby('Mgr Count')['1st ...